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EDITORIAL

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Folded Distance: Towards a Techno-ontology of Distributed Aesthetics

Megan Phipps

Abstract

This article proposes "folded distance" as a critical conceptual framework to theorize techno-ontological aesthetics in the context of computational media and digital culture. In contrast to representational approaches, it introduces the notion of techno-ontology—a mode of analysis that foregrounds the operational, recursive, and affective infrastructures of networked life. Through close examination of VJ Peter Rubin’s live-mixing practices and the immersive architectures of techno-events such as Berlin’s Mayday and Chromapark, the article elucidates how media systems enact distributed sensation, rhythmic entrainment, and modulated proximity. Folding, in this context, is theorized as both spatial and affective topology through which subjectivity, perception, and relation are reconfigured. The recursive logics of technical media are shown to generate aesthetic conditions where distance is infrastructurally mediated rather than spatially determined. This study contributes to debates in media theory by articulating a techno-aesthetic ontology of sensation—one that interrogates how recursive systems shape the lived realities of digital and post-digital culture.

Introduction

Moving through the layered proximities and stratified intimacies of contemporary digital aesthetics, this article traces the recursive folds that pulse across rave floors, signal pathways that map media infrastructures, and the ambient architectures of networked life. The concept of folded distance is proposed as a technologically generated spatial and biologically affective condition, in which proximity and mediation converge through distributed emergence. Folded distance is a virtual architecture of being-together apart, a modulation of sensation or presence tuned through electromagnetism, circuitry, and loops of light, sound, and code. Distributed intimacies is proposed as an additional concept, intended to further this framework, describing how relationality-as-intimacy is now routed through latency, signal, and affective entrainment. These entanglements operate within techno-ontological affect—a field where media form(at)s transcend isolated interaction, manifesting in collective phenomena intrinsic to Techno/Tekno raves, network ecologies, electronic pictoriality, platforms, CGI, AI, and beyond. Consequently, cultivating decentralized, immersive experiences, that gesture toward a mythos of potential that promises to enable processes of de- and re-territorialization across social, political, cultural, and personal landscapes.

This exploration aligns with the broader evolution of digital culture, where emergent media formats and the affective architectures of mass communication continually reconstruct the collective fabric of experience and perception, which is driven by the flux of technological mediums. Recursive techno-aesthetics unfold and reassemble within the signals of these affective architectures as operational milieus—feedback-driven ecologies that are autopoietic in structure, generating and regulating affective climates. They encode coherent vibing intensities as well as their erosion into perceptual saturation and attenuated attunement. Within such architecture, network anesthesia (Munster) hovers as an affective condition. Once indebted to the clinical origins of anesthesia in industrial-era surgery, where the numbing of factory-worn bodies paralleled the rise of mechanized perception (Buck-Morss), aesthetic affect undergoes modern reconfiguration. In its digital iteration, this anesthesia no longer silences surgical pain but disperses as overexposure to signal and image that dulls critical response, replacing intensity with ambient haze, disorientation with seamless flow. What arises is a recursive sensorium, layered assemblages of perception and sensation that elude binary framings of the body as either individual or collective, biological or machinic. Folded distance thus offers a conceptual incision into the architectures of mediation, where the body becomes inflected, interfacial, and ambient. In tracing these dynamics through the experimental visual rhythms of VJ Peter Rubin, and the immersive architectures of Techno events like Mayday and Chromapark, this article articulates a conceptual techno-ontological terrain governed by recursive techno-aesthetics, folded distance, and distributed intimacies. Inhabiting this recursive field, we are compelled to ask: how might we locate and/or recompose the rhythms of our technological present?

Rhythm & Architecture of Techno-ontology

Techno-events do not unfold in neutral space, they inhabit and reconfigure historically saturated architectures. The power of techno events lies in transforming “techno spaces” into transregional, transnational, and transitional aesthetic sites. In 2024, German UNESCO paradoxically acknowledged this elusive presence by inscribing Berlin's techno culture into the National Inventory of Intangible Cultural Heritage, describing it as “electronic sounds strung together in a rhythmically monotonous structure” (German Commission for UNESCO). A formative example is Tekknozid (see Figure 1), a pioneering rave series held in Berlin between 1989 and 1992, widely credited as the first German rave initiative and thus a crucial influence on the evolution of the city’s techno/Techno scene. Despite its intangible signifier, Techno culture is inextricable from the subcultural reclamation of collective identity and spatial agency. Events like Teknivals embody this ethos, occupying contested or abandoned sites and turning them into virtual, liminal zones of perceived connection and collective resistance. Ephemeral interventions intended to critique state control over land, culture, and access to public space. Similarly, Germany’s Mayday festival, launched in 1991, emerged as a protest against the threatened closure of East Germany’s youth radio station DT64.

<INSERT FIGURE 1> Figure 1: Tekknozoid Flyer from the year 1991. The Peter Rubin Collection: Amsterdam. Courtesy of Eye Filmmuseum.

American-born, Amsterdam-based experimental filmmaker and video artist Peter Rubin played a formative role in shaping the architectural aesthetics in Germany’s technoculture, such as in Mayday’s. Beyond his organizational support, Rubin crafted the festival’s sensory architecture through fusing visuals, movement, and rhythm into a singular techno-aesthetic. His first engagement in Germany came in 1988, when he brought his VJ practice from the Netherlands to Berlin’s Tempodrom, followed soon after by a performance at Hamburg’s Grosse Freiheit. In 1994, three years after Mayday’s debut, Rubin returned to Berlin for Chromapark—the first exhibition devoted entirely to techno art and culture. Held at E-Werk under the theme ‘House of Techno’, Chromapark reimagined Berlin as a speculative media ecology. From U-Bahn interventions by Lila Lutz, Christoph Husemann, and others, to a continuous 96-hour rave-exhibition featuring over 40 artists, Chromapark collapsed the distinctions between art gallery, discotheque, and media lab. The post-reunification void—social, political, and architectural—offered fertile ground for reappropriation. Vacant East German factories, bunkers, and power stations became vessels for social and sonic inhabitation. Venues like E-Werk, Tresor, and Berghain (formerly Ostgut) emerged not merely as nightlife spaces, but as affective techno-architectures where historical detritus fused with a hope in ecstatic cyber futurity.

Berlin’s techno-aesthetic politics drew power from the city’s raw materiality—concrete, steel, rust—and infused it with loops of light and sound. Chromapark’s E-Werk, a site housed in a former electricity plant in Mitte and active in the early 1990s, straddled a moment of techno-ontological transition in Berlin’s techno imaginary. Its architecture was marked by clean industrial lines, open multi-level layouts, and cathedral-like vertical volumes. It honed a spatial grammar that activated both the machinic past of energy production and the spiritually speculative future of digital collectivity. Unlike the total occlusion of subterranean compression in Tresor, or the sensorial vortex of verticality in Berghain, E-Werk cultivated a “modulated openness” as a surface of spatial recursion across levels, balconies, and stairwells. As dancers moved vertically and horizontally through its grid, they were caught in rhythmic feedback loops—not just of music, but of collective movement, expanded architecture, as well as virtual and visceral affect. As Brian Massumi notes, “a building is a technology of movement... in direct membranic connection with virtual event spaces” (204). Following Massumi, techno-architectures and techno-events thus engender virtual techno-aesthetic matrices: environments that produce and are produced by the movement of recursive flows of affect, rhythm, and embodied presence. Like the dancing attendee, the crowd itself becomes a techno-social body that is tuned to pulses, flickers, and spatial thresholds.

In the context of techno-ontology, the virtual folded architecture of the networked rave is a rhizomatic cartography that re-maps intimacy and distance. It is a dance of spatial and rhythmic dynamics oscillating between proximity and separation, individuality and collectivity, orientation and disorientation. The Techno rave’s foggy dancefloor, saturated with recursive images, beats, and stroboscopic flickers, embodies rhythmic space (Lefebvre): a space not statically given but produced through the interplay of bodily, social, and environmental rhythms, and as lived, affective zones continually reconfigured through repetition, difference, and syncopation. Within these environments, sensorial distance is not erased but folded as it is stretched and reframed through industrial-mechanical recursive cycles of decentralized sound, light, and (collective) movement. Recursive oscillations become both structuring force and site of disjunction, a deterritorialized zone of molecular motion, composing a techno-affective matrix where identity, agency, and perception are rendered and recalibrated. Folded distance becomes the operative condition of the techno-affective matrix, emerging as distributed presence that is modulated across signal paths and recursive perceptual architectures, resisting binary models of the body (e.g., individual or collective, organic or machinic). In this techno-aesthetic milieu, rhythmic folded distance is both perceptual and architectural: a topology where the expanded screens and techno tones becomes a sensory membrane, and the network becomes an ambient field of a new anaesthetic affect (Munster).  Presence is no longer singular, immediate, or bound to the intensity of individual consciousness, but rather becomes recursive, patterned, and machinically modulated across environments and biological systems, expanding any pre-conceived notions on the boundaries of mediated proximity.

In the techno-event or rave, distance becomes felt in the shared pulse of bodies and mediated through the technological interfaces of sound systems, visual projections, and expanded visuals (Reynolds; St. John; Butler; Garcia; Thornton; Gaillot; Holl). This interplay enacts feedback that is recursive, where proximity becomes reconstituted through rhythmic entrainment and distance is reimagined as resonant intervals within the latency between beats, flickers, gestures, and re-mixed visual media. Within the techno-aesthetic paradigm of the mediated sense acts (Fedorova), this perspective highlights a critical ontological tension requiring a rethinking of the Real and aesthetics in media theory (Fazi). Particularly, it addresses the conceptualization of images as they reconcile the fluid, continuous nature of sensory experience with the discrete, fragmented digital "bits" traversing hyper-networked virtual architectures. In the context of new media assemblages and the advent of discorrelated images (Denson), the blurred flatline between real/virtual, simulated/physical, and synthetic/non-synthetic images emerges as a liminal zone that requires historical and theoretical tracing. Namely, what modes of techno-aesthetic operations govern the movement constituting the hyper-networked audiovisual images, dispersed across aggregates in flux? What machinic logics, what rhythmic recursions, drive the movement of hyper-networked images? How do they circulate, fragment, and recombine across architectures of flux? And, what exactly are techno-aesthetics in relation to operationality versus experimentation, and as constituting folded distance(s)?

Before addressing questions concerning techno-aesthetics as operational systems or experimental ruptures, recursion must be situated as the coupling of automation and emergence. Yuk Hui frames recursion as a spiral of individuation, where each loop expresses singularity and self-determination (Hui). Within this framing, a critical distinction arises between technological implementation and artistic experimentation. Though both operate within techno-aesthetic domains, their trajectories diverge both functionally and ontologically. Artistic practices, such as Rubin’s live-mix visuals or Techno rave’s rhythmic architecture, generate perceptual uncertainty that craft affective spaces intended for the unsettling of normative embodiment. Technological implementations, by contrast, operationalize, automate, and codify these affective dimensions into predictable loops of capture, habituation, and optimization. Thus, if experimentation opens zones of affective potential, implementation folds them into circuits of profiling, feedback optimization, and predictive modulation. The tension between disorientation and modulation, rupture and recursion, marks the contested terrain of folded distance. Recursion becomes no longer a generative fold but a flattened logic of modulation and control. Folded distance becomes the battleground between technics as protocol and technics as potential.

Techno-Aesthetic Operations of The Fold

Techno-aesthetics mark the threshold where technical apparatuses acquire aesthetic force through human gesture, movement, and perception (Simondon, "On the Mode of Existence of Technical Objects "). Techno-aesthetic operations form a milieu in which subject and system no longer stand opposed, but co-emerge through loops of feedback, ambient intensities, and machinic attunement (Simondon, "On the Mode of Existence of Technical Objects”; Deleuze and Guattari). In this configuration, “techno” functions as a conceptual-aesthetic vector, an abstract interface with the machinic, where perception and system become reciprocally generative (Rapp) enacting mediation of (relational) perception and biofeedback. This orientation invites a turn toward aesthetic coherence as it arises within distributed systems, not through semantic unity but through emergent aesthetic consistency. Drawing from machine learning and continental aesthetics, Peli Grietzer proposes aesthetic unity—or vibe—as an “abstractum,” a shared vector of affective coherence across divergent inputs—“the collective affinity of the objects in a class” (27). To approach techno-aesthetics, one must then also consider the role of the abstract collective—the weaved assemblage, and the fold between the layers in the assembled set—as that which constitutes an abstraction paradoxically inherent to the representational logic of human subjectivity. To deepen an understanding of distributed aesthetic unity, Gilles Deleuze’s concept of the Fold, developed in The Fold: Leibniz and the Baroque (1993), offers a critical method here.

For Deleuze, the Fold is a continuous operation and an ontological gesture that replaces the dichotomy of interior and exterior with a topology of relational contingency. Drawing on Leibniz’s monadology, Deleuze envisions folds as infinite, compressed, and enfolded within matter itself. The Fold is thus an abstract architecture of subjectivity shaped through inflection, bending, and co-formation. In a 1986 seminar entitled Subjectification, Deleuze declares: “Folding the outside... placing oneself on the inside of the outside… not at all a personal sort of interiority” (Deleuze; “Foucault Seminar: Part III – Subjectification”). This is the crux of the Fold: it functions as a diagram of subjectivation and a logic of becoming in which the inside is not a given interiority but a position on the “inside of the outside,” formed through recursive engagement with external forces (Deleuze, “Foucault” 103). Subjectivity, in this sense, is not a sealed or closed entity (Deleuze Cinema 1 60; Cinema 2 98), but a transductive process, a topological inflection shaped by the interplay of forces, systems, and sensations (Simondon, “On the Mode of Existence of Technical Objects”; Deleuze, “The Fold”; Massumi). Erin Manning extends this by conceptualizing the Fold as a site of ‘movement-thinking’—a pre-cognitive field where sensation exceeds representation and subjectivity is felt before it is named (Manning). In her terms, the Fold is where affect becomes operative: a generative milieu in which perception, relation, and motion co-compose. Applied to techno-aesthetics, the Fold thus emerges as a structural figure for understanding abstraction as affective proximity—as a sensuous logic across virtual and actual domains. Grietzer’s “vibe” resonates as a maximally virtual Stimmung (mood) that binds disparate elements into affective coherence. It is within this field of recursive abstraction and modulation that techno-ontological operations unfold.

VJ Peter Rubin’s live-mixing practice offer a tactile instantiation of the Fold as techno-aesthetic praxis. Through recursive layers of moving images, flickering projections, and audiovisual loops, Rubin’s performances function as techno-aesthetic sites of both abstraction and subjectivity. His Mayday Vision Mix 1 (see Figure 2) elucidates this dynamic as a saturated palimpsest of re-mixed samples, sensory textures, temporalities and visual intensities. Rubin’s VJ process began with a collection of curated material—TV broadcasts, avant-garde cinema, early computer-generated imagery via the Panasonic MX50, and live footage of ravers—all woven into a real-time choreography of signal and affect. Each source—fixed on VHS tape—formed a discrete “set,” as contained visual archives. Yet in performance, these closed sets became operationally open. Rubin loaded dual VHS decks, switching inputs live to create emergent pairings, modulating rhythm and visual tone in response to crowd energy, musical tempo, and spatial dynamics. The set’s affective potential thus exceeded its material fixity. Rubin’s artistry of discovering aesthetic unity lay not only between media sources, movement in two oscillating images, but also in attuning to the virtual atmospheric and intangible aesthetic inherent in the ambience, pulse, or vibe of the room, setting, and crowd. Through layered rhythms of moving images, flickering projections, and immersive audiovisual loops, Rubin’s work became both a site of artistic experimentation and evidence to techno-aesthetic operations of collective abstraction.

<INSTER IMAGE 2 HERE> Figure 2: Peter Rubin. VHS cassette, box cover. Mayday Visionmix 1. 1992. The Peter Rubin Collection: Amsterdam. Courtesy of Eye Filmmuseum.

<INSTER IMAGE 3 HERE> Figure 3: Peter Rubin. Mayday Visionmix 1. 1992. (curated selection of stills). The Peter Rubin Collection: Amsterdam. Courtesy of Eye Filmmuseum.

The techno-aesthetics of Mayday Vision Mix—split-screen panels, increased source-input options, and hyper-rapid rhythmic alternation or convergent cuts (Deleuze, Cinema 156; Cinema 2 43)— illustrate the operations of aesthetic unity by enacting the techno-ontological Fold in two key ways. First, they emerge from time-coded media infrastructures, shaped by earlier circuits of broadcast and signal transmission. As Friedrich Kittler reminds us, what we can see or hear is always conditioned by what media can store, process, and transmit (Kittler). Rubin’s work channels this media archaeology, elucidating how audiovisual formats are haunted by prior regimes of perception, recursively feeding forward in layered remix that demands a sense of agency. Second, Rubin’s aesthetics anticipate the oversaturated visual logic of contemporary networked media ecologies. His techniques prefigure today’s expanded sensor-media environments constituted by affectively dense visual flows that float in recursive loops—palettes and rhythms unmoored from linear narrative or spatial logic. The Fold, here, becomes an emblem of the audiovisual excesses of contemporary techno-images—images that no longer depict binary subjectivity, but pulse in the folded liminal zones of institutional media regimes and individual affective perception.

Contemporary techno-images can be defined by their hypermodulated plasticity, oversaturated palettes, upload excess, and decoupled spatial logics and topologies. They float in a liquid visual field of ephemeral loops and recursive rhythms, as ‘groundless’ (Gil-Fournier and Parikka; Tehseen) configurations that reflect a profound ontological shift in the form and function of the audiovisual image. No longer tethered to a singular frame, these visuals dissolve traditional cinematic dispositifs (Baudry; Foucault " Power/Knowledge”; Stiegler; Elsaesser). Instead, becoming mutable nodes in a larger structural system of data flows and affective registers. Namely, contemporary techno-aesthetics now operate within vast, slippery, and sticky (Rushkoff; Munster 100) assemblages layered with rendered ambiguity and buffered abstraction. In digital or computational aesthetics, AI systems process imagery through depth maps, shadow buffers, and probabilistic textures that reconfigure perception across machinic and human registers. These operations mark the shift from local, embodied sensation to planetary-scale modulation: from representation to recursive sensation. The techno-aesthetic image becomes a node within a distributed infrastructure that hovers between abstraction and immersion, data and flesh, system and subject. Through the lens of techno-ontology, such images are not simply seen but felt, folded into the environments they circulate within. Rubin’s artistic works, and transition from cinema as experimental filmmaker to techno artist as VJ, anticipates a broader shift toward the logics of techno-aesthetics: hypermodulated, synthetic visuals traversing a "sea of data" (Steyerl), a corpuscular, media-ecological fog (Massumi, 146; Gibson) of disarray and affect of networked bombardment. Rubin’s legacy, then, lies not just in pioneering a live-media performance style, but in foreshadowing a broader ontological condition: where sensation is recursively bombarded, imagery is algorithmic and programmed, and affect becomes distributed, remixed, and infrastructural.

Affective Registers of Folded Distance: Network Disorientation & Distributed Intimacy

Rubin’s remix craft—rooted in sampling, splicing, découpage, and modulation—prefigures the fluid, recursive visual logic that now defines the hyperlinked weave of techno-aesthetics in networked environments. However, Rubin’s early influence draws from abstract, avant-garde, experimental, and structural film traditions, particularly flicker films and kinetic time-based art—as exemplified by Paul Sharits, Peter Kubelka, Eric De Kuyper, etc.—or “the art movement of the 1960s known as ‘op art’ or ‘kinetic art’ (‘art cinétique’)” (Lameris 86). As Bregt Lameris notes, artists like de Kuyper explored rhythm through ‘oscillations and instabilities’ (86), treating these elements as the grammar of sensation. In La beauté du diable, de Kuyper reflects on the rhythmic role of color alternations, suggesting that editing can serve as synesthetic layering akin to musical counterpoint that has clear effect on the spectator (21). These early explorations of flicker, rhythm, and sensory overload laid the groundwork for techno-aesthetic operations and the affective registers of rhythmic spaces and techniques that would later unfold across clubs, screens, and data centers.

From analog light shows to stroboscopic assaults, operations of techno-aesthetics have probed perceptual thresholds of vision by inviting us to see what has no physical anchor. These perceptual strategies trace back to mid-20th-century strobe experiments, which were once scientific, then countercultural, later aesthetic (Canales 183). As the strobe migrated from lab to dancefloor, it carried with it the ghost of altered states, mescaline dreams, and Beat experimentation. In a 1958 Cambridge trial, subjects exposed to flickering light reported visions of microbial life—floating forms without depth or logic (Canales 183). Such images are “object-like impressions” (Massumi), sensory apparitions with form but no fixity, hovering between hallucination and substance. Through light, rhythm, and sensory overload—or what Rubin termed “calculated bombardment”—they create immersive experiences that extend perception into altered, affective territories where the seen and the felt blur. Through rhythm, intensity, and the calculated bombarded chaos of light, they sketch a new networked sensorium where the intangible becomes momentarily felt, and the real flickers in and out of phase. Recursive rhythms and trance-inducing pulses act not merely as stylistic flourishes but as ontological ruptures: moments of ‘ontological shock’ (Tillich; Noorani) that short-circuit or circuit-bend the machinic and rewire sensation and/or perceptual parameters.

Teetering between proprioception and vertigo, ontological shocks intrinsic to folded distance induce affective registers of techno-aesthetics that elucidate what Anna Munster refers to as “network anesthesia” (Munster): where rhythmic ecstasy and numbing simultaneity converge. This somatic network-disorientation functions as both a “technique of ecstasy”—as seen in the promotional marital of the rave Dreamscape (see Figure 4)—and a numbing simultaneity of nodes, links, and flows that obscure relationalities from the local to the global. This techno-ontological shift marks a transition from localized sensory experience to generalized perceptual saturation, where visuality is both expansive and anesthetized, immersive and opaque. This political-aesthetic shift marks the technological transformation from linear input/output models to implementation of recursive feedback loops, expanding distances and fostering distributed intimacies. In networked aesthetics and AI-driven media, however, shock is no longer episodic but ambient—produced through recursive algorithmic mediation, unstable images, and machinic authorship that destabilize the subject’s grounding in space, time, and perception (Denson; Munster; Chun).

<INSERT IMAGE  4 HERE> Dreamscape Flyer from the year 1995. The Peter Rubin Collection: Amsterdam. Courtesy of Eye Filmmuseum.

In the technosphere, the entanglement of networked aesthetics and the constellation of audiovisual data sets now constitute machine learning sequences, pipelines, and inferences of interoperability: aggregate relations between audiovisual sets persist. These sets, as Munster and Adrian MacKenzie suggest, constitute moments where the ceaseless flow of images is momentarily captured and transformed, marking a threshold between perceptual flux and operational meaning.  Affective registers of networked techno-aesthetic operations thus construct an immanent territory constituted by a spatio-temporal energetic urban architecture—a virtual grid or transarchitecture (Novak) where "code as architecture works to structure the boundaries, as well as regulate the flows, of the internet traffic" (Cheney-Lippold 166). The scaffolding of these global communication networks relies on standardized protocols and predictive algorithms for interoperability in ways that exceed human perception, reshaping environments and folding human subjectivity. These media systems, subsequently, embody a tension between determination and indeterminacy as they act as more than tools, actively reconfiguring the conditions of corporeal existence. This “correlative capture” (Denson 40) intertwines phenomenological and statistical forces, embedding political structures in affective and aesthetic realms, shaping and augmenting collective and individual norms. Proprioception—the body’s sense of spatial orientation—becomes redefined through vestibular reconfigurations within global network environments. Drawing on Merleau-Ponty’s corporeal phenomenology, Shane Denson explains that the “spectators’ bodies act as filters, distilling visual phenomena from a range of extraperceptual facets” (Denson). He continues, “in particular, bodies react to invisible algorithmic infrastructures, which, in the case of machine learning algorithms, also operate as filters in their own right. The collision of metabolic and computational microtemporal operations calls forth a number of embodied affects, ranging from sublime awe to disorientation, cringe, and uncanny feelings of relational and environmental entanglement” (Denson 1). The body’s affective register of this "city-like" infrastructure of aggregates thus affords boundaries upon not only sensation or movement, but also embodied perception and identity. Thus, embedding individuals into a recursive algorithmic structural system while simultaneously shaping the possibilities for emergent forms of (sensorial) engagement within networked folded distance(s). Augmented by audiovisual technologies, proprioception thus becomes a mediated act (Fedorova), extending the body’s sensory reach into mapped virtual dimensions. Akin to the flicker film, this augmentation disrupts the equilibrium of center and periphery by producing a sensory disorientation that recalibrates the digital subject’s relationship to space and time as well as the collective and self. Olga Goriunova explains how traffic of this virtual (tracked) movement defines the digital subject. Namely, by situating subjectivity in “an abstracted position, a performance, constructed persona from data, profiles, and other records and aggregates.” (Digital Subjects: An Introduction 126) Recursively affording a dual shaping of the algorithmic infrastructure (or ‘grid’), via movement through the global communications network, as constitutive of subjective perception and affective embodied dimensions.  This political-aesthetic shift signals a technological transformation, from linear input/output models to recursive feedback systems that implement and extend distance as an operational affordance. Within this expanded architecture, the eurhythmic gives rise to distributed intimacy and other distributed forms of attunement. Eurhythmia, when aligned with the collectively entrained body—on the rave floor, in protest formations, or in digital swarms—reveals an affective mode of synchronization shaped by techno-aesthetic infrastructures (Jaques-Dalcroze; Banham). Networked ecologies of rhythmic space, composed of hyperlinked techno-aesthetics, become sites where the eu-rhythmic body emerges (Lefebvre), marking a move away from fixed choreographies toward situated harmonics that resonate across media interfaces, infrastructures, and spatial architectures. This distributed body clusters in folded proximities, with entrained participant embedded in networks of technologically mediated collective sensation. Returning to Deleuze’s notion of subjectification, folded distance becomes the catalyst for eu-rhythmic modulation, acting as interplay for proximity and for dispersal that continually recalibrates embodied affect, perception and relational subjectivity.

Conclusion: Folded Relations in Soft Architectures of the Now

Across rave architectures, digital aesthetics, communication infrastructures, and platform protocols alike, affective sensation loses fixed linear and geographical coordinates. Rhythmic entrainment becomes a mode of dispersed yet collective individuation, a field of distributed intimacy where sensation folds and circulates between bodies, interfaces, (virtual) circuitry, and across recursive loops of light, sound, and code. These are architectures of folded distance are where subjectivity emerges through recursive relation and where presence is glitched, echoed, ambient, refracted. Rhythmic entrainment—the body moving with sound, the VJ’s hand splicing visual flickers to the beat—restructures proximity as affective latency and re-maps sensorial affect, engendering a layered topology threading collections of globalized sensorial assemblages. The techno dancefloor acts as a media archaeological site in this soft architecture of media infrastructure and digital aesthetics and their affordances of distributed intimacy and techno-ontological affect. In these virtual spaces, relationality is buffered and constantly mediated with latency and lag.

Rhythmic recursion as the fold operates as an ontogenetic logic of modulation where the contingent loops of sensation, system, and subject co-compose the consensus of the Real. Folded distance thus marks the contemporary condition of technologically mediated life, where subjectivity emerges from tracked, stacked and layered sensation, becoming virtually infrastructural. In digital ecologies, presence is no longer singular or embodied, but spliced, compressed, and routed through layers of latency, feedback, and interface. Techno-aesthetics extend beyond the rave’s tactility and into the operational aesthetics and techno rhythms of everyday digital infrastructures, such as in platforms and communication networks, where the digital subject is shaped by the rhythmic of pulses curated through feeds, notifications, livestreams, and biometric loops. Intimacy becomes engineered through impersonal cycles of coded attention, where closeness becomes perhaps a function of refresh rate or content velocity. Recursive media thus both simulates nearness as well as automates it. In doing so, it reconfigures the politics of relation through re-mapping the limits of proximity that afford affective sensorial experience. Techno territories where feeling is filtered, optimized, and abstracted into distant, formulaic, and anticipated patterns.

Folded distance thus persists beyond the club or screen. It permeates digital culture through the pulse of notifications, the infinite scroll of feeds, the curated tempo of livestreams. Recursive rhythms, in this context, are the framework for techno-ontological infrastructures. From Rubin’s VJ sets to platform rhythms to AI-generated images, a shift in how techno-aesthetics operate surfaces as a critical site of inquiry as it artificially and generatively mutates. The vibe, once curated through embodied feedback, is now predicted and interpolated through latent data structures. AI remix systems translate atmosphere into vectorized resemblances. In this machinic fold, the VJ becomes a prompt. Bodies disappear into training sets and affect is statistical. Yet even here, the Fold remains. As Deleuze reminds us, the Fold is the site of recursive interiority formed by the inflection of the outside. The techno-aesthetic Fold enacts this topology through entangling agency, authorship, and atmosphere through an increasingly complex logic of globalization and new cartographies mapping of expanded proximities for sensational affect. Yet, whether analog or algorithmic, the question persists: How does aesthetic unity arise in systems without center? What becomes of resonance when the body is no longer proximate, but inferred?

Recursive techno-aesthetics produce proximity without nearness, rhythms without physical or tangible friction. They render intimacy spectral—felt, but dispersed. This is the melancholic edge, the sad design (Lovink, " Sad by Design "), of techno-ontology: the yearning for connection persists even as presence is endlessly mediated, unreachable, and untouchable. This recursion is bifuricated as a Janus-face that promises to hold potential. The techno-event, with all its synthetic intensity, becomes a site of diffraction or agential cuts (Barad 132) where rhythm unsettles fixed boundaries. Yet, without critical reflexivity, this deteritorialization through recursive loops risks reinforcing the logics they might disrupt. As Hui cautions, recursive systems (may) merely mirror acosmic enframings—technological salvationism replacing relation with abstraction (Hui). To move forward, techno-aesthetics must reconcile rupture with repair, disorientation with consciousness as both vestibular and potentially grounding. This means resisting the fetish of recursion for its own sake, and instead fostering a techno-sensibility that critically moves with, rather than beyond, the world—across systems, scales, settings, and folds. In the end, techno-aesthetics offer not answers but questions that are tangled, pulsing, and recursive.

The question then remains: where, exactly, are these folded distances leading us towards?

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Biography  

Megan Phipps is media research, writer, and PhD candidate at Philipps-Universitat Marburg and a member of the Research Collective ‘Configurations of Film’ at Goethe-Universitat Frankfurt. She was previously a Lecturer in Media & Information at the University of Amsterdam, where she also earned her MA in New Media & Digital Culture. She obtained her B.A. in Liberal Arts & Sciences (Humanities) at Tilburg University. Her work spans collection acquisitions for CLARIAH Media Suite, curatorial research at Eye Filmmuseum, and publication in journals like Millennium Film Journal and PULSE. Her doctoral research, rooted in new media curation, audiovisual archive, and media archaeology, investigates the evolution of techno-subcultures through audiovisual performance, with a focus on collective movement, spatiality, and digital cultural values. Her research interests include the history, theory, and philosophy of art, culture, science, and technology.  

The Computational Approach to Aesthetics: value alignment and the political economy of attention in museums and text-to-image generator Stable Diffusion

Sami P. Itävuori

Abstract

Whilst research into cultural value and digital technologies is nascent in art museums, neural media technologies like generative AI pose new methodological and theoretical challenges. Looking at the case of the Tate Gallery and the dataset LAION 5B used to train the text-to-image Stable Diffusion model, the article highlights the long running challenges of studying digital media from a museum perspective. Reflecting on previous uses of AI in the museum, they propose experiments in dataset research and analysis by which museums can evidence the use of their images in the training of Stable Diffusion. But these experiments also aim to develop ways in which changes in cultural value can be analysed and theorised when art collection photographs get operationalized in LAION 5B. Sketching the first steps of an epistemological analysis of image aesthetic assessment and aesthetic predictors from the perspective of museum values and aesthetics, I call for a more thorough engagement with the discourses and practices on art developed in computer sciences so that new collective and connected imaginaries of culture and advanced technology may be constructed.

Introduction

Art museums have tended to frame their understanding of AI as an add-on to existing museum activities and a tool to fulfil legacy missions like conservation, collection activation, museum learning or productivity gains. This utilitarian approach has clear benefits in supporting various aspects of museum work. But focussing too closely on this dimension disregards not only the heterogeneous systems that fall under the umbrella term ‘AI’ but also the economic, social and cultural forces shaping it. Whilst museums like Tate have taken small steps in this direction with projects such as Transforming Collections, a more focused discussion of images and AI, aesthetics and technology, values and automation can help address “the yawning gap” (Rutherford 60) that is said to separate the mindsets of museum practitioners and computer engineers.  

Research into this area is crucial to fully understand the technological ecosystem that art institutions such as the Tate gallery participate in and to inform their public programming and curatorial practices, considering the emerging digital politics of generative AI systems. Museums also raise significant problems about the formation of aesthetic and cultural value around aesthetics and visuality as they get conceived in the case of Stable Diffusion, a popular generative AI model and digital image generation service that is widely used in commercial systems such as Midjourney or DreamStudio.

The aim of this article is to demonstrate the use of images issued from the Tate’s collection in the training dataset of Stable Diffusion (SD) and briefly explain how this influences the production of images generated by this model. Once this link has been established, I will argue that divergent sets of values regarding art and its purpose emerge from the computer sciences literature in what I tentatively propose to call a computational approach to aesthetics. In relation to SD, this approach is not only a set of computational image-processing techniques that radically change the contextual use and nature of images harvested from the net. Instead, these generative techniques also produce and interpellate subjects as objects of scientific research and automation, as well as producers and consumers of data. Not only does SD rely on the automation of creative and cognitive tasks previously performed by humans, but its operations are predicated on prior modes of attention capture and commodification that underlie current digital platform economies (Nixon). Before undertaking this critical discussion, I will shortly survey existing approaches that have been adopted to generative AI within the museum sector in what I think are archetypal examples.  

AI in the Museum

AI’s areas of application in the museum are numerous but I will be focusing on projects involving digital collections of art and how AI has been used to open the collection or create new ways of searching it.

Custom AI models such as the Digital Curator (2022) have been built to retrieve patterns within large amounts of collection data from a consortium of Central European art museums. The browser-based platform enables users to see the statistical occurrence of objects such as “melons” or “monsters” by period, geography or artistic movement. It opens the metadata of collection images to visitors whilst also incentivizing them to discover lesser-known artefacts.

This idea of the discovery of new images is also present in other projects. The Rijksmuseum’s Art Explorer (2024) invites users to write prompts regarding their current emotional state, their likes and dislikes into a browser-ran generative pre-trained transformer model (GPT) that then retrieves assets from the digitized collection. The interface aims to create a more intimate and affective approach to the collection, surfacing  works the user wouldn’t have intuitively searched for themselves.

On the other end of the spectrum in the context of Helsinki Biennale, the Newly Formed City (2023) project deployed AI to curate an online exhibition where artworks from the Helsinki Art Museum’s collection are located on a web mapping platform of the city. The artworks are algorithmically selected, placed on a digital map and the digital images of paintings or sculptures get inserted into the panoramic digital street views of these locations. This model applies a filter to the surrounding landscape, like augmented reality apps. The filter transposes the artwork’s formal qualities such as colour, texture, materials, shapes onto the digital landscape, providing a new experience of the city to local inhabitants and visitors alike.

Many more examples have been recorded in recent literature on the topic of museums and AI more generally. In 2021 Soufian Audry highlighted the emergence of AI as a popular topic for museum exhibitions, surveying eight international exhibitions on the topic (4).

Similarly, the edited volume AI in Museums (ed. Thiel and Bernhardt) present the various applications of AI in all areas of museum work from collection management to education via marketing and curation. Hufschmidt surveys a hundred and twenty-two such projects taking place between 2014 and 2019, with most of them focusing on enhancing visitor experience of the collection with audio guides and collection search-tools (133).  Most recently, the 2025 MuseumNext’s MuseumAI Summit brought together international museum professionals and creative technologists with a focus on collection activation and visitor-data analysis using AI.

Museums are actively adopting AI-powered software to analyse or ‘activate’ the vast amount of data they hold about objects in their collections. But it is very much of ‘adoption’ that I am talking about here, insofar the techniques of artificial intelligence are adopted from outside the museum by either using off-the shelf models or commissioning creative technologists to do it for them. There is nothing inherently wrong about these approaches given the technical complexity of these models and the significant skills and investment custom models require. But this nevertheless isolates the museum and the development of AI products from each other as separate fields of life, activity and reflection, with little to no common ground for dialogue or interrogation.

This separation is not without consequences and contributes to a “yawning gap” between the concepts, mentalities and practices of museums and of developers behind AI systems (Rutherford 60). It reinforces the ‘black box’ narrative that hinders a head-on engagement with the ‘AI tech stack’ (Ivanova et al.) as too complex or too big for scrutiny by researchers outside computer sciences (Bunz 26; Gogalth 175). Whilst issues of bias, privacy or copyright are already being discussed in the sector, art museums are largely lacking means to stir a critical reflection on the inherently social and economic dimensions of AI technologies, their impact on human lives and the role of the museum collection in an era of neural media. Considering AI in the singular mystifies the variety of techniques that are deployed to analyse and synthesize large amounts of data, and the values that guide these deployments. It leads the conversation away from the real problem: the human use of these technologies with and on other humans. The digital politics of museum collections in an information society, the process of defining the values that guide their existence and societal role, thus need to be revised considering emerging AI powered neural medias (Fuchsgruber; Allado-McDowell)

This idea of the museum having societal agency builds on the contemporary articulation of its role as not only sites of collection preservation and exhibition, but also spaces of experience centring the visitor, their needs and agency in what has been called the post-museum (Hooper-Greenhill 22). With roots in the nineteen-nineties new museology, this re-centring of the visitor and the civic role of the museum in the UK had also been pushed since the two-thousands by the focus on culture’s “use-value” in securing government funding (McPherson 46). This reformatted the museum as a site of pedagogy and entertainment, to both address the growing competition of new media and experience economies for public attention, as well as produce measurable impact metrics to justify public funding of these institutions (Scott, Dodd and Sandell 9). Whilst this policy orientation has pushed a new industry of quantitative research about public impact and outreach, the museum object remains conceptualized as holding an ‘intrinsic’ value that ties personal experience to collective meaning making. In this definition, the artefact and museum expertise (organisational, pedagogic and curatorial) mediate the representation of a social group to a symbolic world linking the past to the present as well as a potential future, endowing heritage institutions with a unique societal role (Crossick and Kaszynska 16).  

Whilst this haptic dimension of the museum’s symbolic function is a constant in the justification of museum collections and investment in preservation work, this intrinsic value of the object has been complicated by digital technologies that create distance between the audience, the space and the object, but also new distributed modes of communication about images, stories and experiences of artworks. The meanings and contexts of artworks have been further fragmented in digital networks for instance. These networks multiply the sites and voices that mediate the reception and discussion of artefacts, and trouble the institution’s curatorial authority, which often relies on one-way broadcasting modes of online communication (Styles; Zouli). Online media landscapes complicate not only the measurement, but the very conception of cultural value, as the parameters of art and its images’ experience change (Dewdney and Walsh 15). A trend that only seems to be accentuated by the creation of machines capable of identifying, evaluating and recreating images of art and whose values seem to conflict with values associated with artistic authenticity, creative labour or the disinterestedness of aesthetic experiences. There seems to be an inherent problem with the ‘alignment’ of values between institutional perceptions of art in museum collections and emerging generative AI, which build on previous tensions from preceding digital medias like television or the internet.¹  

So, what is the transformation of cultural value that has taken place with the advent of systems that can produce images of art with natural language text prompts? And what does this say about the emerging relation of art museums to these models?  

To answer this question, I will now evidence the link between Tate gallery and the text-to-image generative AI model Stable Diffusion  

Diffused Images

i) the digital photograph of artworks

The Tate gallery is a national museum in the UK that consists of four geographical sites across London, St. Ives and Liverpool. To use language from its previous media strategies in the early parts of the two-thousands, Tate’s website was conceived as the “fifth site” of Tate with its own programme and dedicated visitor resources (both curatorial experiment and “brochure ware”) (Rellie). The history of this “fifth site” can be traced back to the British Art Information Project (BAIP) of the late 1990s, which promoted the large-scale digitization of collections and archives across national portfolio institutions in the UK. Whilst digital photography of collections started in the early nineties, the launch of the Tate’s website in 1997 is directly tied to this digitization project in the build-up to the opening of the new Tate Britain wing as part of the museum’s Millenium Project.

Figure 1: Screenshot of the Art and Artists Section of the Tate Website. https://www.tate.org.uk/art (accessed 5. June 2025)

The current iteration of the collection website Art and Artists displays more than seventy-seven thousand collection photographs ranging from film stills to paintings via artist sketches and installations. Most photographs are available under creative commons licenses or can be licensed for a fee from Tate. Guiding the release of these images online was the idea of supporting access to the collection, regardless of geographical and temporal boundaries. It supported the fundamental targets of this national museums’ mission statement to promote the appreciation and understanding of British and international art to the public in addition to the preservation of collections in digital form. Adjacent to these aims, the net was also conceived as a site of free information circulation, as well as an expanded marketplace where virtual visits would translate into ‘real’ footfall and income in the gallery (ticketing, catering, gift shop).  

Digital media then becomes a ground where disparate, overlapping values get negotiated: tools to support the cultural and civic mission of the museum to promote its art collection as valuable in and for itself. Tools supporting governmental goals of societal regeneration and education. But also, tools to support the emerging entrepreneurial business model of the museum following public funding cuts (Hughes 9). The production of digital photographs of artworks in museums is thus guided and animated by these divergent and concurrent values that co-exist when being operated internally in organisational databases and circulated on the public facing website.  

The online circulation of the artwork’s digital image leads to a change in its nature. Whilst the physical artwork remains mediated and ‘framed’ by the institutional discourse and values, the digital photograph of the artwork once online gets appropriated, reused and maybe misused in a multitude of ways by online users. The circulation of digital images means that any image posted online undergoes endless copying, compression, editing and pasting that impact the image technically (degraded resolution, dimensions, watermarks, captions), and culturally by decontextualising the image (Steyerl, “In Defence of the Poor Image”). Once online, the museum relinquishes a degree of control over its reception and uses, including its reproduction, derivations or commodification. This network of online circulation is the ground on which the museum meets new AI systems such as Stable Diffusion, which I will briefly present now.    

ii) the images of Stable Diffusion  

Stable Diffusion (SD) is a popular generative AI system developed by the Ludwig-Maximilian University of Munich and the British private company Stability AI. The system can generate images from natural language prompts. SD operates on a diffusion model (DM), which is a process of deep learning (Rombach et al). In training a DM, a set of algorithms called a neural network is iteratively improving its capacity to remember the content of ‘images’ and to reconstitute them from statistical noise. How does it do this? The DM relies on a process of noising - where the data in input images is increasingly deteriorated by inserting Gaussian noise. The aim of the model is to then denoise the degraded image by following successive steps of reconstructing the target image (or ‘re-membering’, putting parts or pixels back into their place). The noisy starting point has some structured clusters of pixels remaining in it, which the model builds on to draw the outlines of its target image.  

How is this prediction process guided? DM utilizes a pre-trained machine vision model called the Contrastive Language-Image Pre-training (CLIP), which ties textual descriptors to image data within a latent space (Radford et al). A latent space is a high-dimensional statistical space where image data and text data are converted into machine-readable numerical tokens. These tokens act almost like coordinates on a 3D map and each token’s position is determined by the statistical frequency of their co-occurrence in the training data. This is essentially the model’s ‘attention’ to the context-specificity of certain words and figures, and how it can differentiate the ‘apple’ in a tree from ‘Apple’ computers. This is also how the model can be steered to produce new images that may not exist in its dataset by writing text-prompts. The prompts connect different areas of the latent space and enable a hybridisation of the data. The model aims to guess how these images would look like and rely on the successive feedback of humans but also automated models to either validate or reject its predictions and to re-adjust its process. The illustration in Figure 2 aims to illustrate the prediction process of SD, denoising a seed image in 4 stages for the prompt “photorealist image of an apple-computer (​​1 steps, 4 steps, 5 steps, 15 steps).

Figure 2: ​​four denoising steps on Stable Diffusion (v. 2023) ran on ComfyUI. https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main

In this broad summary, I hope it is clear that generative models like SD rely on the association of natural language descriptors to image data. Also essential is the compression of this data into a new tokenized form, distributed in a multidimensional vector space according to the statistical frequency of their occurrence. By using language and mathematical abstraction, a semiotic-visual syntax and vocabulary is developed. The model is seemingly able to ‘know’ what an apple or a computer looks like and thus be able to ‘predict’ what an Apple computer or image an ‘apple-computer’ must look like, based on its ‘memory’ and attention to context in the latent space.  

It is inevitable that the popularity of this diffusion model will be supplanted by another model in the years to come, especially given the speed at which research is moving in the current AI arms-race (Aschenbrenner). I would still argue that the fundamental representational logic of generative image-making is unlikely to change. This logic relies on the collecting, modelling and articulation of images with language and mathematics to make them machine-readable. Whilst in contemporary art theory semiotics had been mostly evacuated from the visual field, particularly with the ‘autonomy’ thesis of the artwork and the challenged ‘indexicality’ of photographic images, the concepts behind diffusion models take a diametrically opposite approach. In diffusion models, images are framed as solid representations of a homogeneous reality that is describable by language and statistics. This correspondence of images to reality in DM raises a series of broader epistemological questions about images of art, their treatment in generative AI research and the underlying aesthetic culture of this technology. On this basis it is enlightening to look at the ways images are pre-processed for models like Stable Diffusion in their visual memory bank, namely the dataset LAION 5B.

iii) Images of LAION  

Here the online circulation of collection images ties into the pipeline of machine learning training of SD. The training process requires large amounts of image-text data, which in the case of SD were harvested from the internet using a bot called a crawler. Whilst the initial crawling process of downloading large swaths of the internet was done by a not-for-profit organisation called Common Crawl, it was another not-for-profit, LAION which with the support of Stability.AI and the LMU curated and compiled the LAION dataset (Schuhmann et al). Currently LAION-5B is a five billion strong dataset with images and text harvested from the internet. It contains both the ‘best’ and the ‘worst’ of the internet, from amateur websites to stock photos, photographs posted on Flickr or digitized artworks. In my research at Tate, I chose to work on diffusion models like SD because the training dataset LAION is available open-access and thus searchable. Until recently LAION-5B was available for free download on Hugging Face but in 2024 significant amounts of harmful, abusive and illegal content was discovered, forcing its removal from circulation until a full audit is completed (Thiel 7). The sheer amount of data contained in LAION means that having the images checked by humans was considered impractical, too lengthy and too expensive. For these reasons the reviewing process was automated with an object recognition system. The system was tasked to rate the ‘safety’ of images depending on their likelihood to have harmful content or poor image quality. LAION’s five billion images had been algorithmically reviewed. The algorithm had failed on several occasions.  

A smaller subsection of LAION considered to be safer and of a ‘higher aesthetic quality’ is still online and available to download. This LAION-Aesthetic subset contains eight-million lines of data. Each item includes certain essential information including the source URL of the images and text.  

I coded a simple search tool to identify assets issued from the Tate website. Browser-run search-tools had previously been available for LAION-5B until it had been taken offline. I settled on the use of Datasette, an open-source software that enables the search of datasets using the SQLite relational database model. The software was coded and ran using the cloud-based development environment Github Codespace. I used a free plan and ran the program from my laptop using a 2-core, 8GB RAM, 32GB virtual machine.

 Because LAION-5B and LAION-Aesthetic are presented as relational datasets made of columns holding asset metadata, I had the possibility of filtering LAION-Aesthetic by search terms. As each image-text pair was indexed with their source URL, I was able to filter the database for the domain “tate.org.uk” and received 354 results back. The scraped images ranged from works by J. M. William Turner (thirty-two in total), a photograph of the sculpture Winter Bears (1998) by Jeff Koons or illustrations by Beatrix Potter. Nine women artists were represented out of a total of 125.

Figure 3: Searching for URLs matching “tate.org.uk” on LAION Aesthetic using Datasette

Fourteen works were from the 1700s,147 from the 1800s, 132 works from the 1900s. The rest of the images did not contain a date in their text data. All images, to the exception of Winter Bears, were photographs of two-dimensional paintings. These figures largely reflect the make-up of Tate’s collections. For instance, the Tate holds 37 000 works and sketches by William Turner, the majority of which has been digitized.

This selection of images illustrates a data bias towards representing works from the art historical canon. The type of images present in databases like LAION are an essential part of the conversation on the biases of AI systems, which has been the subject of significant attention in critical AI scholarship and computer sciences (Ferrara 2). This bias is also recognised by the developers of SD: “deep learning modules tend to reproduce or exacerbate biases that are already present in the data” (Rombach et al. 9). But saying that the bias lies “already” in the data seems to ignore biases that occur when programmers process, mediate and operationalize the collected data (Offert and Bell 1133). By this I mean that LAION is not just made up of raw data collected from the wild. Instead, a series of human decisions based on cultural and technical rationales determine what data gets used, how and why. For this reason, models like SD don’t generate new media just out of raw data but are deeply informed by decisions underlying the collection of data, human interpretation of this data and the aims they want to achieve with it. The bias is already in the human process of capturing the world as information in what could be called the ‘capta’ (Drucker 2).

Looking closer at other categories of the dataset the column “aesthetic” stands out. “Aesthetic” denotes the aesthetic score attributed to each image on a sliding scale of zero to ten to measure its aesthetic quality. This quality score is a prediction of the image’s appeal to a human viewer and the scores categorize images in LAION from poor to good quality. The images are distributed in ‘buckets’, that is groupings of images by score. The lowest buckets are poor resolution images with watermarks for example and those which supposedly contain potentially harmful content, whilst the high-quality images are in the buckets 8 to 10. Thus, images are not only described in terms of their content: apples. They are also rated: this image of apple scores 8.18764114379828. This is very interesting at two levels: how are images evaluated for their aesthetic quality? And why are they evaluated? It is to these questions I turn attention to in the next section.

Figure 4: Image of Apples in LAION Aesthetic. Described as ‘apples’. Aesthetic Score 8.18764114379828. Source: Bird Feeder Expert website, https://birdfeederexpert.com/wp-content/uploads/2020/12/orchard-1872997_640.jpg

Image aesthetic assessment: virtual viewers and platform capitalism  

Just as they are analysed for perceived harmful content (their safety score), images in LAION are allocated an aesthetic score automatically using a modified version of the CLIP model called an aesthetic predictor (Schuhmann & Beaumont).    

Two datasets were used in CLIP aesthetic predictor’s pre-training, namely the Simulacra-Aesthetic Captioning (SAC) and the Aesthetic Visual Dataset (AVA). Both datasets are considered benchmarks in machine vision research. Both SAC and AVA contain photographs scored by humans either on online photo competition platforms or by research participants in academic studies. SAC contains ratings for 230 000 AI-generated images (Pressman), whilst AVA contains 250 000 images with ratings and comments collected from the photo-challenge platform DP.Challenge (Murray et al.). Both SAC and AVA were used in training the aesthetic predictor for LAION and thus inform the production of SD’s memory and its parameters for evaluating appeal. But how is this appeal defined and determined? Especially since appeal appears at first as a subjective phenomenon.

Underlying the automation of aesthetic rating, lies the scientific effort to theorise and measure the impact of images on humans or elucidate the qualities that make an image appealing. The field of Image Aesthetic Assessment (IAA) takes up this question, with research being undertaken in neurosciences, cognitive psychology, computing and marketing research (Bodini 5). The aim of IAA is to determine what makes an image beautiful and to produce experimental apparatuses, including computational simulations, to support these theories. Surveys of the literature show significant previous efforts to develop an automated assessor based on the formal analysis of artworks. This means hand-crafting arbitrary lists of positive visual qualities, such as composition, subject, rule of thirds, colour combinations, contrast and more (Deng, Loy and Tang, 6). This approach however has lost in popularity since the appearance of deep neural networks that utilize large amounts of data and bypass the need for hand-selected features. The idea that there are a-priori formal qualities underlying the aesthetic appeal of images could be called an objectivist approach to the study of aesthetics (Bodini 4), because it centres the object’s qualities as the source of subjective aesthetic experiences. In contrast, current deep learning models aim to mimic the behaviour of human test-groups without prior knowledge of formal qualities that make a visual object appealing. In this subjectivist approach, the impact of an image on its observer takes precedence over formal qualities. In general, this approach aims to measure the impact of individual images on a scale from zero to ten on thousands of human test subjects to produce average scores on a large body of images (Folgerø 19). A deep neural network is then trained to start recognizing patterns of pixels that tend to be associated with high human scores. These minute pixel patterns that are invisible to the human eye are the building blocks from which the machine perceives appeal, a process that seems completely opposite to human ways of perceiving and receiving images as totalities, rather than minute details. In this paradigm, appeal is not a tangible objective quality but a statistical trend that ‘emerges’ from the aggregate of human ratings collected ‘in the wild’. Echoing Chris Anderson’s controversial 2008 claim that big data marks the end of theory in scientific research, current IAA paradigms assert that with enough data, numbers can not only explain but also make aesthetic judgements.  

CLIP Aesthetic is built on this subjectivist approach because it has taken most of its scores from online photography websites. One of them, DP.Challenge, is an amateur photography competition run by the Digital Photography Review since 2002. Users are invited to organise thematic competitions and to upload images, which are then anonymously scored and commented. This is the data used for CLIP, but other researchers have proposed to use Reddit’s r/photography discussion thread (Nieto et al) or Flickr comments (Soydaner et al) as alternative data sources to train IAA deep learning models. The scores are averaged, but also sometimes require correcting as simple averages tend to neglect sentiment polarity. Polarity, the presence of both very high and low ratings simultaneously, defines an image as ‘divisive’ because the consensus on its score is considered less reliable. These polarizing images are often removed from the training set because images that have wider appeal are considered ‘truer’ examples of aesthetic attractiveness since they gather unanimous agreement amongst scorers (Park and Zhang). In a sense CLIP aesthetic predictor is a simulation of user behaviour on platforms like DP.Challenge (​​as well as the university student test group of AVA, which should be discussed in a separate paper). The aesthetic predictor’s aim is to predict reliably the appeal of images, and thus the middle of the road, or ‘mean’ aesthetic gets prioritised over images that may be divisive or appear unconventional to users. Underlying these aims is to make the model as popular and appealing as possible to a wide user- and consumer-base.  

This notion of unreliability is important to discuss the differing value systems that guide SD and museums when they utilize digital images of art. In the training pipeline, the attribution of aesthetic scores dispels the idea of text-image data as something “already” in the data. It is a capta in the sense that the choices underlying the selection of the data, the source of the data and the truth-value assigned to this data are processes of capturing and socially interpreting information within an epistemic and technical framework. In this situation, the aim to produce appealing images with SD means that a way of formalising aesthetic appeal becomes a technical pre-requisite for the synthesis of new images. Images from museum collections are inputs for machine learning and the cognitive processes of human viewers of art also become conceived as inputs. The formalization of appeal requires its definition and in the case of CLIP-aesthetic, appeal is defined as the statistical frequency of pixel patterns unconsciously liked by online photo communities. The user ratings from DP.Challenge then constitute a ‘ground truth’ of aesthetic appeal (Sluis) in the development of image generators like SD. These ratings are representative of a generalizable human cognitive response to images and can be inferred to make guesses about the appeal of future images. This process is automated in CLIP and finally defines the aesthetics and visual look of images produced by SD. Generative AI then not only translates a further datafication and commodification of images of art, but a datafication of photo-competition participants’ cognitive labour when they produce scores and feedback about photographs. In this sense, these systems do not only treat images from national art collections as a means to an end, but they also objectify human interactions with these images, they objectify aesthetics.

Analysed from a visual cultures or aesthetic theory perspective, this approach seems to have several problems. The inductive nature of the reasoning behind the operationalization of this subjectivist approach is problematic because the ratings of amateur hobbyist photographers from North America are confounded with a universalizable notion of aesthetic taste (Sluis and Palmer). This generalization highlights a Western photographic unconsciousness in generative AI but also points to the strong geographic and cultural contingency of the aesthetics promoted by DP.Challenge. This also applies to CLIP aesthetic as it was trained on the same data. It could be said then, that LAION-5B is organised by an automated ‘virtual viewer’ of these images, a sort of ‘virtual connoisseur’ with median aesthetic taste, engineered by a mixture of cognitive psychology, neuroaesthetics, statistics and computing. The aesthetic predictor is a form of automata, performing human-like labour of indexing and tagging but its logic seems alien to the affective charge of human reception and interpretation of images. It is built on data about human behaviour, but the predictor’s behaviour is very different from the subjective human experience of the world, which seems to change at every iteration, particularly in the case of art. Art is characterized by the difficulty to agree on its definition, but also by a polyvocality in the experience of individual artworks. To just quote a historical example used by Helliwell to discuss value alignment in AGI, the work of Vincent Van Gogh, who was one of the early artists to be used to showcase the power of style-transfer (Gatys et al 5), was derided during his lifetime. His work got positive recognition only decades after his death and points to the fact that the appreciation of artistic styles changes over time and is not a fixed quality, which can be captured at one point and reproduced indefinitely. Theoretically this means then that SD’s logic would prohibit the spontaneous emergence of new visual aesthetics that do not conform to existing tastes and preferences. The algorithms then appear as deeply conservative. A reality that stands at odds with the recurrent discourse of progress, democracy and futurity invoked by the developers of these systems.  

Conclusion: From Images to Attention Economy

But why is generative image-making the object of so much economic investment, artistic controversy and popular mass adoption? I argue that the techniques behind image generation actually build upon and reinforce the commodification of the online space. It frames users as customers and any data as resources to be extracted and monetized. The subject being automated in generative AI’s virtual viewer reflects this atomized subject of digital platform economies. Online users are being atomized because the platforms on which they build their online existence aim to increasingly isolate them from each other, whilst also extracting as much capital from them in the form of service income or the data they produce (Bridle, 91). This atomization is further reinforced by these platforms’ reliance, from social media to generative platform, on the commodification, capture and retention of the users’ time and attention. This race to capture attention lies behind early investment in IAA research  so companies could better understand how online consumers act. Internet users are simultaneously interpellated ² as consumers by algorithmic recommendations within a wider digital marketplace for the provision of goods and services (Terranova 2; Hentschel, Kobs and Hotho 2; Baeza-Yates and Fayyad 132). The political economy of attention in communicative media discussed by Nixon ties the epistemology and techniques of aesthetic appeal in generative AI. It also ties to the wider exploitation of data produced by image-makers, artists, museums and online commentators worldwide. This process of attention capture, extraction and image generation perpetuate data colonialism’s framing of digital networked images as “an ‘open’ resource for extraction that is somehow ‘just there’ for capital” (Couldry and Mejias, 337). Previous logics of platform companies such as search engines and social media extend the creation of new consumer needs in the form of mercantile image-generation services online. By extracting digital images, companies selling generative AI services have effectively privatized the internet commons. This lies at the source of controversies with artist lawsuits, scriptwriter strikes and cross-sector concern about the future of creative economies. This process of data pillaging, data colonization and privatization runs parallel and within the scientific project to measure, define and quantify human psychological and cognitive processes to predict the appeal of images, messages and information, both analogue and now synthetic. These techniques of observation, and now generation, continue to inform a symbiotic relationship between emerging modes of visual culture, scientific study of human cognition and emerging modes of economic exploitation (Crary).

This subject atomisation in the digital sphere and reality driven by the aesthetic predictor, the visuality of SD or the marketplace of consumer-oriented platforms stands at odds with the cultural values that are usually associated with artistic heritage and its digital images. Leaving aside the ways in which generative AI models decontextualize all images in their datasets to exploit their representativeness and reduce them to textual descriptors and aesthetic scores, the political economy of these models tends to disenfranchise artists and break the symbolic function of images as a site where meaning, identity and histories are collectively negotiated, preserved or relinquished. As pointed out by Steyerl, the formation of a common-sense of aesthetics relies on the messy, asynchronous and sometimes unresolved reception of images, whose attraction may endure even if their appeal is polarizing (Medium Hot, 51). This raises questions about the possibility of alignment of museum missions with the emerging visuality of generative AI.  

Despite their creators’ aspiration to ‘democratize high-resolution image synthesis’ (Rombach et al. 1), the inherent political economy of the aesthetics of systems like SD appear more to alienate than to strengthen social bonds and promote creativity. LAION’s virtual viewer is not fully the same viewer as the human in the gallery space or the museum website. The instability and free-play associated with aesthetics gets reduced to rational choice-making subjects modelled according to contemporary market logics in aesthetic predictors. Not that market logic is exclusive to the machine, it also animates the values behind museum entrepreneurialism, although these values are constantly negotiated and problematized within the linear progressive values of the institution issued from representational artistic modernity (Dewdney, 5).  

By standing at the crossroads of new forms of economic exploitation and emerging forms of human image making, generative AI problematizes what it means to look at images, where we look at them and what infrastructures facilitate these modes and techniques of vision. It also raises questions regarding art, images and private property. The process of privatizing data issued from the internet commons reframes all data as a resource to extract and own, thus determining who gets to monetize it, when and how (Bailkin, 14). A similar paradigm of property ownership is characteristic of the way in which museum collections operate, the exhibition being a format that conditions what works can be seen, how and when. But museum websites have gradually disrupted this property paradigm. For instance they have aspired to open the collection’s stores and promote the idea of artwork stewardship: namely that artworks in collections are not mere possessions but rather common goods that need to be managed for the good of all (Cheng-Davies, 290). Common goods understood as that which benefits the community of users, be it in the promotion of social cohesion, provision of education, improvement of mental health or some other projected value of the artwork. Whilst the common good can be seen to animate museum activities when they release digitized collection data online and partake in public programming, it is less evident in the way that commercial generative AI platforms use images issued from national collections. The opacity of platforms like Midjourney or Dall-E that hides behind convenient user-interfaces, reinforces barriers to a wider understanding of how these systems work and the economic processes that make them possible. This then poses two problems for the humanities and museum institutions: how can cultural institutions reassert a common capacity of anyone to understand and tinker with these systems? What mechanisms or imaginaries need to be formulated, and by whom, to redistribute the benefits of these generative technologies for a common good: creatively, societally, financially?

The task at hand is then to develop strategies, curatorially, organisationally or infrastructurally that promote the reappropriation of data heritage, digital commons and a social ownership of the means of prediction. As a site of heritage, with buildings, expertise, objects and archives, the museum has the affordance to bring strangers together and maybe turn them into neighbours and community-members (Balshaw). This coming together is essential, not only to share grievances but also a common capacity to deliberate what the common good looks like in a specific situation, place and time. Even if the museum is defined by conflicting values including strong market forces, it also values criticality as a mode of culturally engaging with their histories and collections. This means there are affordances in the current value-system and infrastructure of the museum to engage in this conversation about re-commoning data heritage. As argued at the beginning of this article, the starting point needs to be an understanding of the technology from the perspective of those affected by it: institutions, humans, communities and cultures. As Katz writes: “Explanation of rules is a prerequisite for the democratic control of rules”. (22)​ ​ What this democracy may look like in the museum remains to be imagined and opens a line of research into the techno-aesthetics of generative AI models, not only to inform new museum activities but maybe assert the right and necessity of cultural workers to have a say in the ideas and applications of these fast-moving techniques. It is thus not only the rules guiding the algorithms that need to be explained but also the rules of the emerging political economy of corporations and digital platforms powered and guided by AI that need to be elucidated within visual culture.  

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Biography

Sami P. Itävuori (he/they) is a London-based researcher, curator and cultural programmer with a specific interest in advanced technologies, audio-visual cultures and contemporary museum practices. Their practice is informed by community-centring approaches that promote skill-sharing, self-organization and alternative modes of making and art. They are on the board nomination committee of Anrikningsverket/Norbergfestival and are a PhD student at London South Bank University’s Centre for the Study of the Networked Image, the Royal College of Art and Tate.

The Computational Approach to Aesthetics: value alignment and the political economy of attention in museums and text-to-image generator Stable Diffusion

Sami P. Itävuori

Abstract

Whilst research into cultural value and digital technologies is nascent in art museums, neural media technologies like generative AI pose new methodological and theoretical challenges. Looking at the case of the Tate Gallery and the dataset LAION 5B used to train the text-to-image Stable Diffusion model, the article highlights the long running challenges of studying digital media from a museum perspective. Reflecting on previous uses of AI in the museum, they propose experiments in dataset research and analysis by which museums can evidence the use of their images in the training of Stable Diffusion. But these experiments also aim to develop ways in which changes in cultural value can be analysed and theorised when art collection photographs get operationalized in LAION 5B. Sketching the first steps of an epistemological analysis of image aesthetic assessment and aesthetic predictors from the perspective of museum values and aesthetics, I call for a more thorough engagement with the discourses and practices on art developed in computer sciences so that new collective and connected imaginaries of culture and advanced technology may be constructed.

Introduction

Art museums have tended to frame their understanding of AI as an add-on to existing museum activities and a tool to fulfil legacy missions like conservation, collection activation, museum learning or productivity gains. This utilitarian approach has clear benefits in supporting various aspects of museum work. But focussing too closely on this dimension disregards not only the heterogeneous systems that fall under the umbrella term ‘AI’ but also the economic, social and cultural forces shaping it. Whilst museums like Tate have taken small steps in this direction with projects such as Transforming Collections, a more focused discussion of images and AI, aesthetics and technology, values and automation can help address “the yawning gap” (Rutherford 60) that is said to separate the mindsets of museum practitioners and computer engineers.  

Research into this area is crucial to fully understand the technological ecosystem that art institutions such as the Tate gallery participate in and to inform their public programming and curatorial practices, considering the emerging digital politics of generative AI systems. Museums also raise significant problems about the formation of aesthetic and cultural value around aesthetics and visuality as they get conceived in the case of Stable Diffusion, a popular generative AI model and digital image generation service that is widely used in commercial systems such as Midjourney or DreamStudio.

The aim of this article is to demonstrate the use of images issued from the Tate’s collection in the training dataset of Stable Diffusion (SD) and briefly explain how this influences the production of images generated by this model. Once this link has been established, I will argue that divergent sets of values regarding art and its purpose emerge from the computer sciences literature in what I tentatively propose to call a computational approach to aesthetics. In relation to SD, this approach is not only a set of computational image-processing techniques that radically change the contextual use and nature of images harvested from the net. Instead, these generative techniques also produce and interpellate subjects as objects of scientific research and automation, as well as producers and consumers of data. Not only does SD rely on the automation of creative and cognitive tasks previously performed by humans, but its operations are predicated on prior modes of attention capture and commodification that underlie current digital platform economies (Nixon). Before undertaking this critical discussion, I will shortly survey existing approaches that have been adopted to generative AI within the museum sector in what I think are archetypal examples.  

AI in the Museum

AI’s areas of application in the museum are numerous but I will be focusing on projects involving digital collections of art and how AI has been used to open the collection or create new ways of searching it.

Custom AI models such as the Digital Curator (2022) have been built to retrieve patterns within large amounts of collection data from a consortium of Central European art museums. The browser-based platform enables users to see the statistical occurrence of objects such as “melons” or “monsters” by period, geography or artistic movement. It opens the metadata of collection images to visitors whilst also incentivizing them to discover lesser-known artefacts.

This idea of the discovery of new images is also present in other projects. The Rijksmuseum’s Art Explorer (2024) invites users to write prompts regarding their current emotional state, their likes and dislikes into a browser-ran generative pre-trained transformer model (GPT) that then retrieves assets from the digitized collection. The interface aims to create a more intimate and affective approach to the collection, surfacing  works the user wouldn’t have intuitively searched for themselves.

On the other end of the spectrum in the context of Helsinki Biennale, the Newly Formed City (2023) project deployed AI to curate an online exhibition where artworks from the Helsinki Art Museum’s collection are located on a web mapping platform of the city. The artworks are algorithmically selected, placed on a digital map and the digital images of paintings or sculptures get inserted into the panoramic digital street views of these locations. This model applies a filter to the surrounding landscape, like augmented reality apps. The filter transposes the artwork’s formal qualities such as colour, texture, materials, shapes onto the digital landscape, providing a new experience of the city to local inhabitants and visitors alike.

Many more examples have been recorded in recent literature on the topic of museums and AI more generally. In 2021 Soufian Audry highlighted the emergence of AI as a popular topic for museum exhibitions, surveying eight international exhibitions on the topic (4).

Similarly, the edited volume AI in Museums (ed. Thiel and Bernhardt) present the various applications of AI in all areas of museum work from collection management to education via marketing and curation. Hufschmidt surveys a hundred and twenty-two such projects taking place between 2014 and 2019, with most of them focusing on enhancing visitor experience of the collection with audio guides and collection search-tools (133).  Most recently, the 2025 MuseumNext’s MuseumAI Summit brought together international museum professionals and creative technologists with a focus on collection activation and visitor-data analysis using AI.

Museums are actively adopting AI-powered software to analyse or ‘activate’ the vast amount of data they hold about objects in their collections. But it is very much of ‘adoption’ that I am talking about here, insofar the techniques of artificial intelligence are adopted from outside the museum by either using off-the shelf models or commissioning creative technologists to do it for them. There is nothing inherently wrong about these approaches given the technical complexity of these models and the significant skills and investment custom models require. But this nevertheless isolates the museum and the development of AI products from each other as separate fields of life, activity and reflection, with little to no common ground for dialogue or interrogation.

This separation is not without consequences and contributes to a “yawning gap” between the concepts, mentalities and practices of museums and of developers behind AI systems (Rutherford 60). It reinforces the ‘black box’ narrative that hinders a head-on engagement with the ‘AI tech stack’ (Ivanova et al.) as too complex or too big for scrutiny by researchers outside computer sciences (Bunz 26; Gogalth 175). Whilst issues of bias, privacy or copyright are already being discussed in the sector, art museums are largely lacking means to stir a critical reflection on the inherently social and economic dimensions of AI technologies, their impact on human lives and the role of the museum collection in an era of neural media. Considering AI in the singular mystifies the variety of techniques that are deployed to analyse and synthesize large amounts of data, and the values that guide these deployments. It leads the conversation away from the real problem: the human use of these technologies with and on other humans. The digital politics of museum collections in an information society, the process of defining the values that guide their existence and societal role, thus need to be revised considering emerging AI powered neural medias (Fuchsgruber; Allado-McDowell)

This idea of the museum having societal agency builds on the contemporary articulation of its role as not only sites of collection preservation and exhibition, but also spaces of experience centring the visitor, their needs and agency in what has been called the post-museum (Hooper-Greenhill 22). With roots in the nineteen-nineties new museology, this re-centring of the visitor and the civic role of the museum in the UK had also been pushed since the two-thousands by the focus on culture’s “use-value” in securing government funding (McPherson 46). This reformatted the museum as a site of pedagogy and entertainment, to both address the growing competition of new media and experience economies for public attention, as well as produce measurable impact metrics to justify public funding of these institutions (Scott, Dodd and Sandell 9). Whilst this policy orientation has pushed a new industry of quantitative research about public impact and outreach, the museum object remains conceptualized as holding an ‘intrinsic’ value that ties personal experience to collective meaning making. In this definition, the artefact and museum expertise (organisational, pedagogic and curatorial) mediate the representation of a social group to a symbolic world linking the past to the present as well as a potential future, endowing heritage institutions with a unique societal role (Crossick and Kaszynska 16).  

Whilst this haptic dimension of the museum’s symbolic function is a constant in the justification of museum collections and investment in preservation work, this intrinsic value of the object has been complicated by digital technologies that create distance between the audience, the space and the object, but also new distributed modes of communication about images, stories and experiences of artworks. The meanings and contexts of artworks have been further fragmented in digital networks for instance. These networks multiply the sites and voices that mediate the reception and discussion of artefacts, and trouble the institution’s curatorial authority, which often relies on one-way broadcasting modes of online communication (Styles; Zouli). Online media landscapes complicate not only the measurement, but the very conception of cultural value, as the parameters of art and its images’ experience change (Dewdney and Walsh 15). A trend that only seems to be accentuated by the creation of machines capable of identifying, evaluating and recreating images of art and whose values seem to conflict with values associated with artistic authenticity, creative labour or the disinterestedness of aesthetic experiences. There seems to be an inherent problem with the ‘alignment’ of values between institutional perceptions of art in museum collections and emerging generative AI, which build on previous tensions from preceding digital medias like television or the internet.¹  

So, what is the transformation of cultural value that has taken place with the advent of systems that can produce images of art with natural language text prompts? And what does this say about the emerging relation of art museums to these models?  

To answer this question, I will now evidence the link between Tate gallery and the text-to-image generative AI model Stable Diffusion  

Diffused Images

i) the digital photograph of artworks

The Tate gallery is a national museum in the UK that consists of four geographical sites across London, St. Ives and Liverpool. To use language from its previous media strategies in the early parts of the two-thousands, Tate’s website was conceived as the “fifth site” of Tate with its own programme and dedicated visitor resources (both curatorial experiment and “brochure ware”) (Rellie). The history of this “fifth site” can be traced back to the British Art Information Project (BAIP) of the late 1990s, which promoted the large-scale digitization of collections and archives across national portfolio institutions in the UK. Whilst digital photography of collections started in the early nineties, the launch of the Tate’s website in 1997 is directly tied to this digitization project in the build-up to the opening of the new Tate Britain wing as part of the museum’s Millenium Project.

Figure 1: Screenshot of the Art and Artists Section of the Tate Website. https://www.tate.org.uk/art (accessed 5. June 2025)

The current iteration of the collection website Art and Artists displays more than seventy-seven thousand collection photographs ranging from film stills to paintings via artist sketches and installations. Most photographs are available under creative commons licenses or can be licensed for a fee from Tate. Guiding the release of these images online was the idea of supporting access to the collection, regardless of geographical and temporal boundaries. It supported the fundamental targets of this national museums’ mission statement to promote the appreciation and understanding of British and international art to the public in addition to the preservation of collections in digital form. Adjacent to these aims, the net was also conceived as a site of free information circulation, as well as an expanded marketplace where virtual visits would translate into ‘real’ footfall and income in the gallery (ticketing, catering, gift shop).  

Digital media then becomes a ground where disparate, overlapping values get negotiated: tools to support the cultural and civic mission of the museum to promote its art collection as valuable in and for itself. Tools supporting governmental goals of societal regeneration and education. But also, tools to support the emerging entrepreneurial business model of the museum following public funding cuts (Hughes 9). The production of digital photographs of artworks in museums is thus guided and animated by these divergent and concurrent values that co-exist when being operated internally in organisational databases and circulated on the public facing website.  

The online circulation of the artwork’s digital image leads to a change in its nature. Whilst the physical artwork remains mediated and ‘framed’ by the institutional discourse and values, the digital photograph of the artwork once online gets appropriated, reused and maybe misused in a multitude of ways by online users. The circulation of digital images means that any image posted online undergoes endless copying, compression, editing and pasting that impact the image technically (degraded resolution, dimensions, watermarks, captions), and culturally by decontextualising the image (Steyerl, “In Defence of the Poor Image”). Once online, the museum relinquishes a degree of control over its reception and uses, including its reproduction, derivations or commodification. This network of online circulation is the ground on which the museum meets new AI systems such as Stable Diffusion, which I will briefly present now.    

ii) the images of Stable Diffusion  

Stable Diffusion (SD) is a popular generative AI system developed by the Ludwig-Maximilian University of Munich and the British private company Stability AI. The system can generate images from natural language prompts. SD operates on a diffusion model (DM), which is a process of deep learning (Rombach et al). In training a DM, a set of algorithms called a neural network is iteratively improving its capacity to remember the content of ‘images’ and to reconstitute them from statistical noise. How does it do this? The DM relies on a process of noising - where the data in input images is increasingly deteriorated by inserting Gaussian noise. The aim of the model is to then denoise the degraded image by following successive steps of reconstructing the target image (or ‘re-membering’, putting parts or pixels back into their place). The noisy starting point has some structured clusters of pixels remaining in it, which the model builds on to draw the outlines of its target image.  

How is this prediction process guided? DM utilizes a pre-trained machine vision model called the Contrastive Language-Image Pre-training (CLIP), which ties textual descriptors to image data within a latent space (Radford et al). A latent space is a high-dimensional statistical space where image data and text data are converted into machine-readable numerical tokens. These tokens act almost like coordinates on a 3D map and each token’s position is determined by the statistical frequency of their co-occurrence in the training data. This is essentially the model’s ‘attention’ to the context-specificity of certain words and figures, and how it can differentiate the ‘apple’ in a tree from ‘Apple’ computers. This is also how the model can be steered to produce new images that may not exist in its dataset by writing text-prompts. The prompts connect different areas of the latent space and enable a hybridisation of the data. The model aims to guess how these images would look like and rely on the successive feedback of humans but also automated models to either validate or reject its predictions and to re-adjust its process. The illustration in Figure 2 aims to illustrate the prediction process of SD, denoising a seed image in 4 stages for the prompt “photorealist image of an apple-computer (​​1 steps, 4 steps, 5 steps, 15 steps).

Figure 2: ​​four denoising steps on Stable Diffusion (v. 2023) ran on ComfyUI. https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main

In this broad summary, I hope it is clear that generative models like SD rely on the association of natural language descriptors to image data. Also essential is the compression of this data into a new tokenized form, distributed in a multidimensional vector space according to the statistical frequency of their occurrence. By using language and mathematical abstraction, a semiotic-visual syntax and vocabulary is developed. The model is seemingly able to ‘know’ what an apple or a computer looks like and thus be able to ‘predict’ what an Apple computer or image an ‘apple-computer’ must look like, based on its ‘memory’ and attention to context in the latent space.  

It is inevitable that the popularity of this diffusion model will be supplanted by another model in the years to come, especially given the speed at which research is moving in the current AI arms-race (Aschenbrenner). I would still argue that the fundamental representational logic of generative image-making is unlikely to change. This logic relies on the collecting, modelling and articulation of images with language and mathematics to make them machine-readable. Whilst in contemporary art theory semiotics had been mostly evacuated from the visual field, particularly with the ‘autonomy’ thesis of the artwork and the challenged ‘indexicality’ of photographic images, the concepts behind diffusion models take a diametrically opposite approach. In diffusion models, images are framed as solid representations of a homogeneous reality that is describable by language and statistics. This correspondence of images to reality in DM raises a series of broader epistemological questions about images of art, their treatment in generative AI research and the underlying aesthetic culture of this technology. On this basis it is enlightening to look at the ways images are pre-processed for models like Stable Diffusion in their visual memory bank, namely the dataset LAION 5B.

iii) Images of LAION  

Here the online circulation of collection images ties into the pipeline of machine learning training of SD. The training process requires large amounts of image-text data, which in the case of SD were harvested from the internet using a bot called a crawler. Whilst the initial crawling process of downloading large swaths of the internet was done by a not-for-profit organisation called Common Crawl, it was another not-for-profit, LAION which with the support of Stability.AI and the LMU curated and compiled the LAION dataset (Schuhmann et al). Currently LAION-5B is a five billion strong dataset with images and text harvested from the internet. It contains both the ‘best’ and the ‘worst’ of the internet, from amateur websites to stock photos, photographs posted on Flickr or digitized artworks. In my research at Tate, I chose to work on diffusion models like SD because the training dataset LAION is available open-access and thus searchable. Until recently LAION-5B was available for free download on Hugging Face but in 2024 significant amounts of harmful, abusive and illegal content was discovered, forcing its removal from circulation until a full audit is completed (Thiel 7). The sheer amount of data contained in LAION means that having the images checked by humans was considered impractical, too lengthy and too expensive. For these reasons the reviewing process was automated with an object recognition system. The system was tasked to rate the ‘safety’ of images depending on their likelihood to have harmful content or poor image quality. LAION’s five billion images had been algorithmically reviewed. The algorithm had failed on several occasions.  

A smaller subsection of LAION considered to be safer and of a ‘higher aesthetic quality’ is still online and available to download. This LAION-Aesthetic subset contains eight-million lines of data. Each item includes certain essential information including the source URL of the images and text.  

I coded a simple search tool to identify assets issued from the Tate website. Browser-run search-tools had previously been available for LAION-5B until it had been taken offline. I settled on the use of Datasette, an open-source software that enables the search of datasets using the SQLite relational database model. The software was coded and ran using the cloud-based development environment Github Codespace. I used a free plan and ran the program from my laptop using a 2-core, 8GB RAM, 32GB virtual machine.

 Because LAION-5B and LAION-Aesthetic are presented as relational datasets made of columns holding asset metadata, I had the possibility of filtering LAION-Aesthetic by search terms. As each image-text pair was indexed with their source URL, I was able to filter the database for the domain “tate.org.uk” and received 354 results back. The scraped images ranged from works by J. M. William Turner (thirty-two in total), a photograph of the sculpture Winter Bears (1998) by Jeff Koons or illustrations by Beatrix Potter. Nine women artists were represented out of a total of 125.

Figure 3: Searching for URLs matching “tate.org.uk” on LAION Aesthetic using Datasette

Fourteen works were from the 1700s,147 from the 1800s, 132 works from the 1900s. The rest of the images did not contain a date in their text data. All images, to the exception of Winter Bears, were photographs of two-dimensional paintings. These figures largely reflect the make-up of Tate’s collections. For instance, the Tate holds 37 000 works and sketches by William Turner, the majority of which has been digitized.

This selection of images illustrates a data bias towards representing works from the art historical canon. The type of images present in databases like LAION are an essential part of the conversation on the biases of AI systems, which has been the subject of significant attention in critical AI scholarship and computer sciences (Ferrara 2). This bias is also recognised by the developers of SD: “deep learning modules tend to reproduce or exacerbate biases that are already present in the data” (Rombach et al. 9). But saying that the bias lies “already” in the data seems to ignore biases that occur when programmers process, mediate and operationalize the collected data (Offert and Bell 1133). By this I mean that LAION is not just made up of raw data collected from the wild. Instead, a series of human decisions based on cultural and technical rationales determine what data gets used, how and why. For this reason, models like SD don’t generate new media just out of raw data but are deeply informed by decisions underlying the collection of data, human interpretation of this data and the aims they want to achieve with it. The bias is already in the human process of capturing the world as information in what could be called the ‘capta’ (Drucker 2).

Looking closer at other categories of the dataset the column “aesthetic” stands out. “Aesthetic” denotes the aesthetic score attributed to each image on a sliding scale of zero to ten to measure its aesthetic quality. This quality score is a prediction of the image’s appeal to a human viewer and the scores categorize images in LAION from poor to good quality. The images are distributed in ‘buckets’, that is groupings of images by score. The lowest buckets are poor resolution images with watermarks for example and those which supposedly contain potentially harmful content, whilst the high-quality images are in the buckets 8 to 10. Thus, images are not only described in terms of their content: apples. They are also rated: this image of apple scores 8.18764114379828. This is very interesting at two levels: how are images evaluated for their aesthetic quality? And why are they evaluated? It is to these questions I turn attention to in the next section.

Figure 4: Image of Apples in LAION Aesthetic. Described as ‘apples’. Aesthetic Score 8.18764114379828. Source: Bird Feeder Expert website, https://birdfeederexpert.com/wp-content/uploads/2020/12/orchard-1872997_640.jpg

Image aesthetic assessment: virtual viewers and platform capitalism  

Just as they are analysed for perceived harmful content (their safety score), images in LAION are allocated an aesthetic score automatically using a modified version of the CLIP model called an aesthetic predictor (Schuhmann & Beaumont).    

Two datasets were used in CLIP aesthetic predictor’s pre-training, namely the Simulacra-Aesthetic Captioning (SAC) and the Aesthetic Visual Dataset (AVA). Both datasets are considered benchmarks in machine vision research. Both SAC and AVA contain photographs scored by humans either on online photo competition platforms or by research participants in academic studies. SAC contains ratings for 230 000 AI-generated images (Pressman), whilst AVA contains 250 000 images with ratings and comments collected from the photo-challenge platform DP.Challenge (Murray et al.). Both SAC and AVA were used in training the aesthetic predictor for LAION and thus inform the production of SD’s memory and its parameters for evaluating appeal. But how is this appeal defined and determined? Especially since appeal appears at first as a subjective phenomenon.

Underlying the automation of aesthetic rating, lies the scientific effort to theorise and measure the impact of images on humans or elucidate the qualities that make an image appealing. The field of Image Aesthetic Assessment (IAA) takes up this question, with research being undertaken in neurosciences, cognitive psychology, computing and marketing research (Bodini 5). The aim of IAA is to determine what makes an image beautiful and to produce experimental apparatuses, including computational simulations, to support these theories. Surveys of the literature show significant previous efforts to develop an automated assessor based on the formal analysis of artworks. This means hand-crafting arbitrary lists of positive visual qualities, such as composition, subject, rule of thirds, colour combinations, contrast and more (Deng, Loy and Tang, 6). This approach however has lost in popularity since the appearance of deep neural networks that utilize large amounts of data and bypass the need for hand-selected features. The idea that there are a-priori formal qualities underlying the aesthetic appeal of images could be called an objectivist approach to the study of aesthetics (Bodini 4), because it centres the object’s qualities as the source of subjective aesthetic experiences. In contrast, current deep learning models aim to mimic the behaviour of human test-groups without prior knowledge of formal qualities that make a visual object appealing. In this subjectivist approach, the impact of an image on its observer takes precedence over formal qualities. In general, this approach aims to measure the impact of individual images on a scale from zero to ten on thousands of human test subjects to produce average scores on a large body of images (Folgerø 19). A deep neural network is then trained to start recognizing patterns of pixels that tend to be associated with high human scores. These minute pixel patterns that are invisible to the human eye are the building blocks from which the machine perceives appeal, a process that seems completely opposite to human ways of perceiving and receiving images as totalities, rather than minute details. In this paradigm, appeal is not a tangible objective quality but a statistical trend that ‘emerges’ from the aggregate of human ratings collected ‘in the wild’. Echoing Chris Anderson’s controversial 2008 claim that big data marks the end of theory in scientific research, current IAA paradigms assert that with enough data, numbers can not only explain but also make aesthetic judgements.  

CLIP Aesthetic is built on this subjectivist approach because it has taken most of its scores from online photography websites. One of them, DP.Challenge, is an amateur photography competition run by the Digital Photography Review since 2002. Users are invited to organise thematic competitions and to upload images, which are then anonymously scored and commented. This is the data used for CLIP, but other researchers have proposed to use Reddit’s r/photography discussion thread (Nieto et al) or Flickr comments (Soydaner et al) as alternative data sources to train IAA deep learning models. The scores are averaged, but also sometimes require correcting as simple averages tend to neglect sentiment polarity. Polarity, the presence of both very high and low ratings simultaneously, defines an image as ‘divisive’ because the consensus on its score is considered less reliable. These polarizing images are often removed from the training set because images that have wider appeal are considered ‘truer’ examples of aesthetic attractiveness since they gather unanimous agreement amongst scorers (Park and Zhang). In a sense CLIP aesthetic predictor is a simulation of user behaviour on platforms like DP.Challenge (​​as well as the university student test group of AVA, which should be discussed in a separate paper). The aesthetic predictor’s aim is to predict reliably the appeal of images, and thus the middle of the road, or ‘mean’ aesthetic gets prioritised over images that may be divisive or appear unconventional to users. Underlying these aims is to make the model as popular and appealing as possible to a wide user- and consumer-base.  

This notion of unreliability is important to discuss the differing value systems that guide SD and museums when they utilize digital images of art. In the training pipeline, the attribution of aesthetic scores dispels the idea of text-image data as something “already” in the data. It is a capta in the sense that the choices underlying the selection of the data, the source of the data and the truth-value assigned to this data are processes of capturing and socially interpreting information within an epistemic and technical framework. In this situation, the aim to produce appealing images with SD means that a way of formalising aesthetic appeal becomes a technical pre-requisite for the synthesis of new images. Images from museum collections are inputs for machine learning and the cognitive processes of human viewers of art also become conceived as inputs. The formalization of appeal requires its definition and in the case of CLIP-aesthetic, appeal is defined as the statistical frequency of pixel patterns unconsciously liked by online photo communities. The user ratings from DP.Challenge then constitute a ‘ground truth’ of aesthetic appeal (Sluis) in the development of image generators like SD. These ratings are representative of a generalizable human cognitive response to images and can be inferred to make guesses about the appeal of future images. This process is automated in CLIP and finally defines the aesthetics and visual look of images produced by SD. Generative AI then not only translates a further datafication and commodification of images of art, but a datafication of photo-competition participants’ cognitive labour when they produce scores and feedback about photographs. In this sense, these systems do not only treat images from national art collections as a means to an end, but they also objectify human interactions with these images, they objectify aesthetics.

Analysed from a visual cultures or aesthetic theory perspective, this approach seems to have several problems. The inductive nature of the reasoning behind the operationalization of this subjectivist approach is problematic because the ratings of amateur hobbyist photographers from North America are confounded with a universalizable notion of aesthetic taste (Sluis and Palmer). This generalization highlights a Western photographic unconsciousness in generative AI but also points to the strong geographic and cultural contingency of the aesthetics promoted by DP.Challenge. This also applies to CLIP aesthetic as it was trained on the same data. It could be said then, that LAION-5B is organised by an automated ‘virtual viewer’ of these images, a sort of ‘virtual connoisseur’ with median aesthetic taste, engineered by a mixture of cognitive psychology, neuroaesthetics, statistics and computing. The aesthetic predictor is a form of automata, performing human-like labour of indexing and tagging but its logic seems alien to the affective charge of human reception and interpretation of images. It is built on data about human behaviour, but the predictor’s behaviour is very different from the subjective human experience of the world, which seems to change at every iteration, particularly in the case of art. Art is characterized by the difficulty to agree on its definition, but also by a polyvocality in the experience of individual artworks. To just quote a historical example used by Helliwell to discuss value alignment in AGI, the work of Vincent Van Gogh, who was one of the early artists to be used to showcase the power of style-transfer (Gatys et al 5), was derided during his lifetime. His work got positive recognition only decades after his death and points to the fact that the appreciation of artistic styles changes over time and is not a fixed quality, which can be captured at one point and reproduced indefinitely. Theoretically this means then that SD’s logic would prohibit the spontaneous emergence of new visual aesthetics that do not conform to existing tastes and preferences. The algorithms then appear as deeply conservative. A reality that stands at odds with the recurrent discourse of progress, democracy and futurity invoked by the developers of these systems.  

Conclusion: From Images to Attention Economy

But why is generative image-making the object of so much economic investment, artistic controversy and popular mass adoption? I argue that the techniques behind image generation actually build upon and reinforce the commodification of the online space. It frames users as customers and any data as resources to be extracted and monetized. The subject being automated in generative AI’s virtual viewer reflects this atomized subject of digital platform economies. Online users are being atomized because the platforms on which they build their online existence aim to increasingly isolate them from each other, whilst also extracting as much capital from them in the form of service income or the data they produce (Bridle, 91). This atomization is further reinforced by these platforms’ reliance, from social media to generative platform, on the commodification, capture and retention of the users’ time and attention. This race to capture attention lies behind early investment in IAA research  so companies could better understand how online consumers act. Internet users are simultaneously interpellated ² as consumers by algorithmic recommendations within a wider digital marketplace for the provision of goods and services (Terranova 2; Hentschel, Kobs and Hotho 2; Baeza-Yates and Fayyad 132). The political economy of attention in communicative media discussed by Nixon ties the epistemology and techniques of aesthetic appeal in generative AI. It also ties to the wider exploitation of data produced by image-makers, artists, museums and online commentators worldwide. This process of attention capture, extraction and image generation perpetuate data colonialism’s framing of digital networked images as “an ‘open’ resource for extraction that is somehow ‘just there’ for capital” (Couldry and Mejias, 337). Previous logics of platform companies such as search engines and social media extend the creation of new consumer needs in the form of mercantile image-generation services online. By extracting digital images, companies selling generative AI services have effectively privatized the internet commons. This lies at the source of controversies with artist lawsuits, scriptwriter strikes and cross-sector concern about the future of creative economies. This process of data pillaging, data colonization and privatization runs parallel and within the scientific project to measure, define and quantify human psychological and cognitive processes to predict the appeal of images, messages and information, both analogue and now synthetic. These techniques of observation, and now generation, continue to inform a symbiotic relationship between emerging modes of visual culture, scientific study of human cognition and emerging modes of economic exploitation (Crary).

This subject atomisation in the digital sphere and reality driven by the aesthetic predictor, the visuality of SD or the marketplace of consumer-oriented platforms stands at odds with the cultural values that are usually associated with artistic heritage and its digital images. Leaving aside the ways in which generative AI models decontextualize all images in their datasets to exploit their representativeness and reduce them to textual descriptors and aesthetic scores, the political economy of these models tends to disenfranchise artists and break the symbolic function of images as a site where meaning, identity and histories are collectively negotiated, preserved or relinquished. As pointed out by Steyerl, the formation of a common-sense of aesthetics relies on the messy, asynchronous and sometimes unresolved reception of images, whose attraction may endure even if their appeal is polarizing (Medium Hot, 51). This raises questions about the possibility of alignment of museum missions with the emerging visuality of generative AI.  

Despite their creators’ aspiration to ‘democratize high-resolution image synthesis’ (Rombach et al. 1), the inherent political economy of the aesthetics of systems like SD appear more to alienate than to strengthen social bonds and promote creativity. LAION’s virtual viewer is not fully the same viewer as the human in the gallery space or the museum website. The instability and free-play associated with aesthetics gets reduced to rational choice-making subjects modelled according to contemporary market logics in aesthetic predictors. Not that market logic is exclusive to the machine, it also animates the values behind museum entrepreneurialism, although these values are constantly negotiated and problematized within the linear progressive values of the institution issued from representational artistic modernity (Dewdney, 5).  

By standing at the crossroads of new forms of economic exploitation and emerging forms of human image making, generative AI problematizes what it means to look at images, where we look at them and what infrastructures facilitate these modes and techniques of vision. It also raises questions regarding art, images and private property. The process of privatizing data issued from the internet commons reframes all data as a resource to extract and own, thus determining who gets to monetize it, when and how (Bailkin, 14). A similar paradigm of property ownership is characteristic of the way in which museum collections operate, the exhibition being a format that conditions what works can be seen, how and when. But museum websites have gradually disrupted this property paradigm. For instance they have aspired to open the collection’s stores and promote the idea of artwork stewardship: namely that artworks in collections are not mere possessions but rather common goods that need to be managed for the good of all (Cheng-Davies, 290). Common goods understood as that which benefits the community of users, be it in the promotion of social cohesion, provision of education, improvement of mental health or some other projected value of the artwork. Whilst the common good can be seen to animate museum activities when they release digitized collection data online and partake in public programming, it is less evident in the way that commercial generative AI platforms use images issued from national collections. The opacity of platforms like Midjourney or Dall-E that hides behind convenient user-interfaces, reinforces barriers to a wider understanding of how these systems work and the economic processes that make them possible. This then poses two problems for the humanities and museum institutions: how can cultural institutions reassert a common capacity of anyone to understand and tinker with these systems? What mechanisms or imaginaries need to be formulated, and by whom, to redistribute the benefits of these generative technologies for a common good: creatively, societally, financially?

The task at hand is then to develop strategies, curatorially, organisationally or infrastructurally that promote the reappropriation of data heritage, digital commons and a social ownership of the means of prediction. As a site of heritage, with buildings, expertise, objects and archives, the museum has the affordance to bring strangers together and maybe turn them into neighbours and community-members (Balshaw). This coming together is essential, not only to share grievances but also a common capacity to deliberate what the common good looks like in a specific situation, place and time. Even if the museum is defined by conflicting values including strong market forces, it also values criticality as a mode of culturally engaging with their histories and collections. This means there are affordances in the current value-system and infrastructure of the museum to engage in this conversation about re-commoning data heritage. As argued at the beginning of this article, the starting point needs to be an understanding of the technology from the perspective of those affected by it: institutions, humans, communities and cultures. As Katz writes: “Explanation of rules is a prerequisite for the democratic control of rules”. (22)​ ​ What this democracy may look like in the museum remains to be imagined and opens a line of research into the techno-aesthetics of generative AI models, not only to inform new museum activities but maybe assert the right and necessity of cultural workers to have a say in the ideas and applications of these fast-moving techniques. It is thus not only the rules guiding the algorithms that need to be explained but also the rules of the emerging political economy of corporations and digital platforms powered and guided by AI that need to be elucidated within visual culture.  

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Biography

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Choreographing Proximity: Choreographic Tools For Exploring Intimacy In Digital Platforms

Daria Iuriichuk

Abstract

This article explores how choreography can serve as a critical framework for analysing and intervening in the affective economies of digital platforms. Building on André Lepecki’s notion of choreography as a “technique designed to capture actions,” it is examined as a medium that abstracts movement into data, enabling further technical or creative processes. Drawing on theories from dance studies, media theory, and affect theory, this article examines choreography’s capacity to expose, modulate, and reconfigure proximity and distance. It explores how affect, gaze, and movement are governed, simulated, and potentially subverted within platform cultures. The argument is grounded in case studies ranging from Mette Ingvartsen’s performance 50/50 to Candela Capitán’s SOLAS. These examples illuminate how bodies and affects are choreographed not only on stage but within digital architectures, offering tools to think against the commodification of intimacy.

Introduction

Imagine scrolling through your Instagram feed and stumbling upon a girl: her face fills the screen, she seems to be impossibly close. She holds your gaze, maintaining eye contact as if she sees you. Her smile is disarming. You notice her cute cheek dimples and feel hypnotised. It draws you in and makes you feel seen, as if this gaze is meant only for you. She creates a sense of presence that is almost uncomfortably intimate, leveraging the illusion of proximity to connect with her thousands of followers. On platforms like Instagram or OnlyFans, the production of proximity becomes a conspicuous tool for creating intimacy, often blurring boundaries between public performance and private connection.

Emerging technologies are multiplying the ways in which proximity is produced, often by simulating emotional presence and connection. Services for video conferences, such as NVIDIA Maxine, offer real-time gaze correction and facial expression adjustments to create a sense of attentiveness. Deepfake tools like DeepFaceLab and Wav2Lip generate hyperrealistic facial expressions and precise lip-syncing, making pre-recorded or altered content appear convincingly authentic. Most recently, video generation models such as OpenAI’s Sora can produce lifelike gestures, facial cues, and subtle emotional inflexions, further blurring the line between scripted performance and spontaneous, affect-rich interaction.

Intimacy is not just present on the Web — it is thoroughly constructed through strategic self-presentation, continuous engagement, and the creation of affective bonds that simulate closeness. It becomes particularly evident in affective platforms with erotic content, where proximity is not just simulated but commodified. For instance, an OnlyFans content creator may establish a sense of intimacy by creating a digital morning-after scene to evoke a sense of proximity, ultimately directing the viewer towards engagement with monetised content. However, as Kaufman, Gesselman and Bennett-Brown observe in their analysis of cam sites, clients often experience this affective labour as ‘real’ (2). This closeness is perceived by viewers as “authentic,” even though it is produced through a specific choreography of affect, gesture, and gaze that aligns with platform economies.

The production of proximity has been increasingly instrumentalised not only for commodification but also for the circulation of reactionary political affects. With the rise of AI-driven technologies, affective interfaces now simulate intimacy with growing precision, intensifying the automation of attention and further entrenching users within ideologically charged affective economies.

In The Digital Subject: People as Data as Persons, Olga Goriunova coins the term digital subject to describe new forms of subject construction constituted through data, including social media profiles, browsing history, and mobile phone positioning records, as well as biometric and facial recognition inputs. This concept captures the entanglement of biological characteristics, legal frameworks and performed identities. In the context of digital intimacy, shaped both by bloggers and by technologies such as gaze correction, face tracking, deepfakes, and AI-generated videos, the digital subject is formed using data abstracted from the body, including eye movements, smiles, voice, and posture. These emotional gestures are transformed into patterns that can be manipulated, animated, and replayed. According to Goriunova, the idea of distance is central to understanding the digital subject, as it possesses ontological instability, occupying neither the space of lived human experience nor its representation but exists as a distance between the two (5). In her article, Goriunova also warns against assuming an equivalence between “digital subjects and the humans, entities, and processes they are connected to” (5). She argues that distance becomes an urgent political issue when digital subjects are “constructed not only to sell products but also to imprison, medically treat, or discriminate against individuals” (7).

To respond to Goriunova’s political call to confront the erasure of distance, I propose to explore the distance and the production of proximity through the seemingly marginal yet conceptually rich lens of choreography. Here, choreography is understood not merely as dance movement but as a conceptual tradition that engages with the creative and critical potentialities of algorithmic thinking. Building on André Lepecki’s notion of choreography as a ‘technique designed to capture actions’ (Lepecki “Choreography and Pornography”), I examine it as a medium that abstracts movement into data. Viewing choreography as a framework for the production of proximity prompts us to consider how algorithmic structures are embodied and practised, echoing Andrew Hewitt's concept of choreography as embodied ideologies, which are ways in which social order is enacted physically (Hewitt 11). Through this lens, I explore how choreographic thinking might offer not only tools for critical engaging with the mechanisms of proximity production, so central to platform culture, but also strategies for repurposing them, enabling the digital body to become something more than a local embodiment of ideology (Massumi 3).

In this text, I will focus on two perspectives on choreography: first, as a historical technology for representing societal hierarchies by managing affects, distance, and proximity through steps, posture, and collective movement patterns; and second, as a set of strategies developed in contemporary dance to address the abstraction of movement into data, to reframe the choreographic score, and to critically engage with affect. In addressing the concept of affect, I follow the tradition of affect theory articulated by Deleuze and Guattari and developed further by Brian Massumi, as well as its elaboration within choreographic discourse by Bojana Cvejić.

Choreography As An Approach

Long before algorithms learned to track our eye contact or simulate our smiles, there was already a technology for scripting bodies — choreography — organising limbs, timing gestures, and composing presence in highly coded ways. Flourished as a Louis XIV court practice of political control, choreography, a tool of writing down movement, could also be observed as a ‘technique designed to capture actions’ (Lepecki, “Choreography and Pornography”), a medium that abstracts movement into data, enabling further technical or creative processes. By abstracting bodily movement into data, choreography transforms it into systems of control and knowledge production, shaping behaviour by training bodies to perform socially acceptable identities.

In one of the earliest dance manuals, Orchésographie: A Treatise in the Form of a Dialogue. Whereby All May Easily Learn and Practice the Honourable Exercise of Dancing (1589), Thoinot Arbeau introduces an orchésographie (where orchésis - dance) as a written form of dance knowledge transmission. The manual unfolds as a dialogue between a young lawyer, Capriol, and Arbeau himself, offering detailed descriptions of 16th-century and earlier dance forms. Through this textual format, dance is transformed into codified knowledge. The written score abstracts movement from the living body, creating a distance between embodied performance and its data-like representation.

As André Lepecki argues, with the invention of its written form, dance possesses a spectral dimension: by being written down as choreography, it becomes a medium that conjures the presence of an absent dance master (Lepecki, “Exhausting Dance” 28). In this sense, the choreographic score does not just preserve movement—it animates bodies across time, allowing historical authority and disciplinary regimes to speak through the dancer. ‘In Orchesographie, a young lawyer returns from Paris to Langres to visit his old master of “computation (...) Capriol asks for dance lessons to attain what Erving Goffman called a socially acceptable “performance of the self” – a performance that would give the young lawyer admission into social theatrics, into society’s normative heterosexual dancing’ (25). During the Baroque era, choreography evolved further, functioning as a tool of propaganda (Maravall). By codifying steps, postures, and sequences, dance emphasised symmetry and control, aligning the disciplined body with a higher spiritual or intellectual order. As Susan McClary and Robert Isherwood stressed, Louis XIV used dance as a source of political control “to regulate — and even synchronise — the bodies and behaviours of his courtiers” (McClary 89).

Similarly, digital data is aggregated today to mobilise bodies within a fluid logic of surveillance capitalism. In this sense, choreography and algorithms both function as technologies of subject formation, conditioning our behaviours and interactions in increasingly automated ways.

Lepecki’s idea that choreography “socializes with the spectral” helps us think through how the digital subject is haunted by the idea of presence, even when the body is absent, the subject must appear available, coherent, and even emotionally attuned. Through this lens, we can think of algorithmic media as staging choreographies of presence—Zoom backgrounds, auto-eye contact tools, and real-time filters all simulate spontaneity and emotional availability, much like how baroque dancers rehearsed “natural” grace.

Dance Strategies

By the 20th century, modern and later contemporary dance sought to liberate movement and the body from the codifying constraints of choreography understood as a technology that produces societal hierarchies by regulating affect, distance, and proximity through steps, posture, and collective movement patterns. From Isadora Duncan’s embrace of free movement to postmodern dance’s valorization of improvisation, choreographers have historically resisted the rigid legacy of court dance and ballet by privileging spontaneous self-expression and embodied freedom. In problematizing the very notion of choreography, they developed diverse strategies for subverting established structures, often creating new modes of emotional connection with the audience. These strategies offer some insights into the production of proximity and its affective charge, making them particularly relevant in the context of today’s digitally mediated cultures.

In Choreographing Problems, Bojana Cvejić outlines a compelling genealogy of how dance has theorised sensation, emotion, and affect, from the emotionalism of the modern dance tradition, where performing and perceiving movement are inherently tied to emotional expression and kinesthetic empathy, to more critical and experimental engagements with affect in contemporary choreographic practices. The idea of the movement as an emotional act of expression of true self, one that binds the spectator to the performance through empathy, was central to the work of iconic choreographer Martha Graham and her critic and advocate John Martin. Their ideas later informed the practice of the Authentic Movement, which treated movement as the expression of an inner life. As Cvejić notes, in this tradition, emotional proximity between performer and audience was thought to emerge from “an emotional experience of one’s own body and its freedom of movement, a value dance was believed to hold for its viewers” (162). However, postmodern dance explicitly broke with this conception, seeking to dissociate choreography from dance by disrupting what Cvejić calls “the onto-historically foundational bind between the body and movement” (17). Here, movement is no longer the natural expression of interiority, but an object in its own right.

In the clash between two ideas about movement — the one is that movement is an expression of the true self, and the other is that movement is not a reflection of interiority but its own thing, a new approach has emerged. In her performance 50/50, a Danish contemporary choreographer, Mette Ingvardsen investigates the composition of affect, positing the question of whether affect can be deliberately constructed and artistically produced. In 50/50, she works with an interplay of movement and sound borrowed from semiotically distinct expressive forms and clashes them into a specific affective object. Thus, in one of the scenes, Ingvardsen rhythmically moves her buttocks mirroring a drumroll with extreme precision, to create the illusion that the drummer is playing directly on her body. As the rhythm accelerates, the movement becomes a visceral vibration, and the pulsating body dissolves the distinction between stimulus and response. Motion and sound appear to merge, or even reverse roles. This synesthetic fusion intensifies the experience: sound is visually amplified, and movement becomes aurally charged. Ingvardsen’s experiments with affect in 50/50 parallel Brian Massumi’s analysis of Ronald Reagan, who, as Massumi argues, generated ideological effects through non-ideological, but affective means. In both cases, affect is not tied to explicit content but operates through a kind of abstractive suspense — multiple sensorial or expressive registers resonating in parallel to produce an intensity that exceeds rational articulation (Massumi 41).

In her reading of Ingvartsen’s performance, Cvejić approaches affects as “synesthetic events that exist autonomously, neither only in the body of the performer, nor only in the perception of the attender” (194). Drawing on genealogy from Spinoza to Deleuze and Guattari, Cvejić conceptualises affect as impersonal — detached from the subject’s interiority (168). She also shows affects can be composed by choreographing sensorial materials and appropriated styles of performance (rock concert, opera, pantomime). For the analysis of the production of proximity, I find Bojana Cvejić’s argument for a constructivist composition of affect particularly fruitful. It offers a valuable lens for speculating on the affective techniques employed by platforms. This approach allows us to interrogate how affect is composed and how bodies, movement and choreography become integral to this construction.  

In Mette Ingvardsen’s work, movement is treated not as a vehicle for personal expression; but rather as a system of discrete units – gestures, postures, rhythms – that can be abstracted, recomposed, algorithmicised and choreographed to generate affect. This resembles the logics of services for video conferencing, deepfake tools, and AI video generation technologies, in which gestures, facial expressions, and vocal inflections — are broken down into measurable variables, recombining them to create realistic simulations of proximity. Crucially, however, Ingvardsen’s choreography does not replicate this logic in order to reinforce ideological capture; instead, it seeks to expose and reconfigure the affective mechanisms underlying such processes. By rendering the dynamics of distance and proximity manipulable and visible, such practices of choreographing affect might serve as a framework for critically examining how platforms shape attention, behavior, and embodied interaction. Through abstraction, recomposition, and the deliberate misuse of platform grammars, these choreographic strategies open space for friction, distance, and critical reflection—providing potential counter-strategies within systems designed for affective capture and behavioral control.

While Ingvardsen demonstrates how choreographic strategies can be used to critically and creatively compose affect—offering potential applications for platform cultures—another example from contemporary dance, Candela Capitán’s SOLAS, engages more directly with the notion of distance, particularly as articulated by Goriunova in relation to digital intimacy.

In SOLAS digital intimacy production techniques are explored from a detached, bird's-eye perspective. On stage, five performers in tight pink suits each perform an erotic solo in front of their laptops, evoking the setup of webcam models. Simultaneously, the solos are broadcast live to an audience via the Chaturbate platform. Capitán reveals the gap between the digital subject and the labour that sustains it, making this distance strikingly palpable. By exposing the fractured connections and isolating conditions of digital performance, SOLAS lays bare the mechanisms through which intimacy is manufactured, commodified, and consumed in virtual spaces. Candela’s critical gesture is achieved by revealing living bodies behind digital subjects. By foregrounding the performers’ corporeal presence, it insists on the presence of the body as essential for critique in the age of algorithmic mediation.

The performance also invites us to speculate on choreographic interventions within digital platforms. What kinds of artistic strategies might be developed as online practices to reconfigure the digital body so that it becomes more than an embodiment of ideology? How might proximity, attention, and affect be repurposed as aesthetic and political tools for critical engagement and disruption within the platforms?

Thus, choreography becomes not merely a metaphor but a critical method for analysing digital intimacy and the affective architecture of platforms. It can function as a critical lens, a performative practice, and a tactical intervention within platforms and outside them. This choreographic perspective allows us to critically examine the mechanics of digital intimacy and mediated presence while also opening space to imagine interventions into platform architectures themselves.

Works Cited

Arbeau, Thoinot. Orchesography: A Treatise in the Form of a Dialogue Whereby All Manner of Persons May Easily Acquire and Practise the Honourable Exercise of Dancing 1519-1595. Dance Horizons, 1966.

Cvejić, Bojana. Choreographing Problems: Expressive Concepts in European Contemporary Dance and Performance. Palgrave Macmillan, 2015.

Goriunova, Olga. "The Digital Subject: People as Data as Persons." Theory, Culture & Society, vol. 36, no. 6, 2019, pp. 125–145.

Hewitt, Andrew. Social Choreography: Ideology as Performance in Dance and Everyday Movement. Duke University Press, 2005.

Kaufman, Ellen M., Amanda N. Gesselman, and Margaret Bennett-Brown. "Clients’ Perceptions of Authentic Intimate Connection on Erotic Webcam Modeling Sites." The Journal of Sex Research, Aug. 2024, pp. 1–11. https://doi.org/10.1080/00224499.2024.2389211.

Lepecki, André. "Choreography and Pornography." Post-Dance, edited by Danjel Andersson, Mette Edvardsen, and Mårten Spångberg, MDT, 2017, pp. 67–83.

---. Exhausting Dance: Performance and the Politics of Movement. Routledge, 2006.

Maravall, José Antonio. Culture of the Baroque: Analysis of a Historical Structure. Translated by Terry Cochran, University of Minnesota Press, 1986.

Massumi, Brian. Parables for the Virtual: Movement, Affect, Sensation. Duke University Press, 2002.

McClary, Susan. "From the Royal to the Republican Body: Incorporating the Political in Seventeenth- and Eighteenth-Century France." From the Royal to the Republican Body, edited by Sara E. Melzer and Kathryn Norberg, University of California Press, 2023.

Other Sources

Capitán, Candela. SOLAS. 2024. Performance.

Ingvartsen, Mette. 50/50. 2004. Performance.

Biography

Daria Iuriichuk is a dance artist, researcher, and educator based in Berlin. Her work explores the political dimensions of performativity, infrastructural critique, and body politics. In her most recent research and artistic projects, she explores choreography as a medium that abstracts movement into data, enabling further technical or creative processes. Her works have been presented at the MyWildFlag festival (Stockholm), neue Gesellschaft für bildende Kunst (nGbK, Berlin), Hellerau Europäisches Zentrum der Künste (Dresden), H0 Institut für Metamorphose festival (Zürich), and the Meyerhold Theatre Center (Moscow). She is also a co-founder of Girls in Scores, a collaborative project with Polina Fenko, focused on artistic research at the intersection of media studies and expanded choreography.

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Induction of Sonic Distance: Active Noise Cancelling Headphones and the Imposition of Sonic Realities

Nico Daleman

Abstract

This text presents a series of theoretical examinations of concepts such as induction, noise and space as they pertain to a broader ongoing artistic research project entitled Noise Re(in)duction that explores the possibilities of noise reduction technologies as sonic material for artistic practice. The central argument of the project is that by artificially reducing acoustic noise and digitally cleansing sonic environments, Active Noise Cancelling (ANC) algorithms induce a different kind of noise into our perception of reality. This paper further explores this notion by arguing that the induced noise is manifested as a parallel sonic reality (a sonic distance) which, although sensible, is contingent to the biases embedded in the algorithm. Thus, the broader implications of the conceptualization of noise, distance, sound and reality itself are negotiated through noise reduction technologies and the induction of a sonic distance. These theoretical frameworks therefore seek to establish a solid foundation for an artistic and phenomenological exploration of the nuances found in contemporary audio technologies.  

Introduction

The increasing prevalence of Active Noise Cancelling technologies in our everyday life (from headphones to smartphone devices, smart speakers, virtual assistants, hearing aids and hearables, among many others) has a direct impact in the ways in which perceive and relate to our sonic surroundings. By actively processing, modifying, and (re)producing acoustic environments, these technologies create an alternative sonic reality that is presented as actual, but that is in fact a representation of a soundscape that further alienates the listening subject from an unmediated sensory experience. In the pursuit for an optimal signal transmission, the advancement of noise-cancelling technologies has paradoxically led to the emergence of other forms of noise that extend beyond the boundaries of its intended cybernetic and informational system. Despite being grounded on the wave physics’ fundamental principle of destructive interference, the implementation of the relevant noise-cancelling algorithms within the digital realm remains opaque and complex, which is also further exacerbated by the dynamics of patent acquisition and competitive market forces. As it is the case with many other computational processes that are a part of our everyday lives, there is an element of faith in the accuracy and representational capabilities of these devices. However, how much can we really trust our perception, when the perception itself gets mediated? How do digital algorithms mediate the impositions of perception? And how could they reinforce and induce preconceived social biases of race, gender, ability, and class into the perception of soundscapes?  

This paper sets a theoretical ground for understanding the ways in which ANC technologies induce a form of noise that is manifested in a sonic distance which is also contingent to the biases of predeterminate algorithms. That is, in order to understand the broader consequences of ANC technologies in our interconnected and hypermediated social dynamics, the concepts of induction, noise and space are in need of a re(definition).  

The text first briefly describes the phenomenon of electromagnetic induction, as related to the functioning of audio technologies such as microphones and speakers, to then explain it in action on ANC technologies, including the different modalities that can be found in some of today’s most common devices such as Sony’s WH-100XM5 and the Apple’s Airpods.  

Theoretically, induction makes part of Gilbert Simondon’s ontological framework of individuation, where transduction (different than induction or deduction) becomes the main process by which beings emerge (Simondon). In the understanding of the act of listening as a transductive process, ANC them becomes an affront and a hinderance to the nuances that allow this form of sonic individuation. Instead of allowing a transductive process ANC algorithms induce a different kind of noise into our perception of reality. Noise is therefore not only understood in relation to its acoustic and technical modalities, but also in its social, cultural, and aesthetic dimensions. Supported on Olga Gurionova’s and Henri Lefebvre’s discussions of distance and space respectively, I argue that, in the case of ANC, noise is manifested in the form of a sonic distance which affects the perceptual relation of sonic spaces, understood in its broader spectral, architectural and personal dimension.  

Following Mack Hagood's examination of contemporary soothing sound technologies, including ANC, I ultimately offer a perspective in which the transductive quality of the act of listening is truncated by the algorithm, thus inducing noise that manifests itself in sonic alienation and distance. Sonic distance is then presented not only as an act of acoustic isolation, but also as a technological affront to our sonic reality. This is archived through the deliberate prioritization of the cybernetic conceptualization of noise as an objective entity and the subsequent possibility of control and personalization that restructures social dynamics.  

Ultimately, I present noise reduction algorithms not as an invisible tool to improve a prescriptive listening practice imposed by tech corporations, but rather as a visible and tangible tool that can be challenged through its reorientation as musical and artistic material, as referenced in my ongoing artistic research project “Noise Re(in)duction.”  

Active Noise Cancelling (ANC)

The fundamental underlying principle of noise-cancelling technologies is grounded on the phenomenon of destructive interference, which occurs when a signal is summed with a phase-inverted copy of itself. In its most basic form, ANC Headphones function by capturing environmental sound through a few tiny microphones, inverting its phase and summing it to the desired signal (e.g. music or speech). Most contemporary devices use a combination of microphones both outside and inside the headset, creating system of feedforward and feedback. The exterior microphones are responsible for capturing ambience noise, while the inside microphones capture the desired signal and the inverted ambient noise (see Figure 1).

Figure 1. Sony’s ANC technology for Audio. https://www.sony-semicon.com/en/technology/audio/index.html  

The digital processing signal differs from traditional methods of damping noise (such as headphones or earplugs for noise protection), in that not only they aim to block directly the physical passage of sound to the timpani, but also in the lack of control on the amount of noise or signal that is damped. Conversely, the advent of more efficient and compact technology has facilitated the integration of computationally intensive spectral processing algorithms into consumer-grade devices. Digital signal processing enables a precise selection of the spectral characteristics of noise by applying methods such as least mean square (LMS) adaptive filters, noise profiling, spectral analysis and Wiener filtering, each of which is more or less effective against different manifestations of noise. For example, noise profiling is primarily employed for cancelling predictable and repetitive noise, such as that generated by aircraft turbines or heavy machinery. Adaptative filters are frequently used for more unpredictable and sporadic noise, such as street noise or by-passer conversations. Contemporary solutions that rely on artificial intelligence models are often trained by gathering data on the user’s surroundings and habitus. This creates a "scene" that anticipates the context of noise in specific environments. For example, the system can expect to hear dogs and babies in parks, telephones or doorbells in offices, and announcements in airports.

ANC headphones can create accurate acoustic profiles of both the spatial soundscape as well as personal hearing profiles for bodies though a simple impulse-response process, which usually happens when turning the headphones on, by playing an initialization sound. Furthermore, ANC headphones offer a variety of functions and features, including personalized equalizer options, Spatial Sound Optimization for consumer-based spatial sound formats such as Dolby Atmos, based on data gathered from gyroscopes and accelerometers included in the devices circuitry, and the creation and selection of different quotidian soundscape profiles (e.g. home, office, jogging, gym, etc.). ANC headphones often provide the possibility of additional experiences through personalization through an accompanying mobile phone app. This includes Active Noise Awareness, voice interactions, and virtual assistance. The user interface of the app becomes a sonic interface thought the use of headphones.  

A compelling feature of contemporary ANC headphones is the implementation of a so-called Transparency Mode (in Apple’s Airpods) or Ambient Sound Feature (in Sony’s WH-100XM5). Unlike traditional noise cancellation, which aims to suppress environmental audio in order to create an optimal acoustic environment for the transmission of a signal, transparency mode actively captures and enhances certain external sounds, allowing the users to remain aware of their surroundings without removing the devices form their ears. The implementation of a transparency mode depends on a complex process of digital signals in which the physical isolation provided by the headphones must be compensated for by the (re)creation of ambient sounds, ensuring that the user perceives these sounds "as if" they were not wearing the headphones. Transparency mode is one of the main features that differentiates current ACN headphones from previous models. It is, therefore, one of the main features marketed by Apple, Sony and many other developers. While previous models marketed the access to a private sonic space, where silence and tranquillity was the norm, current models are directed towards the possibility of a sonic ubiquity, where the individual could switch at will between different sonic profiles.

The experience of listening though noise cancelling algorithms then varies from device to device. However, in both pure noise cancelling and transparency mode, there is a double process of transduction in real time, from acoustic to digital and back to acoustic, which aims to make the device that produces the medium invisible. To explain the intricacies of the ANC and its consequences on the listening experience, a discussion of some basic concepts that permeate the functioning of audio technology is needed.  

Induction

Electromagnetic induction is a phenomenon through which an electric voltage is generated by a changing magnetic field. This is the basic underlying principle in the functioning of microphones and speakers. In dynamic microphones, variations in sound pressure move a coil around a magnetic field, inducing an electrical voltage which is then transmitted as an audio signal. In dynamic speakers, variations in electrical voltage on a coil induce a magnetic field which results in the movement of an attached diaphragm, producing changes in air pressure. i.e. sound. These two inverse processes are indeed manifestations of a single phenomenon known as transduction, where one type of energy is transformed into another, in this case, acoustic energy to electrical energy and vice versa¹.  

Gilbert Simondon draws upon these physical concepts to develop his theory of physical individuation, where a metastable system is resolved by developing its potential into a structure (Simondon 5). For Simondon, induction is a unidirectional epistemological process that generates plausible generalizations derived from individual observations, therefore requiring a loss of information (15). In ANC, the unidirectional inductive process is exemplified by the transformation of ambient sound into a simulacrum of reality. The outcome of this process is pre-determined by the observations embedded in the algorithms and then presented as a virtual reality, disregarding the information that has been cancelled as unnecessary.

Conversely, transduction provides the basis for an explorative form of thought which is not necessarily teleological or linear. Simondon expand this definition to include “a physical, biological, mental, or social operation through which an activity propagates incrementally within a domain” (13), which also allows for reconfigurations of new structures without loss or reduction (15). Furthermore, transduction is the main process through which individuation is afforded, thus being able to actualize the multiple possibilities of a metastable system.  

ANC’s inductive algorithms that process the acoustic environments therefore negate the processes of transduction, not only by producing a loss of information, but also by negating the process of individuation, replacing potentiality with determinacy. In Simondon’s words: “the veritable limit of induction is plurality in its simplest and most difficult form to cross: heterogeneity. As soon as inductive thought is faced with this heterogeneity that it must resort to transductive thought” (Simondon 127).

Within this framework, the act of listening is regarded as a fundamental transductive act, not only in the actual transformation of acoustic energy into electrical neural signals, but also as a process of individuation: as the potential of creating auditory scenes by resolving the metastable possibilities that the soundscape provides. The act of listening as a transductive process involving intuition, discovery and becoming, serves as the basis for an exploration that "discovers and generates the heard" (Voegelin 4). In contrast, the inductive method imposed by ANC delegates the potentiality of the soundscape to the algorithm, becoming the arbiter of perception, and thereby negating access to the nuances of noise and the soundscape itself.  

Sonic Distance

Considering listening as a fundamental Simondonean transductive act invites to conceptualize sonic distance from a phenomenological perspective that prioritizes spatial and embodied listening and negotiates between malleable relationships of different acoustic realities. Similar to Olga Goriunova’s conceptualization of distance as a non-representational notion that negotiates “between digital subjects and the human entities and processes they are connected to” (128), I consider sonic distance as a relation between two distinct modes of perception: a commonly perceived reality, and an algorithmically altered one, the last one which is only accessible by the inductive processes embedded in ANC. While Goriunova is interested in the distance between the “digital subject”² and the human being, the distance generated by ANC occurs between a “digital acoustic subject” (a digital representation of the data gathered and processed by ANC Headphones) and the sonic actuality of soundscape. ANC not only encourages a sonic distance through sonic alienation and the imposition of an inductive sonic reality already determined by technology, which carries the implicit biases of its teleological functioning. ANC also introduces a form of distance that goes beyond a pure physical realm and affects directly the sonic profile and footprint though which human and non-human agents create a relational dynamic through sound.  

Following Henri Lefebvre's categories of space (Lefebvre 38-9), I would further argue that ANC disrupts the perception of space at its three main levels: spectral (conceived space), architectural (perceived space), and personal (lived space)  

From a technical perspective, ANC headphones disrupt the conventional conception of spectral acoustic space through a series of digital processes that include analogue-digital and digital-analogue conversion, adaptative filtering, and destructive interference, all of which modify the and spectral and spatial qualities of audio signals and surrounding soundscapes. The cancelled spectral space of the signal subsequently becomes a negative space deemed as noise, which favours the idealized signal through a deterministic process of deconstruction and objectification, a signal that exists between the real and the synthetic, where information is actualized as the ideal entity of the cybernetic project: immaterial and disembodied.  

This spectral space is analogous Lefebvre’s conceived space, an abstract representation of audio and soundscapes as numbers, samples and digital processes. ANC challenges this conceived space by obscuring its processes of transformation, and by altering the pre-conceived forms of capture and reproduction of audio signal. In the context of speech transmission and telecommunications, ANC creates an enhanced form of "acousmatic" or schizophrenic voice, by taking it out of its context and (re)creating it as a purer form of itself. (see Kane). In the musical context, ANC attempts to replicate the High Fidelity conditions prevalent in recording studios such as acoustic isolation and controlled reverberation time. In listening to ANC, the user has access anytime anywhere to the HiFi promises of experiencing music “as if” it was listened in the recording studio. These spectral modifications and re-creations do not reconstruct a real space, but rather simulate one based on the references of its acoustic and spectral configurations, i.e. create a simulacrum.  

The spectral non-space that alludes to the HiFi audio quality and optimal recording studio acoustics, negates the context of environmental sound, and thus challenges the conception of architectural space. For Lefebvre “the spatial practice of a society is revealed through the deciphering of its space” (Lefebvre 38), that is, the quotidian relations between private and public sphere are revealed through the activation of places and forms of transit, leaving behind a sonic trace characteristic of their sonic persona (Schulze 123).  ANC’s negates access to these spaces and practices, which is achieved not only by confining the individual to their own headspace, but also by creating a negative architectural space, a non-space, in which only sounds deemed worthy of containing information are allowed to be reproduced. Non-spaces are described by Marc Augé as "a space which cannot be defined as relational, historical, or concerned with identity" (Augé 63). Consequently, the non-space offered by ANC also negates a phenomenological perception of space, by separating not only the signal from its context, but also the listener from his spatial context, from his embodied experience.  

For Lefebvre, space is produced in time, i.e. it is historical. Therefore, the non-space of ANC is not produced in time, it is always already there. While the soundscape of the non-space may be contingent to the nuances of its particular context, the results are always the same: the user is transported to a ubiquitous ahistorical, non-space of noiseless purity where time and space do not exist. As it is the case in malls and shopping centres, the sonic non-space produced by ANC it’s the consistent across locations, and its expectations and experiences are the same whether the listener is located in Berlin, Bogotá or Bandung, listening today or ten years ago. The negation of architectural space is even more present in transparency mode, where the reproduction of the environment is outsourced to the algorithms, presented as pure information which is disembodied and non-relational. This non-space becomes non-existent in re-produced signals, alienating the signal from both its original source and its context.  

Third, ANC presents an affront to personal space. Through transparency mode, the transformation of personal space it is not a simple negation of a social space, but rather an active awareness of one's surroundings, enabling its re-productions and the actualization of a non-place. Isolation and personalization thus negate the social relational dynamics that take place in urban and social contexts. ANC then negates the relation with the lived spaces of Lefebvre through “associated images and symbols” and challenges the “more or less coherent system of nonverbal symbols and signs” (Lefebvre 38). This is exemplified by the mediation of interactions afforded through transparency mode; when users engage with conversation while wearing headphones, there is a disruption in the nonverbal cues and expectations of communication.  

Furthermore, ANC intersect with other forms of sonic technologies in what Mack Hagood calls "orphic" sounds (Hagood, “Hush”), that is, soothing mechanisms to combat the increasingly noisy environments and stressful lives of post-industrial societies. These include not only ANC technologies, but also natural soundscapes, various forms of drones and noise, and so-called binaural beats. Wellness-embedded sound devices also provide an experience of noise cancellation, an optimization not only of the signal, but of the subject itself. By cancelling out noise, the user protects his ears, is more productive, more relaxed, more themselves. Ultimately, it is a manifestation of identity politics in the form of an internal wellness culture.  

The creation of this architectural, spectral and personal non-space afforded by ANC, brings implicitly and unintentionally attention to what is not there. For Simondon, indeed, “resolving transduction operates the inversion of the negative into the positive” (Simondon 15). Transductive listening then turns the negative (noise) into the positive.  

Noise

Implicit in the design of ANC algorithms is a conceptualization of noise as an objective entity, based on a definition of noise as a measure of the probability of information, as proposed by Shannon and Weaver (1964). The quest for a "pure" signal has encouraged communications engineers to devise methods for optimizing message transmission. The relationship between noise and signal (c.f. signal-to-noise ratio) can thus be measured, quantified, and objectified, positioning noise as the opposite of signal: an unwanted, othered entity. Noise-cancelling algorithms participate in a dialectical relationship between imposed social and cultural binaries, reinforcing these very demarcations.  

The ideological consequences of conceptualizing noise as informational entropy account for biases gender, race, and class biases, among others (see Malaspina). Tina Tallon points out how the signal filtering implemented in the early days of mass landline telephony was optimized with the white male voice in mind, producing undesirable (shrill) results for female voices. (Tallon). Similarly, current digital voice communication technologies such as Zoom or Facetime implement significant processes of filtering, compression and noise reduction, as well as anti-feedback algorithms that modify their input for optimal transmission. Likewise, digital compression codecs such as MP3 have compressed and filtered parts of the recorded audio that are considered irrelevant in a psychoacoustic sense, in favour of a smaller, more portable and efficient data format for transmission (Sterne).

Noise, therefore, needs to be expanded into a broader definition that goes beyond the acoustic and informational, and that includes its social and cultural manifestations. For instance, Cécile Malaspina (2018) distinguishes between noise as a qualitative measure of sound and a quantitative measure of information in relation to noise, the former measuring noise as an object of perception, the latter measuring a relation of probability. Within sound studies, the phenomenon of noise is often seen as culturally and historically contingent (see Attali, Hegarty, Voegelin, Hainge). As Mack Hagood puts it: "Noise is othered sound, and like any othering, the perception of noise is socially constructed and situated in hierarchies of race, class, age, and gender.” (Hagood Quiet Comfort 574). Meanwhile, for Jacques Attali noise is violence, disruption and disconnection, an interruption of a transmission (Attali 26) that denotes relations of power and control (Attali 123).  

This socialization of noise is most present in the demarcation and construction of human bodies, and it is even more present in the control and regulation of racialized and queer bodies. Salome Voegelin compares the bodily effects of noise and silence, positing them as opposites: "Noise pushes vertically down my body, compressing my chest and propelling me outward into my breathless bodily fantasy. Silence, on the other hand, enters me and pulls me inward and outward, stretching my nervous system through thin layers of skin, hooking my inner flesh to the outer edge of my body" (Voegelin 86). The numbness caused by noise is then turned inward, creating an individual space of self-protection without the possibility of relating to the outside world. The elimination of this noise is then desired, with a longing for its opposite, silence, which creates an outward awareness of our surroundings.  

Nevertheless, Noise Cancelling Algorithms do not provide the experience of silence, but of "non-noise."  The possibility of a "Cagean" silence that expands listening into a possibility of meaning, as described by Voegelin, is denied in the resynthesis processes of noise cancelling algorithms. ANC imposes its own version of reality by assigning a predetermined meaning and preventing the possibility of exploratory listening. This is presented to the user as a form of control that is also pre-established in the (sonic) user interface. As Joseph Klett notes, "'Silence' does not mean silence 'out there' so much as it represents control over one's hearing 'in here.'" (Klett 124).  

Contrary to the oppositional approach presented by Voegelin, noise and silence can be viewed as part of an ever-evolving continuum based on perception. The opposite end of this continuum is information, or the desired signal. However, in contrast to Shannon and Weaver's objectification, information can be conceived as a perceptual manifestation, based on a phenomenological approach to listening that prioritizes attention and bodily experience as epistemic tools. By negating the complementarity of the noise-silence continuum, noise cancelling technologies have achieved the same phenomenological results as Voegelin's conceptualization of noise: “noise now, in its quasi inertia, is not about mass movement and progress, but about private and isolated fixity: listening on a heavy spot and pondering that position.” (Voegelin 43). That is, ANC force the listener into a unidimensional and homogeneous listening reality that avoids any potential for multiplicity and thus negates a perceptual perception of information. A reconceptualization and repurposing of its technologies is therefore needed to achieve ANC’s aesthetic, exploratory, and transductive potential.  

Reduction  

Musically, noise has evolved from the antithesis of music to a more affective and disruptive method of conveying numbness, discomfort, and discontent. (Voegelin 86). From Luigi Russolo's proto-fascist manifesto to the digital synthesis techniques of Iannis Xenakis to punk and Japanoise, various forms of "noise music" have developed their own aesthetic and confrontational discourse (see Hegarty) As such "noise-in-itself" is positioned as an artistic methodology, that implicitly valuates noise positively and positions the existence of "noise-music" as an aesthetic oxymoron, a metastable phenomenon.  

Nevertheless, within the pervasive negative valuation that is ubiquitous in our cultural milieu, noise as the other has been the subject of several levels of systematic control and regulation mechanisms, of which ANC is the most recent example. The concept of noise pollution has been employed as a pretext to pathologize and regulate loud and chaotic sonic environments, and even to present them as an aesthetic threat to the ecological preservation of soundscapes, as evidenced by the practice of R. Murray Schafer (Schafer). The purist form of listening encouraged by noise regulations is a direct consequence of the cybernetic objectification of noise and is one of many factors that reinforce the social and cultural paradigms underlying the development of noise reduction algorithms.  

While R. Murray Schaffer proposes a return to purer soundscapes and the contamination of the environment by industrial noise pollution, Marie Thompson notes that the ways in which noise is represented take on a form of universalization, where all noise is seen as impure and immoral (Thompson). Thompson presents a series of counter-examples to the noise-silence ethical binary: Solitary confinement and silence are used as forms of punishment, and natural soundscapes such as the Amazon rainforest can be incredibly loud and disturbing (Thompson 100-1). Access to silence is then not only mediated by public order laws, by acoustic, architectural or urban dispositions, but rather refers to an access to control. The mediation of a digital process embedded in ANC is therefore not only a form of environmental control, but also a hyper-real actualization of an acoustic imaginary and its embedded aesthetic moralism.  

From a phenomenological standpoint, beyond creating an optimal acoustic signal, ANC algorithms provide an experience of noise cancellation, regardless of the content of the signal. For instance, Spike Jonze's advertainment shot film “Someday” (Jonze 2025) exemplifies the ways in which ANC headphones are not just a technological advance or a Hi-Fi device, but rather providers of an experience of isolation that is made to be displayed in public: a kind of isolationist voyeurism that is at once individualistic and social. Mark Hagood considers noise-cancelling technologies as mechanisms through which personhood is created and reinforced, enclosing the self and protecting it from the increasing sources of environmental noise (Hagood, “Hush”). Hagood also distinguishes between traditional narrative media that entertain or inform, and current forms of media that not only seek to make the medium invisible, but also seek to make the content itself invisible, creating a perceptual absence (Hagood, “Hush” 22). That is, the digital signal processing of environmental sounds is presented as invisible, as an experience, even though a mediation is taking place constantly.  Indeed for Hagood, media does not function as an invisible medium to carry information, but is rather an affective tool that alters “how the body feels and what it perceives, controlling our relationship to others and the world, enveloping ourselves, and even disappearing ourselves.” (Hagood Emotional Rescue). In Jonze’s commercial, when the ANC is turned on, Pascal is not transported to a completely new reality, but to an enhanced reality, more vivid, playing with the promise and hope of a better future.

Conclusions

The induction of sonic distance could be understood as an imposition rather than an unexpected consequence of a new convenient technology. The difference between regular headphones and ANC is analogous to the difference between Virtual Reality in which the subject is completely immersed in a produced reality, and an "Augmented Reality" where a see-through camera renders the landscape and adds and positions virtual elements into it. A regular stereo experience, such as that of the Walkman, can create a personal soundtrack or a "secret" that can only be experienced by the headphone listener. (Hosokawa).  

Conversely, ANC, through a "hear-through" microphone (i.e., transparency mode), reproduces the soundscape and adds virtual elements to it (music podcasts, phone calls, etc.) This kind of "Sonic Augmented Reality" or "Sonic-Extended Reality" provides a fundamentally new experience, avoiding the isolation of the individual while directly affecting the perception of their "sonic reality." According to Hosokawa, the mystery is still there, but the listener can also eavesdrop on the shared reality (Hosokawa).

Rather than negating the environment by replacing its acoustic content, (i.e. replacing a soundscape with prerecorded sounds), ANC relegates the listening process of transduction and individuation to the contingent biases of the algorithm. The promise of an experience of individual calm is only archived through the simultaneous violent and disruptive imposition of predetermined biases of algorithmic mediation, i.e. the induction of noise. By replacing exploratory listening with a synthetic experience, this induced sonic distance not only alters our relationship to our surrounding soundscapes, but also induces "noise" in the form of alienation of our senses.

In ANC algorithms, Attali's noise as violence and disruption is manifested in the forced modification of everyday environmental sounds, such as crowds, traffic, soundscapes, that are predefined as noise. Nevertheless, taken noise as socially and historically contingent, ANC devices have the potential to reconfigure the sensory experience of noise, challenging its established socially constructed boundaries. The expansive nature of my ongoing artistic research project Noise Re(in)duction resulted in a non-linear basis for a diversity of outcomes and media manifestations. What started as an interest for the inner workings of noise reduction technologies has turned into an intermedial non-linear research project that manifests in lectures, performances, essays, installations around the topic of noise reduction. Some preliminary results have been presented in the form of lecture-performances and audiovisual installations. By making sensible these forms of sonic distance, the conceptualizations presented in this text acquire and additional dimension. This dimension is only be perceived, manifested and embodied within the scope of a speculative artistic practice that are being and will continue to be explored (see Daleman).

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Biography

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