**Martin Zeilinger**

Tactical Entanglements

# **TACTICAL ENTANGLEMENTS**

 **AGENCY**

**Tactical Entanglements**

Martin Zeilinger (Senior Lecturer in Computational Art and Technology, Abertay University) is an Austrian researcher and curator currently based in Dundee, Scotland. His work focuses on critical artistic approaches to emerging blockchain and AI technologies, intellectual property issues in contemporary art, and aspects of experimental videogame culture. He has co-curated Vector Festival (Toronto) since 2014, and organized the 2019 MoneyLab symposium (London). His research is available in books such as *Artists Re:Thinking the Blockchain* and the *MoneyLab Reader 2*, and journals including *Philosophy & Technology*, *Culture Machine*, and *Media Theory*. // marjz.net / @mrtnzlngr

# **Tactical Entanglements: AI Art, Creative Agency, and the Limits of Intellectual Property**

**Martin Zeilinger**

Financial support for this publication was provided through the Abertay University Open Access Publication Fund.

The cover shows an AI-generated image output created by the AI artist and researcher Tom White (https://drib.net/). Reminiscent of the now-famous AI artwork *Portrait of Edmond Belamy* and the authorship dispute it sparked (discussed in chapter 4), the images "were made by [White] using [Robbie Barrat's] code to show that [Obvious Collective] could have done the same" (Artnome 2018).

#### **Bibliographical Information of the German National Library**

The German National Library lists this publication in the Deutsche Nationalbibliografie (German National Bibliography); detailed bibliographic information is available online at http://dnb.d-nb.de.

Published in 2021 by meson press, Lüneburg, Germany www.meson.press

Design concept: Torsten Köchlin, Silke Krieg Copy editing: Isobel Campbell

The print edition of this book is printed by Lightning Source, Milton Keynes, United Kingdom.

ISBN (Print): 978-3-95796-183-9 ISBN (PDF): 978-3-95796-184-6 ISBN (EPUB): 978-3-95796-185-3 DOI: 10.14619/1839

The digital edition of this publication can be downloaded freely at: www.meson.press.

This publication is licensed under a CC BY-SA 4.0 International license. To view a copy of this license, visit: https:// creativecommons.org/licenses/by-sa/4.0/. Copyrights and permissions for images are noted in the image captions.

#### **Contents**

Acknowledgements 7

#### **[ 1 ] Introduction: From Agency to Property, from AI to IP 9** Decentering Human Agency in AI Art 16 AI and Creativity in the Intellectual Property Milieu 21 Overview 26

#### **[ 2 ] What Does AI Hack? Scaffolding for a Critical Art of AI 33**

Redefining AI in and through Artistic Practice 36 Whence and Whither Agency? 42 Tactical AI 50

#### **[ 3 ] (Dis)Locating Creativity, Agency, and Property in AI 57**

AI Authorship According to Hegel 61 Can IP Law Accommodate Artistic AI? 66

Copying and Human-Non-Readability 108

#### **[ 8 ] AI Art for a Posthumanist Cultural Commons 155**

GANs as "Generative Adversarial Copy Machines" 157 AI Beyond the Public Domain 162 Unownability in the Posthumanist Cultural Commons 166

References 175

#### **Acknowledgements**

Book-writing is an amazingly collaborative effort, considering how alienating and lonely it can feel. In the pandemic-stricken year of social isolation during which I wrote most of this book, my work has occasioned very stimulating conversations and discussions with friends, colleagues, artists, and academic peers around the world, for which I'm immensely grateful. Thank you to friends and colleagues old and new, near and far, many of whom offered to look at and think about parts of the manuscript in its various iterations, and who provided feedback, encouragement, words of warning, questions, answers, or other kindnesses. They include Danilo Mandic, Tina Kendall, Ronald Ng, Andrew Bricker, Greg Smith, Bruce Lai and Katrina Lagacé, Jason Bailey at Artnome, Jeff Thompson, Nicky Hamlyn, Patricia de Vries, as well as Christos Michalakos, Hadi Mehrpouya, Joseph De Lappe, Darshana Jayemanne, and Drew Hemment in and around Dundee.

My critical perspective has been shaped in important ways by conversations with several of the artists whose work I discuss in the book—particularly Maja Smrekar, Adam Basanta, and Jake Elwes. Thank you for sharing your work and insights so generously.

The book in its final form also gained tremendously from insightful commentary and critical remarks on a first draft, received from my excellent reviewers Carys Craig and Andreas Broeckmann.

I am very grateful for the supportive welcome I received at Abertay University's School of Design and Informatics, where I took up employment around the time I began working on the book. In particular, thank you to Dr. Dayna Galloway (Head of the Division of Games and Art), Professor Gregor White (Dean of School), and Professor Nia White (Dean of Graduate School) for supporting this publication through the university's Open Access Fund. Thanks also to Andreas Kirchner and everyone at meson

press, for taking on the project at a difficult time, and for seeing it through so efficiently.

To many other friends, thanks for your patience in dealing with cancelled bike rides, skipped movie nights, postponed visits, unanswered phone calls, and much-delayed email replies—I have much yet to learn about book-life balance.

Lastly, the biggest thanks goes to my family, for your enduring support, and to Deniz Johns, for all your love, encouragement, wisdom and understanding.

# **[1] Introduction: From Agency to Property, from AI to IP**

This book explores digital artists' experiments with emerging technologies of artificial intelligence (AI) in order to formulate a critique of how AI is impacting and reshaping the concepts of agency and ownership. The concerns underlying this focus can be roughly summarized as follows: on the one hand, AI appears to gesture toward new paradigms of thinking, acting and being that promise a push beyond ideological horizons centered on the human(ist) agent; but on the other hand, AI is deeply entangled with socio-economic and political regimes that rely on precisely this subject position, often in problematic alignments with the capitalist logic of contemporary ownership models. AI, in this sense, exists in a diffuse border region between the compelling idea of emerging non-human agency (embodied in the very notion of 'artificial intelligence' itself) and the all-too human contexts in which contemporary AI tends to manifest (for example as algorithmic control mechanisms in the domains of work, governance, law, finance, or entertainment). Using AI art as my core subject of analysis and discussion, I want to consider what interventions can be staged in anthropocentric ownership models by

**10** rethinking agency in and through AI. I will argue that critical uses of AI in digital art can be ideally suited for disturbing conventional notions of the singular, unified artist figure, of the unique artwork, and of anthropocentric perspectives on creativity as such. AI art can thus speculate on critical reconfigurations of creative agency—and therefore also on the potential destabilization of intellectual property (IP) models that continue to rely on the integrity the human(ist) agent as creator and owner.

My discussion is not going to echo spectacularizing assertions that AI is "becoming creative" in the sense of fully autonomous computational entities capable of matching and exceeding the aesthetic, artistic, and artful expressions of humans. In my view, claims of emergent AI creativity tend to be marred by problematic anthropocentric biases, both regarding the concepts of creativity perpetuated in such claims, and regarding the humanistic ownership models underpinned by these concepts of creativity. Rather than trying to imagine an AI art in which human artists are supplanted by human-like creative machines, I want to explore what collaborative entanglements exist between artists and AI, and consider the critical ends toward which such entanglements can be oriented. What new types of artistic practice, what reconfigurations of the author function, what new forms of creative and critical expression can be seen to manifest, in what I will call the works and workings of posthumanist agential assemblages?

Given my focus on agency and ownership, two domains of critical thought are particularly important for this discussion. The first is law, and more specifically theories of intellectual property, which form a key critical interface between art, expressive agency, and the cultural and socio-economic environments within which they are embedded. The second is posthumanist thought, which I consider to be among the most useful frameworks for exploring the critical valences of AI in digital art. The conjunction of legal theory and posthumanism allows me to foreground discrepancies and productive contradictions between the two frameworks when it comes to issues of creative expression, agency, and ownership.

Throughout, I draw on posthumanism in a sense similar to that **11** in which Gary Hall has described it, namely as a perspective that "is concerned with the displacement of the unified, self-reflexive, and rational humanist subject from its central place in the world as a result of the erosion of the human's 'natural' boundaries with the animal, technology, and the environment" (Hall 2016, 93). In the context of AI art projects that recalibrate the shape and meaning of creative agency, such a perspective facilitates a departure from the "traditional liberal humanist model that comes replete with clichéd, ready-made . . . ideas of proprietorial authorship, . . . originality, fixity, and the finished object" (xiv).

A discussion of legal technicalities concerning copyright and authorship might seem at a considerable remove from key concerns of AI art as such. But it must be kept in mind that a key effect of IP law is its codification of broadly accepted notions concerning the nature of creative expression and authorship notions that AI art frequently addresses quite directly. Practically speaking, IP laws are formulated with the aim of being inclusive and adaptive. Nevertheless, the law outlines prescriptive requirements regarding what an artwork can be, who (or what) its author can be, how a specific artwork can circulate, and how this circulation can be controlled. If IP law is understood as a key manifestation of broadly accepted views on and attitudes toward authorship and cultural ownership, then there is certainly much to discuss in relation to AI art projects that problematize or disturb views and attitudes codified in the law. Such discussion has particular critical purchase in a number of contexts addressed in later chapters: for example where the algorithmic enforcement of copyright law is concerned, and where proprietary and blackboxed algorithmic systems are used to implement complex legal norms and standards; or also where AI art implements tactics aimed at challenging the integrity of the ownership models that are expressed in IP law, in technology uses sanctioned by the law, and in humanist views on authorship and creative expression underlying the law's prescriptions.

**12** Building on Christiane Paul's widely adopted definition of digital art (2016), I define AI art, most generally, as digital art that incorporates technologies of artificial intelligence as a medium. By this I mean that AI art utilizes AI, and that it implements AI practically even when it does not engage AI thematically. AI art, in other words, is not necessarily about AI. Other cultural theorists of AI have calibrated their focus quite differently: Joanna Zylinska, for example, proposes that "one of the most creative—and most needed—ways in which artists can use AI is *by telling better stories about AI*" (2020, 31; emphasis in original). In the context of my own discussion, I would agree that every AI artist (by which I mean every human artist working with AI) must speculate on the existence and potentialities of non-human creative agency. But I would also emphasize that there can be important differences between narrativizing such speculation and developing it in a mode of practical experimentation. In my opinion, one such difference is that in the many fabulations of AI art, crucial details concerning the functionality or outputs of AI systems can be easily glossed over, simplified, or obfuscated (one example of this kind of rhetorical blackboxing of AI is addressed in my discussion of *Portrait of Edmond Belamy* in chapter 4). My main focus is therefore on AI art projects in which critical speculation is enacted structurally, based on a practical engagement with AI technologies. In the AI art projects discussed in the following chapters, this will be seen to manifest in a kind of hacking of AI that interrogates AI's own abilities to hack concepts including those of agency, creativity, and ownership.

True to AI's origins in the military-industrial complex, and true to the support its development now receives from corporate tech giants, mainstream implementations of AI tend to perpetuate and amplify very narrow perspectives on agency and ownership. This occurs in alignment with ideological frameworks that have been variously described, for example, as computational capitalism (Stiegler 2019), cognitive capitalism (Boutang 2011), communicative capitalism (Dean 2005), or surveillance capitalism (Zuboff 2019). Mainstream AI, in other words, tends to serve **13** socio-economic regimes that rely on the automation, high-speed calculation, data-intensive analysis, predictive techniques, and communicative abilities that computation affords. As a result, AI applications can constitute frightful surveillance tools, restrictive digital rights management systems, manipulative recommendation algorithms, biased personal assistants, Kafkaesque algorithmic governance frameworks, or exploitative high-speed stock trading protocols. Based on these and other sinister realities of contemporary AI, Nick Dyer-Witheford, Atle M. Kjosen and James Steinhoff (2019) put forward the dark notion that the inhuman power of AI may end up emancipating capital from humanity, rather than the other way around. What I want to argue is that AI also offers important opportunities for practice-based experimentation that can recast the ideological imbrications of the underlying technologies very differently. This, perhaps, is the speculative core of my argument in this book: diverging from Nick Land's notion of AI as a fundamentally capitalist technology (Land 2012), I want to explore how, in cultural contexts, AI might also be framed as a tactical anti-capitalist tool, at least in the sense that it is capable of producing and enacting new perspectives on creative practice, authorship, and the artwork that challenge prevailing notions of human(ist) creative agency, and the cultural logic of intellectual property derived from it.

Today, the field of AI art can appear as a creative industries "sector" that is defined, driven and dominated by corporate funders. This impression is conveyed, for example, by the many AI art residencies and project grants sponsored by Google, Amazon, Facebook, Microsoft, and other corporate entities described by Bernard Stiegler as the "new barbarians" that are closing off horizons of technology-based possibility, in efforts to foreclose our ability to expand or reconfigure such horizons (2019). But the digital art communities within which AI art flourishes have also been crucially shaped by open source culture,

**14** radical philosophies of open access to knowledge and technology, by the activist approaches of tactical media, the hacker ethics of the early internet, and the ideals of a cultural commons. It is against this background that I want to develop my discussion of AI art projects which problematize the humanist vision of the singular, unified human agent; of the spirited (genius?) individual as sole originator of creative expressions; of the human as selfinterested proprietor of the fruits of their labor; and of aesthetic, legal, and socio-economic paradigms that model restrictive property regimes around this kind of agent. In adopting a posthumanist perspective, my emphasis is instead on the potential for AI art to instantiate agency as relational, decentered and plural, and to approach creative practice as fundamentally dynamic and embedded.

In what follows, current AI art projects (most of them undertaken since 2018, or still in development at the time of writing) serve as the scaffolding for a broader discussion that spans overlapping discursive fields to integrate posthumanism and law with aesthetics, media theory, and political economy. Across all of these domains, the concepts of agency and ownership are key to understanding how human subjects have been idealized as presumptively free, self-determining, and self-owning; how this idealization feeds into still-prevailing definitions of creative expression, authorship, and the artwork itself; and how these definitions have helped to rationalize a framing of the artist as a quasi-neoliberalist free agent whose activities and desires align with predominant ownership models. Against this backdrop, how are agency and ownership inflected when artificial intelligence comes into the picture?

Mainstream AI development certainly contradicts the utopian tone of predictions that AI will be (or already is) capable of intelligent behavior, that it may soon display traits of agency previously reserved for natural persons, and that it is in the process of becoming creative. It points, instead, in the direction of AI-driven surveillance capitalism, corporate control over networked communication systems, and the rampant **15** financialization of everyday (digital) life. In these contexts, the centrality of the human agent persists, even if only in the interpellation of disembodied data subjects that continue to produce and consume, to own and owe in alignment with exploitative capitalist ideologies. In cultural contexts, likewise, the humanist, anthropocentric notion of the singular, unified artist figure persists, and even where digital culture courts ideals of sharing, free access, or easy reproducibility, much of it is nevertheless structured by a property-oriented fencing-in of individual creative agency. Overall, a key linchpin around which digital culture and digital art continue to turn is the cultural logic of intellectual property. A key question pursued in what follows is how this logic may be challenged when AI art pushes for a rethinking of agency beyond the centrality and supremacy of the human(ist) subject as author and proprietor, and when it reorients itself toward emergent forms of posthumanist agential assemblages that contradict existing property paradigms. As I will argue, in such configurations expressive agency is located beyond the confines of the human artist, and co-exists with them in productive, intra-actional entanglements that integrate *anthropos* with computer hardware, software, algorithms, and other tools, crafts, or knowledge on which the artist relies. As will be seen, this discussion does not abandon the humanist subject entirely, but rather recalibrates it in relation to the ecologies in which it shares.

Agency is most generally understood as the manifestation of a capacity to act. But the concept is also intimately tied to questions of self-determination, autonomy, expressive freedom, and the ability to own property. How, then, is agency constituted in the age of AI? Who or what is an agent now? What new aesthetic, legal, and socio-economic contours and limits of agency emerge in AI? How does human agency relate to its algorithmic and machinic others? What new horizons of critique become possible when creative agency, in its links to authorship and ownership,

**16** are rethought in and through AI, in speculative approximations of posthumanist agential assemblages whose works and workings push beyond anthropocentric and humanist ontologies of creativity and artfulness, intellectual property and cultural ownership?

#### **Decentering Human Agency in AI Art**

Let me offer two introductory examples that resonate with many of these questions. The first focuses on the work of the British artist Anna Ridler, who frequently uses machine learning (ML) to explore issues of creativity, authorship, and ownership. Among Ridler's key strategies for doing so is the use of custom, artistassembled datasets. The artist has described this as a powerful political act that can help move past the exclusionary tendencies ingrained in mainstream AI (Ridler 2020). This approach is well exemplified in *Mosaic Virus* (2019), a three-channel video installation that depicts morphing formations of AI-generated images of tulips (fig. 1). The work focuses on the historical "tulip mania" phenomenon and explores enduring human obsessions with agency and ownership through the themes of monetary wealth and financial speculation. The tulip mania phenomenon dates to 1630s Holland, when tulips had become highly sought-after flowers, and certain tulip bulbs were seen as items of immense value. The perceived value of the bulbs was linked to rare color stripes caused by a plant virus, which would—unpredictably appear on some tulip petals. At the time, the behavior of the so-called mosaic virus was poorly understood, and botanists were unable to control how it propagated the prized petal stripes. This led to frantic trading in tulip bulbs, and what is now considered one of the first speculative financial bubbles. In Ridler's installation, the unpredictable effects of the virus and its destabilizing effect on the value of tulip bulbs allegorize the speculative desires that both art and finance can inspire. Specifically, *Mosaic Virus* links the instability of values bound in commodified artefacts to the perceived unknowability of the computational

technologies used in creating the work. The work thus thematizes **17** a decentering of human agency, its redistribution across nonhuman systems, and the impact of this redistribution on questions of monetary value and ownership.

[Figure 1] Anna Ridler, installation view of "The Abstraction of Nature," solo exhibition at Aksioma, Ljubljana (2020). Photo credit: Domen Pal / Aksioma.

The use of a generative AI system is key to how this is conveyed to the viewer, because it extends questions of monetary value and ownership to the aesthetic realm and the concept of authorial control. To create *Mosaic Virus*, Ridler used a Generative Adversarial Network (GAN), a type of machine learning technology capable of generating novel outputs on the basis of large datasets of pre-existing material. (In-depth discussion of GANstyle generative image synthesis is offered in chapter 4, and also in Zeilinger 2021). The system was trained to generate everchanging images of (inexistent) tulips using an artist-created dataset of roughly 10,000 hand-labelled photographs of actual tulips, and the outputs were additionally inflected by the everchanging value of Bitcoin tokens (a speculative object of desire *par excellence*). Because the project uses a training dataset painstakingly assembled by the artist, *Mosaic Virus* conveys a strong

**18** sense that the artist retains a high level of creative agency (fig. 1 shows parts of the dataset exhibited alongside the video installation). The operational logic of GAN systems, however, lends the outputs a level of unpredictability, which is further amplified by the well-known volatility of Bitcoin token value. As a result, just as it may be impossible to authoritatively predict the value of tulip bulbs affected by a virus, so it becomes difficult to precisely locate creative agency in an artwork produced by a posthumanist agential assemblage of human artist, generative AI system, and erratic cryptocurrency valuation data.

Given the focus of this book, the nature of Ridler's training dataset strikes me as a particularly important parameter in the conceptual equation of *Mosaic Virus*: it foregrounds the presence of the artist in the creative process, while also highlighting the limited agency Ridler has in controlling outputs of the AI system she designed. The chosen medium and thematic framing offer a good context for a self-reflexive exploration of the limits of agency: the triad of generative AI, cryptocurrency, and plant virus can serve as a fitting "blackbox" of unpredictability into which anxieties concerning the limits of human control (i.e., the control of both authors and owners) can be projected. Based on this reading, I see as a central critical aim of *Mosaic Virus* its reflection on emerging digitally-bound value systems in which speculative fantasies of power and wealth become linked to questions of authorship, ownership, and agency. Here, non-human spaces of presumptive unknowability—the viral, the algorithmic—run up against all-too human obsessions with monetary and aesthetic value and authorial control. In other words, faced with complex non-human systems such as viruses, AI, or the blockchain, anthropocentric fantasies of control can dissolve into wild speculation, and the centrality of human agency is recalibrated in much more complex distributions across posthumanist assemblages.

Importantly, this reading is also somewhat undermined by Ridler's own suggestion that human agency—enacted in the significant degree of control the artist exerts over the training data—remains central to artistic uses of AI (2020). The artist's **19** process of hand-assembling datasets is incredibly onerous; it rightfully presents itself as craft, as an artful process through which human intervention in the workings of AI is rendered as a valuable creative act. Ultimately, this provokes an interesting contradiction, since the project mobilizes the perceived unpredictability of generative AI outputs as a playful challenge to the very assumption of human mastery.

A different example indicates that the theme of agency is also a powerful lure for AI artists who are less concerned with critical interrogations of the concept. *Contrapposto* (2020), an AI-generated sculptural work by the American artist Ben Snell, struggles to locate new forms of non-human agency and creative expression meaningfully in AI. The amorphous sculpture, visually reminiscent of Henry Moore bronzes, is part of the artist's *Inheritance* series, which is "inspired by the classics" (presumably a nod to images of canonical sculptural work that constitute the training data) and "made from the pulverized computer that dreamt it" (Snell 2020; an image of the work is available to view on the referenced website). The sculpture thus consists of "flecks of silicon, copper, aluminum, plastic and pcb […]. Within these bits exist the memories, the thoughts and thought-making power of the machine which created it. Its existence comes full circle in this new form, embodied with a newfound aura and agency" (ibid.). This framing is both intriguing and confusing. The artist clearly wishes to attribute creative agency to the computational system used in creating the work; formally, however, absolute dominion over this same system is asserted. Given the material form of the sculpture and the method of how it was made, it is unclear how or where agency is supposed to be situated here. Is it centered around a particular entity (the machine, the algorithm, the artwork), or is it distributed across dissolving boundaries between them? Does it open up a new horizon of creativity (AI as a speculative signifier of emerging posthumanist subjectivity), or

#### **20** doesn't it, rather, demarcate a limit (AI as a pseudo-autonomous tool used by a human artist)?

*Contrapposto* anthropomorphizes AI in the strongest terms. Visually, it invokes the master sculptors of the art history canon, and the series title positions the work as the literal offspring of these traditions. All around, the work is clearly meant to gesture toward a new kind of creativity embodied in computational technology, which is described as being "reborn" in the sculpture, where its processing powers "live on" (Snell n.d.). But it is precisely through this anthropocentric framing that the work ultimately reverts to the domineering centrality of the humanist artist figure who, after all, has "ground the computers to dust" (ibid.) in creating the sculpture (see Vincent 2019 for images of this process). This makes it difficult not to read *Contrapposto* as stillborn (to stay with the anthropocentric jargon). If some new kind of non-human creative agency was indeed present in this work, then the artist's production of the sculpture signals its extermination and embalming. The work, in this sense, undermines its own claims regarding the existence of non-human expressive agency, which is here immediately objectified in a clearly human-made, unique, commercially traded aesthetic artefact. Through this contradiction, the work nevertheless resonates with the conceptual rubric that organizes this book, and the sculpture offers a useful staging area for some of the central concerns I want to unpack in what follows. Where is the line between the anthropomorphized AI-system-as-artist and other, more radical new kinds of non-human expressive agency? How can digital art practices that draw on AI push beyond merely instrumentalizing the technology for the perpetuation of existing, anthropocentric views on authorship in new contexts?

What my brief discussion of *Mosaic Virus* and *Contrapposto*  has not touched on so far is the problem of explicitly locating "authorship" across aesthetic, socio-economic, and legal contexts. I expect that most (human) audiences would approach the two foregoing examples as aesthetic artefacts that were clearly

created by humans. Does this mean that their invocation of **21** non-human agency is merely a rhetorical conceit, a story that is being told about AI? In the following chapters, I will locate a key critical potential of AI in its ability to highlight and foreground generative processes that structurally undermine conventional assumptions concerning the integrity and centrality of human creative agency and authorship. Again, such an undermining does not necessarily imply that AI systems can become authors or artists. But it certainly helps with clarifying the contradictions inherent in claims of AI creativity that are themselves modelled on anthropocentric, humanist frameworks of authorship. Such issues become particularly interesting in the context of competing (human) ownership claims linked to AI-generated artworks, and in legal disputes in which human artists may wish to deny their own creative agency by designating AI systems as authors.

#### **AI and Creativity in the Intellectual Property Milieu**

To rethink authorship in AI contexts inevitably requires a reassessment of key assumptions concerning the nature of creative expression. To my mind, influence, imitation, copying, and reusing are at the core of all artistic practice and (human) creativity. The lines between mimicry and mimesis are blurry, and the most fascinating works of art often emerge out of approaches that entail appropriation, repetition, or iteration. Such approaches are well established and highly visible in everything from digital remix culture and audio sampling to appropriation art and the collage arts of the earlier 20th century. Appropriation-based artistic techniques in particular have always had a tendency to provoke much controversy because they challenge the socio-economic logic of authorship-*qua*-ownership, as well as the humanist ideals based on which intellectual property itself is conceptualized. This includes IP's emphasis on originality and uniqueness as imprints

**22** of aesthetic and economic value, as well as its tight restrictions over how the knowledge and value bound in creative expressions can circulate. Where art transgresses narrowly conceived ideals of cultural ownership, IP law is quick to step in. Copyright, with its emphasis on originality and exclusive authorship rights, is a prime example of the conceptual flattening that occurs when creativity, in my view a profoundly dynamic and relational phenomenon, is reshaped to fit property-oriented paradigms that position the individualized human agent (or its corporate proxies) as the "natural" owner of creative expressions.

The digital is at strong odds with such paradigms for many reasons, not least because of how fundamentally (and obviously) digital technologies rely on processes of copying and reusing. Notably, such processes also figure importantly in many of the generative behaviors that characterize AI, including the underlying "training" and "learning" techniques. I will argue, therefore, that in AI contexts the issues outlined here can be significantly amplified. As I will discuss in a later chapter, legal theory is beginning to consider, for example, whether the assembling and use of training datasets constitutes large-scale IP rights infringement similar to the unauthorized publication of copyrighted texts—even when the data in question remains blackboxed within AI systems and never takes "human-readable" forms. All of this becomes even more complicated (and more interesting) when AI-generated outputs are interpreted on the basis of anthropocentric ideals of originality and creativity. Then, the question is not only whether certain uses of AI may infringe the copyrights of human authors, but also whether IP paradigms could technically apply to AI systems themselves, so that these might have to be recognized as authors/artists, or will, at least, challenge the integrity of prevailing perspectives on the central role and status of the human author/artist. The functionality and outputs of GANs are particularly interesting in this context, since they tend to invoke "AI creativity" very compellingly even though

underlying generative processes, as I will argue, expose such **23** systems as new kinds of highly sophisticated copy machines.

While IP law is becoming an increasingly important tool for negotiating the role of AI in the evolving socio-economic landscape, it is also becoming clear that AI can serve as a tool to elude the limits IP law has traditionally sought to impose on creative practices which involve copying activities. In this sense, AI art is in a good position to extend and expand the trajectory of appropriation art and other artistic approaches that stand opposed to the property-oriented logic of authorship, and which have long engaged critically with the reproducibility of information and aesthetic artefacts. If human artists' practices of imitation, copying, or reproduction are thought of as likely breaches of IP restrictions, then there can be no doubt that generative AI technologies are increasing the critical stakes of such practices. As I will discuss in the chapters to follow, this is already triggering interesting controversies surrounding AI-generated artworks that challenge ideas of the traditional (human) artist figure and their exclusive rights, of the nature of the work of art, and of the art market as such. AI, in other words, has the potential to substantially disturb IP policy and political economies that have formed around the romantic ideal of the author as owner.

There is now a considerable body of research that links IP theory and AI discourse, yet there is no consensus, among legal scholars and cultural theorists of AI and IP, about whether artificially intelligent systems are capable of autonomously producing the kinds of creative expression to which IP law could conventionally be applied. Widely available mainstream AI tools used for generating novel digital imagery—for example AI apps capable of style transfer (see Yuan 2018) or GAN-style image generation tools (see ArtBreeder n.d.)—seem to indicate that AI is limited, at best, to the emulation of creativity in the anthropocentric senses of the term. When Nigel Shaboldt, the co-founder of the Open Data Institute, was asked if he believed that computational systems can be creative, he is said to have deflected the question **24** by responding, "machines will become adept at persuading us that there is indeed something behind the façade" (Miller 2019). Matteo Pasquinelli (2019), a key cultural theorist of AI, is similarly skeptical when he argues that state-of-the-art AI technologies have little to do with creativity as such, and are better understood as highly sophisticated tools for statistical analysis.

Such criticism notwithstanding, there are also those who herald the coming of truly "creative" AI. In one of several recent monographs focusing directly on this subject, the mathematician and popular science writer Marcus du Sautoy frames creativity as a kind of "human code" (2019) which, in the author's opinion, can be computed algorithmically with such efficiency that machine creativity must eclipse human creativity sooner or later. This perspective aligns problematically with what James Bridle (2018), echoing Bernard Stiegler and others, has criticized as a pervasive form of "computational thinking," i.e., the application of computational logic to efforts of solving hard problems such as developing artificial intelligence and computer-based creativity. Indeed, computational thinking goes far beyond a basic understanding of algorithmic processes as "methods for solving problems that are suited for computer implementation" (Sedgewick and Wayne 2011, 3); sometimes criticized as "computationalist," it can pander to generalizing assumptions concerning the abstractability and programmability of human behavior, in ways that ignore or overlook crucial political issues (for influential contributions to such critiques see, for example, Dougherty 2001; Hayles 2005). AI systems certainly already are what Ed Finn has called "culture machines" (2017), at least in the sense that they contribute importantly to the socio-cultural fabric in which they are embedded. But this acknowledgement says little about whether creativity should indeed be considered as "effectively computable" (cf. Copeland 2020), i.e., whether AI systems could, at least theoretically, match the creative capabilities of the current top-of-the-line culture machine, the human agent itself.

Inevitably, serious consideration of issues related to creative AI, **25** and to computational creativity more generally, requires that some fundamental questions regarding the nature of intelligence must also be revisited. In cultural contexts, this is of course precisely what makes the concept of AI so compelling. Thus: what is meant by creativity in relation to AI, and what metaphysical, ontological, and hermeneutical frameworks are invoked to address the concept in such contexts? How do new perspectives on AI-inflected creativity function in conjunction with the prevailing mix of aesthetic theories, socio-cultural norms, economic policy, and legal regulation concerning creative expression? Zylinska suggests that in much current AI research, the underlying concept of intelligence is somewhat of a blind spot, "either taken for granted without too much interrogation or molded at will and then readjusted depending on the direction the research has taken" (2020, 19). A similar observation applies to the ways in which the concept of creativity is invoked in AI contexts, where it is defined and used across human-exclusive and AI-specific contexts. For example, two prominent researchers of creativity, the psychology and human development researcher Robert J. Sternberg and the cognitive science scholar Margaret A. Boden, rely on similar sets of criteria in their definitions of creativity (these revolve, in a nutshell, around novelty, originality, unexpectedness, and usefulness), even though Sternberg's work (2011) focuses on topics such as imagination, wisdom, and love, while Boden (1990) is a key reference for computer scientists' efforts to program creativity in computational systems. My own approach to this issue will be to adopt a mix of prevalent definitions, and accept as creative those behaviors, phenomena, activities, and artefacts that can be meaningfully interpreted as such in the aesthetic, cultural, socio-economic, and legal milieus which creativity and art are understood to inhabit (more on this in the following chapter). This, I hope, is helpful for developing a perspective that does not discriminate by default between human and nonhuman actants when navigating the diverse meanings and critical valences of creative expression in relation to digital art and AI, or,

**26** likewise, in relation to the philosophical and legal perspectives that frame them.

As I have already suggested, any claim that a given artwork was created not by a human artist but instead by an AI system must contend, at least speculatively, with the existence of an AI author/artist. If taken seriously, the notion of creative AI must then be seen to potentially undermine humanist ideals and romantic fictions of the genius artist (human, of course) and their (historically: his) unique ability to create original, inspired artworks. Given the larger context of my discussion, this is important because of how these same ideals have long served to justify and perpetuate IP models. In brief, the law tends to link intellectual ownership claims in an aesthetic artefact to the (human) individual from whom it is found to have originated. The intellectual or creative labor exerted in the production of such an artefact generally entitles its author to a range of exclusive rights. Critical uses of AI in artistic contexts can disturb the integrity of all these assumptions significantly. In other words: when an AI system isn't just understood as a tool used by human artists, but as an agential entity (or an assemblage of such entities) capable of "creative" expression, this then problematizes not only aesthetic assumptions regarding the nature of creativity and authorship, but by extension also socio-economic and legal assumptions regarding the ownership or, indeed, the very "ownability" of such expressions.

#### **Overview**

Throughout this book, the discussion of how artists working with AI link questions of creativity and agency to issues of cultural ownership forms a key part of my effort to sketch out a critical theory of AI art. As noted, my focus in this is on AI art's potential for disturbing property-oriented frameworks that emerge out of humanist perspectives. Central to this project is the observation that key underlying concepts—AI, agency, creativity,

ownership—are themselves far from stable. AI art, I argue, can **27** seize on this instability, for example in critiques of dataset bias, of AI's impact on privacy, or of algorithmic governmentality. The targets of such critiques themselves may be obscured by extreme algorithmic complexity, blackboxed in proprietary technologies, or packaged in code and algorithms that operate at a scale that is inaccessible to human cognition, or which are simply not human-readable. Digital art can cross these thresholds and give more accessible shape to these issues and the underlying technologies. As I will argue, it can do so by approaching AI tactically, by appropriating it, and by redeploying it to different, critical ends. Throughout the following chapters, I will thus discuss the becoming-tactical of AI in critical artistic practice as a development that mobilizes AI's emergent capabilities for interrogating, exposing, problematizing, and challenging the aesthetic, ideological, or technological frameworks driving the commodification and propertization of creative expression.

While this book is a fundamentally interdisciplinary endeavor, different chapters will focus on different elements of the broader discussion. This means that while the chapters add up (I hope) to a multifaceted whole that integrates arguments, problems, and perspectives from very different areas of theoretical inquiry and artistic practice, they can also be read individually. As such, chapter 2 and chapter 3 primarily (but not exclusively) deal in theory, and address ontological and definitional problems of agency, creativity, and ownership across the realms of philosophy, legal theory, and posthumanist thought. Chapter 4, chapter 5, and chapter 7 comprise in-depth analyses of separate AI art projects, to explore how these projects engage critically with the issues raised in the preceding chapters. Chapter 6 offers a counterpoint to this discussion, and considers how tactical aspects of critical AI practices can also manifest in strategic inversions, specifically in corporate AI applications in the digital culture mainstream. Chapter 8, finally, brings my discussion to a conclusion in sketching out a speculative framework for a

**28** posthumanist cultural commons that could accommodate the critical approaches to AI outlined throughout.

To offer a slightly more detailed summary: Chapter 2 begins to unpack what a critical art of AI could be. This includes theoretical elaboration on some key concepts that play into my overall project, including those of agency and creativity, as well as my attempt to define AI in the specific cultural context of contemporary digital art-making. This is framed with reference to relevant aspects of posthumanist theory, and with a more detailed introduction of my concept of the posthumanist agential assemblage, which, in the broader context of my discussion, is meant to offer a way to push AI art beyond the humanist subject of the singular, unified artist, their individualized voice, and their original and uniquely spirited creative expression. The chapter concludes with an initial discussion of tactical approaches to working with AI. This includes a brief analysis of Kate Crawford and Trevor Paglen's collaborative project *ImageNet Roulette* (2019) and its highly effective critique of dataset bias, which offers a good segue to beginning a broader critical discussion of how agency is construed in and through AI.

Chapter 3 begins with a brief discussion of Michael A. Noll's *Gaussian Quadratic* (1963), a generative work that here serves as an early example of the fraught interfacing between computer art and copyright law. This sets the stage for unpacking the interrelated foundations of agency, personhood, and ownership in legal philosophy, with a focus on theories of IP in general. Also included is a consideration of how property itself is conceived philosophically, as well as some explanatory commentary on main histories of copyright. The chapter then surveys key issues of authorship in legal research on presumptively "creative" AI, highlighting in particular the anthropocentric biases that frequently characterize such work.

Chapter 4 extends these specific concerns to an in-depth discussion of the controversy surrounding *Portrait of Edmond Belamy*

(2018), an AI-generated artwork that was auctioned off, for the **29** record sum of US\$432,500, on behalf of a French group named Obvious Collective, amidst allegations that the work's code base had actually been taken, without due credit, from the young AI artist Robbie Barrat. My focus here will be on spectacularizing claims that the work was "not the product of a human mind" but "a work of art created by an algorithm" (Christie's 2018). Several aspects of the work itself contradict this claim, which is, in fact, conceptualized and produced in problematic alignment with humanist notions of authorship and cultural ownership. In light of the informal copyright and ownership debate that the controversy provoked, I conclude this chapter by considering whether a tactical use of generative AI could render an artwork "unownable" when the use of the technology makes it difficult to recognize a conventional author figure.

In chapter 5, I explore possible answers to this question by analyzing one of the first formal copyright infringement complaints involving a work of AI art. The work in question, the Canadian artist Adam Basanta's AI-driven "art factory" *All We'd Ever Need Is One Another* (2018), serves as an excellent example of a tactical approach to working with AI. Manifesting as what I call a posthumanist agential assemblage, it affords the human artist who designed it a certain level of "deniability" of expressive agency. In other words, the use of AI here destabilizes the anthropocentric concept of authorship to such a degree that the allegations of copyright violation leveraged against Basanta by another artist may become difficult to uphold.

Chapter 6 considers the conjunction of creative expression, AI, and the destabilization of authorship in a broader context. Temporarily stepping away from discussion of AI-driven digital art, in this chapter I analyze Content ID, the AI-based digital rights management system used by YouTube to enforce its copyright policy. In this context, the potential deniability of creative agency emerges not as an artistic choice (as in the previous chapter), but instead as a highly problematic curtailment. As I argue, Content

**30** ID shifts away from an enactment of emerging non-human agency in AI, and instead reorients itself toward the enforcing of human non-agency through AI. Again, this has far-reaching implications for how notions of authorship, ownership, and agency play out at the intersection of humans and AI.

Integrating insights and arguments from the preceding chapter, chapter 7 speculates on a broader-scale becoming-tactical of AI art, and considers in more depth some critical implications of the destabilization of humanist ideals in and through posthumanist agential assemblages. This discussion begins with an analysis of two projects by the British AI artist Jake Elwes, *Machine Learning Porn* (2016) and *Zizi* (2019), both of which enact a deliberate "queering" of AI and dataset politics. Foregrounding the dynamism of sexual identity and invoking issues of cultural ownership, these works draw on AI to problematize normative, anthropocentric discourse on established subject positions. My final example, *!brute\_force*, is an ongoing project by the Slovenian artist Maja Smrekar, which introduces canine intelligence into an experimental AI training regimen in order to explore co-constitutive qualities of human and non-human ontologies of agency and knowledge production. A powerful example of a posthumanist agential assemblage, *!brute\_force* goes to considerable lengths to create speculative systems of decentered, relational, and contingent subject positions, with the effect that questions of agency and cultural ownership are reconfigured beyond anthropocentric horizons.

From the co-constitutive human-AI-canine knowledge ontologies envisioned in *!brute\_force* to the defamiliarizing appropriation of AI functionality in *Zizi*, from the legal provocations of *All We'd Ever Need Is One Another* to the clumsy declamations of non-human creative agency in *Portrait of Edmond Belamy*, and from the uncloaking of AI bias in *ImageNet Roulette* to the problematic enactment of human non-agency in the Content ID system—what commonalities, productive contradictions, and critical potentialities reverberate across the examples discussed

throughout this book? The final chapter considers the new con- **31** ceptual space that is constituted by the becoming-tactical of AI in posthumanist agential assemblages. In revisiting my earlier suggestions that certain uses and outputs of presumptively agential AI can be fundamentally incompatible with anthropocentric perspectives on creativity, originality, and authorship, I conclude by sketching out the concept of a posthumanist cultural commons, and by considering how—and to what critical ends such a commons could contain the works and workings of the posthumanist agential assemblage.

# **[2] What Does AI Hack? Scaffolding for a Critical Art of AI**

My aim in this chapter is to consider in more detail what a critical art of AI could be. This entails some additional unpacking of key concepts that play into my project, including those of agency, creativity, and the notion of AI itself. Framing this discussion with reference to contemporary perspectives on agency as fundamentally relational and distributed then leads me to introduce the concept of the posthumanist agential assemblage. Throughout this book, this concept will offer a way of exploring AI art and its critical implications beyond the humanist subject, and without having to rely on the restrictive conceptual framing of the traditionally singular, unified artist figure, their individualized voice, or the original and uniquely spirited creative expression. To locate AI art in the works and workings of the posthumanist agential assemblage also makes it easier to specify how a critical art of AI may disturb property-oriented frameworks derived from humanist perspectives. This initial discussion of the posthumanist agential assemblage sets the stage for exploring, in the next chapter, how questions of AI agency play out in legal theory, where they connect much more directly to issues of authorship,

**34** personhood, and ownership. Following a brief discussion of Kate Crawford and Trevor Paglen's critical AI art project *ImageNet Roulette* (2019), I conclude this chapter by defining what I will call "tactical" approaches to utilizing AI in artistic practice. Not all of the AI art projects discussed later on constitute posthumanist agential assemblages, nor do all of them operate in a tactical mode; all of them, however, will be considered in relation to these concepts, and the speculative horizon of a critical art of AI as it is laid out here.

To begin, I want to argue that to engage critically with AI, whether in digital art or in a theoretical capacity, is likely going to involve a conceptual and/or practical "hacking" of AI. For example, in a call for proposals for a workshop held at the 2020 Transmediale Festival, contributors were invited to adopt an "adversarial" approach, and "break into the technological abstractions of AI" (Transmediale n.d.) in order to consider what it might mean to produce new knowledge and critical literacies about artificial intelligence. Similarly, *Ways of Machine Seeing* and *Machine Feeling*, two event series organized at Cambridge University's Centre for Research in the Arts, Social Sciences, and Humanities (CRASSH) between 2017 and 2019, sought to integrate practical and critical approaches co-developed by computer scientists, researchers, and artists, and encouraged critiques of AI that may also directly utilize the technology (see Impett 2017; Andersen and Cox 2019). I fully agree that AI, like all emerging technologies, should be subject to every manner of hacking efforts by activists, artists, researchers, engineers, and so on. It should be subjected, in other words, to theory- and practice-based structural analysis and ideological critiques that may materialize in speculative alternatives to existing AI implementation and development approaches. It strikes me that a good point of departure for doing this is to approach the issue from an inverse angle—by asking what is already being hacked in conventional uses of AI, or, to put it differently, what AI as such is capable of hacking. Among the art projects discussed in the following chapters, several approach AI

from precisely this kind of angle, and go on to explore what hacks **35** are in turn made possible by divergent uses of AI.

To elaborate some observations and arguments already introduced in the previous chapter, I begin my discussion of critical AI art by offering two speculative claims concerning artificial intelligence, which converge in a third: AI hacks agency, AI hacks creativity, and AI hacks ownership. (All three require a fairly selfevident disclaimer, namely that these hacks are not achieved, of course, by AI "itself," but rather through practices that AI technologies facilitate.) In the most straightforward sense, the first claim, that AI hacks agency, can mean that AI is becoming a standard element of algorithmic control regimes. The validity of this claim is easy to test in various contexts, including the AIdriven manipulation of consumer behavior (e.g., recommendation algorithms), the state-level use of facial recognition technologies, or the use of AI-based predictive technologies in the financial and insurance sector. In all of these areas, human agents' ability of self-determination and politically informed expression—what Bernard Stiegler, in his last book, called "dreaming" (Stiegler 2019)—are impacted and, to varying degrees, curtailed. This links to the second claim, namely that AI hacks creativity. By this I mean that emerging AI technologies impact how creativity is defined in a general sense, how specific creative practices are reconfigured as a result, and how shifts in the production and circulation of creative expression are consequently negotiated and regulated. All of this manifests, for example, in debates around whether AI and its outputs could be claimed to be creative (or to be capable of becoming creative).

Where these two claims regarding the hacking capabilities of AI converge, a third can be formulated, namely that AI hacks ownership. In other words, a potential becoming-agential and becoming-creative of AI challenges key assumptions regarding humanist foundations of the concept of authorship and the nature of the work of art, and by extension also assumptions concerning how these foundations underpin current IP regimes. **36** For one thing, speculation on the emergence of artistic AI systems that could soon compete with human artists suggests that it may become more challenging to assign authorship (and enforce ownership rights), and therefore also to maintain the integrity of these concepts. For another, ongoing efforts to make creativity machine-readable can enable AI systems to interpret, validate, and consequently control how creative expression circulates digitally. This can already be observed in examples such as the use of AI-based digital rights management (DRM) tools for enforcing corporate copyright policy (the main topic of chapter 6). As a consequence, the ability of a given agent (whether human or non-human) to express themselves creatively and critically, to access knowledge and information as part of expressive activities, and to participate freely in the production and circulation of art and culture, could be severely impinged. It is here that AI's impact on the question of agency and of creativity links most directly to current IP models. As I will argue, the various hacking capabilities of AI can be utilized strategically, i.e., in alignment with such models, but also in opposition to them. I describe the latter as tactical uses of AI, and will conclude the chapter by elaborating this notion, both as an extension of existing discourse on tactical media, and in the context of the kinds of critical AI art projects discussed throughout the book.

#### **Redefining AI in and through Artistic Practice**

AI is a strange, chimera-like beast. Arguably, it is as much a science as it is a technology, as much a cultural phenomenon as it is a philosophical construct. And while AI can be said, quite plausibly, to be all of those things, it may also be argued that it is really just mythical fabulation, that it does not exist at all. While some argue that what is now called AI will continue to inch closer to a full exteriorization of "general intelligence," and then on toward a "singularity" event (Kurzweil 2005), others insist that AI will remain a computational augmentation of human intelligence (as discussed in Markoff 2015). The difficulty of

definitively describing artificial intelligence is also complicated by **37** the so-called "AI effect." This occurs when technological advances lead to newly emerging behaviors of AI systems that would once have been thought of as proof of intelligence, but which are no longer accepted as such because the underlying computational processes are understood better (cf. McCorduck 2004). In other words, as the capabilities of specific AI applications are moving into the mainstream or are superseded by new developments, they are no longer seen as satisfying existing definitions of intelligent behavior, so that AI always remains an elusive "n+1". At the 2019 Ars Electronica festival, the media art historian Andreas Broeckmann suggested that it might indeed be best to use scare quotes whenever "artificial intelligence" is invoked, while the media theorist Joanna Zylinska describes much of AI as "a socalled intelligence" (2020).

But regardless of how broadly or narrowly AI is conceived, and regardless of whether "Artificial General Intelligence" (AGI) is framed anthropocentrically in terms of "true," "strong," or "weak," or rather as something that human intelligence cannot even begin to fathom, there are some general characteristics that find broader, interdisciplinary agreement. For example, a general definition of AI that integrates widely held perspectives from the technical and social sciences as well as the humanities is likely to focus on the description of algorithmic tools that are capable of generating knowledge of some kind, often on the basis of iterative and generative processes that involve largescale analysis of datasets on which AI systems are trained. It is also generally accepted that machine learning technologies are increasingly responsible for how humans perceive the world, how they interact with it, and, consequently, who (and how) human agents can be in a world with AI. Definitions nevertheless diverge widely across different research domains. An influential definition by Russell and Norvig (2009) hints at this divergence nicely: their definition situates AI in a four-dimensional spectrum to consider whether any given "intelligent agent" is designed to think

**38** (or act) like humans, or, rather, to think (or act) rationally. While the work of Russell and Norvig has tended to focus on rational (non-human-based) action (as opposed to thinking), the authors acknowledge that more informally, the term AI is often used to describe systems that cross over between these definitional boundaries in direct imitation of functions associated with human cognition. Notably, all of this leaves considerable room for very different kinds of philosophical, technical, and ideological viewpoints.

To extend these broad characterizations into the cultural sphere and connect them to my discussion of critical artistic practice, I venture the following definition of AI: I will describe as artificially intelligent any assemblage of technologies, operations, functions, and effects that can be meaningfully perceived as resulting from intelligent (including creative) behavior, or which can be identified in outputs that are the results of such behavior. This formulation is deliberately inclusive, open-ended, and focused on perception. While it allows for the designation "AI"—with or without scare quotes—to be applied to technological systems such as advanced computational neural networks that approximate functions of the human brain, it can also describe the effects created when the behaviors of an otherwise dull machinic system are perceived as intelligent, or when its outputs are interpreted as creative. My definition therefore acknowledges that while inevitably there must be a non-human dimension to artificial intelligence, AI is nevertheless contingent upon human perception and interpretation. Much like the co-originary nature of the human and the technical (cf. Stiegler 1998), AI is, in other words, co-constitutive with human intelligence. Further, because my discussion is framed by an interest in how AI interfaces with realms of art, culture, and politics, my perspective disregards the notion of AI as unfathomable for human cognition, and I emphasize, instead, that AI is in the eye of the beholder. Even so, my definition is positioned both against the anthropomorphizing of AI as humanlike, and against anthropocentrically biased definitions that

perceive AI as existing in a position of subservience to human **39** users. Importantly, I also want to avoid thinking of AI as bounded in any singular entity, such as an algorithm or a computer. This helps to accommodate the many ways in which AI is continually rethought, redefined, and newly instantiated in and across emerging technologies, new tools, as well as new ways of using and critiquing these. Most importantly, this leaves room for a final core element of my thinking on AI, namely that it can be understood to manifest in posthumanist agential assemblages that cut across human/non-human divides, and which may incorporate human (and other living) entities alongside computational or machinic ones.

A broad definition such as the one just offered isn't likely to satisfy everyone. Much more precise definitions can be found in virtually every field of research on which my own discussion draws, from philosophy to computational creativity, from aesthetic theory to legal philosophy. My definition has the advantage, however, that it can accommodate the broader interdisciplinary focus through which I link agency and ownership issues to AI. Where necessary and useful, my broad conception of AI will, in any case, solidify into much more specific definitions, for example when discussing how artists use machine learning technologies such as Generative Adversarial Networks (GANs), or when considering the blackboxing of proprietary algorithms used for digital rights management (DRM) purposes. Ultimately, dipping in and out of the wide range of available technical definitions of AI makes it easier to connect them to the philosophical, aesthetic, legal, and political strands of my discussion, and to show how this can be incorporated into the self-reflexive and meta-discursive critiques that critical AI art leverages.

In discussions of AI, it is sometimes far from clear what exactly "intelligence" itself signifies. It might be objected, for example, that my own definition of AI can accommodate not only "true" intelligence (however that might be construed), but that it could also apply to the mere imitation or simulation of intelligent

**40** behaviors or effects. Such a concern was well articulated in the philosopher John Searle's famous "Chinese Room" argument (cf. Searle 1980 and Cole 2020), which concerned the difficulty of differentiating between the correct-but-meaningless manipulation of linguistic symbols and an actual understanding of these symbols. The thought experiment describes how an entity, concealed in a room, might convince an observer on the outside that it knows the Chinese language, even though it remains impossible for the observer to determine whether this knowledge expresses intelligence or merely a mute ability to convincingly simulate intelligence.

Importantly, this distinction loses much of its relevance when the focus is placed simply on whether the entity concealed in the room can be perceived, for all intents and purposes, as displaying intelligent behavior. Even if true non-human intelligence were understood as being so profoundly different from our own that it could become unknowable for humans (as discussed in Bown 2015 and Bridle 2018, among others), what Searle's example suggests is that I can only ever relate to AI through an inflection of my own, inevitably human-centric, understanding of what determines intelligence. In the baseline definition of AI that I have proposed above, it is for this reason that to identify AI means to interpret perceivable functions, effects, or behaviors as intelligent. This acknowledges that the theoretical frameworks I invoke are themselves interpretive paradigms, and it highlights that I am, of course, speaking from the perspective of human agency. To acknowledge this is also to open up AI toward the integration of posthumanist thought, where, as I discuss below, the agencies in which "intelligence" is grounded are understood as relational, dynamic, and multi-dimensional.

I have noted earlier that anthropocentric configurations of AI may manifest in surveillance technology, in financial applications, and in new algorithmic legal tools, where they build on and, indeed, amplify ideological frameworks that privilege (and also exploit) the human agent and the presumptively singular subject

position it is often understood to inhabit. Key to my overall **41** discussion is the observation that something similar applies when presumptively "creative" AI is framed in anthropocentric terms that derive from longstanding notions of the singular, unified artist figure: the anthropocentric instrumentalization of AI in copyright contexts, for example, will ever only be able to enforce IP perspectives in which the human author/artist figure functions as the singular originator and proprietor of humanmade creative expression. Such an instrumentalization could hardly accommodate non-human authorship of—or ownership in—creative expressions.

To elucidate this, I offer two brief examples that will undergo closer analysis in subsequent chapters. The first concerns the Canadian artist Adam Basanta's AI-driven installation *All We'd Ever Need Is One Another* (2018). Building on approaches familiar from appropriation art, the installation is designed to autonomously generate outputs and to circulate them as artworks if, and only if, they are determined by an AI system to closely resemble other existing artworks (see chapter 5). While this approach aligns with progressive views on creativity as multi-modal and relational, unsurprisingly it contradicts perspectives that are focused on identifying single (human) authors for unique artworks. And indeed, *All We'd Ever Need Is One Another* has provoked a legal complaint alleging that the installation violates another artist's IP rights. Given the prominent role of AI in the generative process, my discussion of this case speculates that Basanta himself cannot be credibly accused of copyright infringement, and I argue that the accusation itself makes sense only as long as it hinges on an anthropocentric perspective on AI, authorship, and creativity. The second example is situated at the interstices between creative expression and commercial AI applications, and concerns You-Tube's AI-driven DRM system Content ID (see chapter 6). This tool is designed to flag and remove presumptively infringing media uploads by enforcing the assumption that creative expression is incontrovertibly linked to ownership claims based on identifiable

**42** human authorship. By enacting this view algorithmically, Content ID helps to integrate uploads into the platform's for-profit, property-oriented media ecology. Additionally, it normalizes the highly problematic notion that complex legal norms concerning authorship, which are themselves intimately tied to long histories of philosophical discourse on the subject, are effectively computable. On this basis, Content ID disavows more complex configurations of creative expression and agency, which it consequently prevents from freely circulating.

The example of Content ID indicates that AI is capable of enforcing the notion of singular human agency, and in doing so of impeding divergent forms of creative expression that draw on relational, multi-modal, recombinant models. The example of *All We'd Ever Need Is One Another*, in turn, suggests that AI experiments which go beyond the anthropocentric paradigm of singular human creative agency can disturb the logic of the ownership models which systems such as Content ID are designed to safeguard. The project frames a new kind of AI-driven creative expression that no longer fits (conceptually or practically) with the familiar idea of unique expressions that are uniquely linked to artists/authors/owners, and which effectively challenges anthropocentric ownership models that tend to be amplified in current AI applications. My discussion of this kind of AI art espouses a move toward a position that does not privilege human agency above its various non-human interlocutors, and in this sense Basanta's project will serve as a good example of what I describe as a posthumanist agential assemblage.

#### **Whence and Whither Agency?**

In all of this, agency reveals itself as a rather slippery idea. There are numerous ways in which the concept has been defined across different strands of philosophy, law, psychology, and neuroscience—not to mention poststructuralism, actor-network theory, postcolonial theory, feminist theory, and queer theory,

among others. Most basically, agency denotes the manifestation **43** of a capacity to act (see, for example, Schlosser 2019, on which this brief summary in part relies, and Chopra and White 2011, who try to develop a legal theory of autonomous artificial agents). But things quickly get a lot more complicated. Contemporary philosophical perspectives on agency tend to take as a common starting point the "standard theory of action" (cf. Davidson 1980), which considers actions in terms of their intentionality. The theory assumes that an entity is capable of exercising agency if it is also capable of acting intentionally. In more detailed analyses of intentionality that also consider causation, will, or motivation, this leads onwards to theories of distinctly human agency (e.g., Frankfurt 1971; Taylor 1977).  But further obstacles appear when additional underlying questions are considered: What is an "agent," in this sense, and what defines an "action"? What level of autonomy is required to warrant the designation "agential"? Who or what can be the judge of intentionality? Once agency becomes vested in human beings, it also becomes necessary to ascribe and account for mental states, as well as to consider socio-political dimensions of autonomy, conscious deliberation, and reasoning. It is here that human agency can easily become separated hierarchically from other, non-human forms of agency, resulting in a normative formalization of anthropocentric bias. Therefore, when the agency of artificially intelligent entities is considered, the standard theory of action, with its strong focus on intentionality and, by extension, mental states, becomes somewhat of a hindrance, rather than an aid, with the consequence that advanced AI systems might inevitably be relegated to the "lower" status of mere "minimally agential" entities (e.g., Barandiaran et al. 2009).

The views on agency I have presented so far are limited in the sense that they leave little room for considering the political dimensions of agency in socio-economic contexts. Their applicability to AI systems is also limited, and they certainly cannot easily account for an understanding of AI systems

**44** as "culture machines" that reach beyond anthropocentric paradigms. In this sense, it is the idea of AI itself that casts doubt on the centrality of the human when it comes to questions of agency. This notion reverberates powerfully through posthumanist perspectives. Here, agency is no longer perceived as the exclusive domain of human subjectivity, nor even of animate bodies. Bruno Latour, for example, in formulating his actornetwork theory (ANT), heralded the use of the term "actant," which he defined as "something that acts or to which activity is granted by others," and which "implies *no* special motivation of *human individual* actors, nor of humans in general" (Latour 1996, 373, emphasis in original). New materialism, to give a different example, ascribes "vitality" to inanimate matter that has the capacity, as Jane Bennett writes, "not only to impede or block the will and designs of humans, but also to act as quasi agents or forces with trajectories, propensities, or tendencies of their own" (Bennett 2009, viii). In extending these perspectives, some posthumanist strands of thought altogether veer away from the notion of a unified and singular host of agency, and instead foreground the fluidity and open-endedness of the assemblage or apparatus that comes to replace the once-singular agent. The quantum physicist and feminist theorist Karen Barad (2007) thus proposes an "agential realism" in which agency radiates not from a singular host but emerges through "intra-actional" entanglements that cut across the various materialities which form agential potentials. In much of this, boundaries between the human and non-human are entirely rejected, and unsurprisingly, this also broadly rejects humanist attitudes inscribed in familiar anthropocentric figurations of agency (cf. Braidotti 2013).

Taken together, these perspectives contest the notion of agency as clearly delineated, stable, and singularly grounded in the human subject. Agency here becomes relational, porous, fluid, and distributed. Where humanist and anthropocentric perspectives persist, they take form, for example, in the legal status of personhood. As a specific instantiation of (mostly) human

agency, this anchors many of the rights and responsibilities of **45** contemporary agential subjects, from the right to free expression to the ability to own property. But here, the dynamism and inclusivity of posthumanist perspectives is nowhere yet to be found (unless one were inclined to accept the concept of corporate personhood as a radical decentering of human subjectivity). What is noticeable, however, is that anthropocentrically conceived norms and limits of agency are today increasingly upheld through AI technologies that are also potentially capable, as I argue, to disturb these same norms and limits. I have already mentioned examples of how non-human, computational entities can be instrumentalized to perpetuate narrow conceptions of agency in surveillance, governance, and IP contexts. It is precisely against this backdrop that AI must be engaged critically, to rethink what is meant by agency as such, and to explore radical implications of non-human expressive agency enacted through AI. The speculative scattering of agency across human-machinicalgorithmic assemblages instantiated in critical artistic experiments with AI are an ideal vehicle for just this kind of rethinking.

It is here that the critical potential of the posthumanist agential assemblage becomes more apparent. The concept offers a way to push AI—and AI art—beyond the humanist subject of the singular, unified artist, their individualized voice, and their original and uniquely spirited creative expression. In the works created by and with such assemblages, the artwork loses its humanist contours, while the dynamism and relationality of creative expression become manifest. And in their workings—in the operational logic, functionality, routines, protocols, and processes that characterize AI—the productive entanglements between different human and non-human elements find expression in manifold redistributions of agency across all participating elements. The works and workings of the posthumanist agential assemblage will continue to take shape throughout the following chapters. What all of the examples will be found to share is that they engage critically with the humanist horizons of a singular,

**46** unified agency and the various frameworks of cultural ownership and intellectual property that rely on these.

In making the leap from intelligence to creativity, it is worth looking a bit more closely at the entanglements of AI and agency in the Turing test, as well as a few of its successors-in-spirit. Alan Turing (1950) proposed his original test as a hypothesis concerning how human observers might identify a computer system as intelligent. Right away, the test's most notable characteristic should be emphasized here, namely that to identify a computer system as intelligent means to mistake it for another human agent. While Turing's setup is ingeniously simple, it also hinges centrally on a fairly obvious expression of anthropocentric bias. In response to this, as well as to eliminate how the Turing test potentially endorses the mere simulation of intelligence, Selmer Bringsjord et al. (2001) designed the hypothetical Lovelace test, which proposes that a computational system can be accepted as intelligent when it is capable of producing outputs that human observers will interpret as creative expressions. Bringsjord et al.'s test builds on Ada Lovelace's oft-cited suggestion that computers cannot be said to possess intelligence because they "have no pretensions whatever to originate anything" (Lovelace, quoted in Baum 1986). It tests, in other words, for qualities which computers, according to Ada Lovelace, cannot possess. In this setup, a human observer is tasked with judging whether the presumptively artistic output of a computer is, indeed, creative. This is found to be the case if the human observer cannot explain how an output was generated, even when it is verifiably not based on a mere hardware error (Bringsjord et al. 2001, 9). Like Lovelace herself, Bringsjord and his co-authors assume that this is effectively impossible, and conclude that (at the time of their writing) the hypothetical test was not likely to be beaten by any computer, in part because the machine would need to possess a "rather radical kind of autonomy" that they find difficult to imagine, let alone to design (ibid. 25).

The ambition of the Lovelace test is to improve upon some of **47** the Turing test's perceived flaws, in part by replacing a judge's subjectivity with a query regarding the algorithmic foundations underlying presumptively creative AI outputs. But it can be argued that the test merely replaces one subjective notion (an anthropocentric understanding of intelligence) with another (an anthropocentric understanding of creativity). This criticism can also be applied to more recent iterations of the Lovelace test. For example, based on the assumption that human creativity is fundamentally rule-driven and algorithmic, Marcus du Sautoy (2019) proposes his own Lovelace test, which is virtually identical to the version of Bringsjord et al. except for the slight twist that it should also be repeatable, i.e., not random. Lev Manovich's Turing AI Arts test, to give a different example, shares the goal of the Lovelace test(s) before it, but is modeled more directly on Turing's test design. Problematically, Manovich adds an elitist dimension to the anthropocentric bias of the original: in this version, the artistic nature of AI outputs is asserted only when "professional members of the art world recognize [them] as belonging to 'contemporary art'" (2019, 1). What these proposed tests share is that their determinations of non-human creativity and intelligence are made on the assumption that all creativity and/or intelligence will resemble that of human agents. They all propose that if the test is passed, the non-human entities that are being evaluated are elevated to a realm of agency that renders them, oddly, more human-like. Again, I would argue that such a perspective can hardly accommodate dynamic and relational manifestations of agency in a posthumanist sense, nor new forms of perceived intelligence or creativity that these could frame.

In this context, "pretensions" and "originate" jump out from the Lovelace statement quoted above. The terms imply that if an AI system were capable of passing the test, it would exhibit the intention of creating outputs *ex nihilo*. This not only invokes the interrelated humanist notions of creativity, originality, and authorship, but also links to the ways in which IP theory has

**48** conventionally defined the terms, for example for the purpose of determining the copyrightability of an expression. The privileging of a will to effect originality as a core prerequisite for creative agency is also implied by Bringsjord et al.'s formulation that AI creativity must satisfy the criterion of "unexplainability." In current AI discourse, the concept of unexplainability invokes the idea that the nature of non-human intelligence (as well as the internal processes that lead to AI-generated outputs) may be unknowable from a human perspective. But when it is linked back to debates about AI creativity, the concept of unexplainability also invokes humanist ideals of authorial agency.

An important development in the trajectory from Turing test to Lovelace test is that more recent perspectives no longer seem to focus merely on identifying intelligence as such, but also on the idea that creative expression may be understood as an embodied and authentic manifestation of agency. The idea that creativity is an important marker of intelligence certainly seems valid; what is problematic is that the various tests introduced above appear to suggest either that creativity is effectively computable, or that it must be framed anthropocentrically. In any case, it would appear that, following this trajectory, it can be assumed that if AI is found to be creative, it must also be found to have agency. If it is further assumed that creative agency will be exteriorized in a form that can be grasped and evaluated by human observers (namely in presumptively creative AI-generated expressions), then inevitably there also arise difficult questions concerning the moral, legal, and economic statuses which AI-as-artist (as opposed to the human artist working with AI) and the AI-generated artwork might have to be granted.

The question of whether AI can be creative interests me only insofar as it represents an opportunity to rethink what is actually meant by creativity, and how such a rethinking can impact the technological, socio-economic, legal, and, ultimately, ideological entanglements of emerging forms and constellations of creative agency. Since creative agency implies authorship, any assumption that AI can be creative is a return to the third claim introduced at **49** the beginning of this chapter, namely that AI is capable of hacking ownership, anthropocentrically conceived. It must therefore also be asked what ownership itself signifies in the context of agential AI. The conceptual underpinnings of the IP paradigms currently in place would seem to suggest that if AI can achieve human-like creativity, it should also be entitled, like a human author, to control the fruits of its labor. In mainstream debate on creative AI and AI art, the alarmist response to this suggestion tends to be that AI will disrupt the art market (see my discussion in chapter 4). I would argue that this should not be taken to mean only that AI will begin to "compete" with human authors, but also that critical AI art has a powerful opportunity to upend the value propositions of anthropocentric and humanist approaches to authorship. This can happen, for example, by emphasizing relational reconfigurations of agency and creative expression that move toward a decentering of the humanist (and human) subject that grounds anthropocentric property models.

The concept of property itself, even when it is narrowly conceived, always delineates a relational constellation (between owner and property, and between owners of property). Additionally, any notion of a property enclosure also entails, at least speculatively, the potential for a commons (I will return to this in my final chapter). As such, property rights, despite their notional exclusivity, are mostly situated in environments that delineate ownership as porous and malleable. The logic of IP, for example, situates copyright as a core mechanism through which the expressive agency of individuals solidifies into exclusive property rights. But this logic is recursive, since it is itself based on philosophical traditions (including Hegel's personality theory, discussed in the next chapter) that construe property as that which helps to solidify individuality, personhood, and thus agency. When critical uses of AI propose the emergence of posthumanist expressive agency, this effectively begins to undermine the recursive logic of IP.

#### **<sup>50</sup> Tactical AI**

A critical art of artificial intelligence appropriates AI's capability of hacking agency, creativity, and ownership, and opposes it tactically. In doing so it strongly contrasts with what I would describe as strategic uses of AI. The distinction between strategic and tactical practices has been most prominently theorized in Michel de Certeau's *Practice of Everyday Life* (2011). De Certeau's discussion of tactics in cultural contexts has greatly influenced the work of new media art activists such as the Critical Art Ensemble (2001), and even though the concept of tactical media, as theorized, for example, by Rita Riley (2009), fell out of fashion relatively quickly, de Certeau's concept of tactics continues to resonate with a broader canon of media theoretical work from the left, for example Alexander Galloway's (2004) discussion of control protocols in decentralized communication technology, McKenzie Wark's influential *A Hacker Manifesto* (2004), or more recently the manifesto published by the Critical Engineering Working Group (Oliver et al. n.d.).

In de Certeau's formulation, the purpose of strategy is to serve administrative and managerial agendas by drawing on systeminherent control architectures, often with the purpose of containing critical or divergent elements within a system. Examples might include the organization of work processes on a shop floor, or the algorithmic ordering of consumer behavior. Strategy is described as goal-oriented, and focuses on instrumentalizing the structural affordances of the environmental/social/technological substrate in which it is embedded. In this sense, strategic approaches to AI (and perhaps even strategic critiques of it) are likely to align with what I have earlier discussed as computational thinking, and strategic implementations of AI might manifest, for example, in applications that reinforce or amplify anthropocentric ideological frameworks in surveillance, finance, or IP contexts. In artistic contexts, strategic utilization of AI may employ the technology to reinscribe anthropocentric configurations of

the humanist artist figure and artwork. As examples, I would **51** count AI works such as *Contrapposto*, discussed in the introductory chapter, as well as *Portrait of Edmond Belamy*, which is the subject of my analysis in chapter 4.

Tactics, by contrast, are described by de Certeau as the recalcitrant and oppositional response to strategic operations. In de Certeau's thinking, to proceed tactically is to practice the "art of making-do," i.e., to operate from a position that is responsive, fluid, and embedded in the system or technology that is being challenged. In comparison to strategy, tactics is less goaloriented. It operates along open-ended vectors of resistance, rather than pursuing the ossification of a situation in strategically advantageous positions of dominance. As such, tactical approaches are associated with oppositional viewpoints, cunning, and shrewdness (see Scott 1998, discussed in Zeilinger 2017). In AI contexts, this can translate into approaches that "hack" AI in precisely the ways introduced earlier in this chapter. Tactical AI, in this sense, is likely to resist strategic approaches that blackbox knowledge, restrict access, or reinforce narrow conceptualizations of agency. In the given context of my general project, this also means that tactical approaches to working with AI align themselves with characteristics of the posthumanist agential assemblage, specifically its challenges to humanist frameworks of singular, unified agency and anthropocentric perspectives on cultural ownership.

Kate Crawford and Trevor Paglen's *ImageNet Roulette* (2019) is a good example of a critical work of AI art that proceeds tactically. Utilizing mainstream image recognition technology, the project was designed to expose bias inherent in the data used for machine learning purposes, and consequently also in the algorithmic procedures derived from them. As such, the project articulates a powerful critique of how agency and cultural ownership are construed in and through such systems (Crawford and Paglen 2019b). *ImageNet Roulette* (which, for reasons explained below, is no longer available for online viewing or use)

**52** took the form of a web interface that invited users to upload a self-portrait, which would then be evaluated and interpreted by a purpose-built image recognition system. This system had been trained on the ImageNet dataset, a compilation of over 14 million labelled images that includes everyday objects as well as portraits of individuals, and which is widely used for the development of machine vision applications by researchers, government agencies, and in corporate contexts (ImageNet 2016). Importantly, the initial labelling of the dataset contents had been carried out by clickworkers, (individuals who typically receive extremely low per-task remuneration for repetitive data-entry activities carried out remotely). The experience of interacting with *ImageNet Roulette* somewhat resembled the popular personality quiz apps that circulate on social media; in return for private user information, such apps customarily offer free and exhilarating feedback, such as pseudo-psychological profiles or astrological advice. Often, such offerings disguise app developers' ulterior motives of data-extraction for commercial or political purposes. In the case of *ImageNet Roulette*, a user's reward for uploading an image of themselves was to have the system analyze and evaluate the uploaded image, to potentially shocking effect.

The intention behind *ImageNet Roulette* was that as part of this evaluation process, it would become clear to the user that beyond the artifice of presumptively objective computational processes, artificially intelligent systems are in fact deeply saturated with human bias. The subjectivity and extreme bias ingrained in the ImageNet dataset meant that a "photograph of a woman smiling in a bikini" might be classified as a "slattern, slut, slovenly woman, trollop" (Crawford and Paglen, 2019b). To give another example, in a review of the project for *The Guardian*, a journalist of Asian descent found herself identified as "gook, slant-eye" (Wong 2019). By subjecting users to this kind of system-inherent offensive feedback, *ImageNet Roulette* very successfully managed to challenge popular assumptions regarding the presumptive objectivity and interpretive validity of computational processes;

to expose such processes, which are commonly blackboxed **53** or otherwise not human-readable; to raise awareness of the exploitative labor practices behind biased labelling practices; to highlight data mining practices that link machine learning and private user information; and, by implication, to raise ownership issues related to dataset compilation practices and "big data" more generally.

Exposure to the hard-coded bias of the ImageNet dataset confronted users of *ImageNet Roulette* with the realization that outputs generated by non-human computational systems still express the all-too human prejudices injected into such systems, and that these systems are themselves part and parcel of the ideological frameworks that produce them and within which they circulate. In the context of discussing tactical approaches to AI, the achievement of Crawford and Paglen's project is to radically disturb the public image of machine learning technologies by appropriating and repurposing the data that forms the subject of the critique. As such, the project made it easy for non-specialist users to grasp that while the non-human, algorithmic evaluation of image characteristics cannot avoid human bias, such technology is certainly a good way of obfuscating and disguising this bias.

*ImageNet Roulette* thus forced an awareness that presumptively agential algorithmic systems can powerfully deny human users their own agency; in the case of this project, the evaluations received in response to uploading an image were likely to violate a user's sense of ownership with regard to their self-image, identity, and the interpretation thereof. Like the AI-based evaluative tools mimicked by *ImageNet Roulette*, the project itself flaunted a seemingly objective capability to produce new and valid knowledge about human users. But because *ImageNet Roulette* simultaneously worked to undermine this appearance, it also activated users' resistance to the workings and outputs of ImageNet.

**54** In publications and promotional material framing the project, Crawford and Paglen raised awareness concerning the potential misappropriation of image content circulating online, specifically with regard to the web-scraping of dataset contents, a practice that frequently exploits legal loopholes in existing IP frameworks. Again, *ImageNet Roulette* approached this subject tactically, by emulating the functionality of popular online tools offering free services in return for permission to use private user data. While *ImageNet Roulette* itself was explicit about not retaining copies of uploaded images, the project format nevertheless drew attention to image ownership questions concerning the underlying dataset. For example, while the operators of ImageNet (the project is maintained in a collaboration between Princeton University and Stanford University) state that they do not hold copyrights in any of the images in the dataset, this does not mean that the individuals captured in the dataset images have consented to their use. Originally, online documentation for the project noted that ImageNet recognizes copyrights held in "the images in their original resolutions" (ImageNet Download FAQ). But this implied that the institutions maintaining the project may well claim ownership in the collections of low-resolution thumbnails with which labels and classifiers are paired, or likewise in the compilation of URLs linking back to the original images. Additionally, the project website clarifies that copies of all images in the dataset are kept on non-public servers, where they can be made available, upon request, for certain non-commercial and/or educational uses. While this formulation suggests that ImageNet is not meant for commercial exploitation, it is very difficult to control the contexts within which tools derived from ImageNet are used.

The achievements of *ImageNet Roulette* consist in the efficiency with which the project reconfigured the existing structure and functionality of ImageNet into a powerful critique of the underlying technology, as well as in the impact that the project had on public perceptions of dataset bias. As such, the project exemplifies how tactical artistic approaches can disturb, complicate,

and contest problematic ideological figurations of AI, and offers **55** a good context for beginning to explore how agency is construed in and through AI. Within less than a year of the project's release, hundreds of thousands of images and image descriptors were being removed from the ImageNet dataset (see Yang et al. 2019; Rea 2019), at which point the project was taken offline.

Tactical uses of AI, as I have described them above, denote critical modes of practice that proceed along vectors of flexibility, deterritorialization, and resilience. In contrast to the computational thinking encoded in strategic AI (such as ImageNet itself), tactical AI ultimately emphasizes the need for broader systemic understanding in computational contexts. It exemplifies a push toward what Bridle calls a fluency "not only in the language of a system, but in its metalanguage" (2018). Arguably, *ImageNet Roulette* aimed to steer its users toward such a critical systemic literacy of AI. Ed Finn describes this as the ability to develop practices of "algorithmic reading," i.e., the ability to develop and implement a "critical frame for interpreting objects that are also interpreting you" (Finn 2017, 55). In the following chapters, I will expand my discussion of AI art to projects which, like *ImageNet Roulette*, address issues of agency and cultural ownership, but which link these issues more directly to intellectual property, expressive agency, and to speculation on the becoming-creative of AI.

# **[3] (Dis)Locating Creativity, Agency, and Property in AI**

When the American engineer and computer artist Michael A. Noll attempted to register his copyright in *Gaussian Quadratic* (1963), a software-generated plotter graphic that is now considered a foundational work of software-based generative art (fig. 2), he encountered difficulties linking questions of creative agency to questions of intellectual property (IP), in a prefiguration of similar issues that are now playing out in AI art contexts. Copyright registrars at the U.S. Library of Congress had concerns regarding the general eligibility of *Gaussian Quadratic* for such legal protection, and repeatedly denied Noll's request before it was finally granted.<sup>1</sup> *Gaussian Quadratic* had been created at Bell Labs, where Noll, who, throughout his career, has persistently resisted identification as an artist, was employed as an engineer and researcher (cf. Taylor 2014, 32ff.). As Noll (1994, 41) recalls,

1 Readers with specialist knowledge of copyright law may wonder if Noll was attempting to register the underlying software code as a literary work, or instead to register its outputs as individual artistic works. Unfortunately, Noll's (1994) own anecdotal retelling of the episode offers little clue, and no digital record of the copyright registration is available.

**58** the work was initially deemed ineligible for copyright protection because the registrars viewed it as the creation of a computer system, rather than that of a human artist. This might seem like a progressive interpretation, but in a pragmatic sense it simply meant that from the copyright registrars' perspective, the law's requirement for an identifiable author/creator did not appear to be met. In response, Noll clarified that the graphic is, in fact, based on code written by a human, and explained that his program incorporates randomness to generate ever-different outputs. His second request was then also denied, this time based on the concern that even if a human author had been at work here, the randomness characterizing the work violated the "fixity" requirements of copyright law, i.e., the need for the work to exist in a fixed expression. Copyright protection for *Gaussian Quadratic* was finally granted when Noll rephrased his request a third time, insisting that even though the outputs of his generative algorithm are unpredictable, the human-authored code substrate represents an original and stable text, whose creativity is constituted precisely through its capacity to generate dynamic results.2

The contradictory chain of interpretations involved in the process of registering copyright in *Gaussian Quadratic* invokes the various kinds of speculation regarding non-human creative agency that I

2 Today, *Gaussian Quadratic* is considered as one of the first copyrightprotected works of computer-generated art. Noll writes that AT&T and Bell Labs, his employers at the time, were hesitant to publicly support his creative activities. Indeed, permission for him and his collaborator Bela Julesz to claim intellectual property ownership in their work appears to have been granted in order to divert public attention away from the corporate entities themselves, perhaps on the assumption that the creative experiments would be of little commercial value, or even harmful to corporate image and strategy. It is worth mentioning that this general attitude would soon reverse, with prominent artist residency programs springing up at Bell Labs and many other IT corporations. A corollary of this development is that permission for participating artists to retain exclusive IP rights is now much less likely to be granted. On the emergence of artist residencies in corporate technology contexts, see Scarlett 2020.

[Figure 2] Michael A. Noll, *Gaussian Quadratic* (1963). Copyright Victoria and Albert Museum; image used by permission.

have described in the previous chapter, and foreshadows how AI projects are now interfacing with similar questions of creativity, agency, and ownership. To me, the hesitations of the Library of Congress registrars indicate humanist and anthropocentric biases underlying their interpretation of Noll's work for the purpose of determining its eligibility for copyright protection. In

**60** this sense, the initial rejection of Noll's request suggests that the registrars may have humored, at least speculatively, the notion of a redistribution of creative agency from a human to a nonhuman entity; at the same time, the rejection shows that legally speaking, the registrars were unable to accept a computer as the artwork's author in the conventional sense. If the graphic was to be considered as the output of an autonomous computer, this meant that no valid author figure could be identified, and the graphic itself could not, consequence, be recognized as a copyright-protectable work of art. When Noll insisted that he himself was indeed the creator of the work, the Library of Congress registrars then questioned his creative agency, given that by his own admission he was not fully in control of his program's outputs. Ultimately, in order to be granted the protection that IP law commonly affords artists, Noll had to rescind on the main speculative implication enacted in this algorithm-driven generative work, namely that a shared creative agency existed between him and the technological system he used. (This implication resonates with the position of other early computer artists, including Frieder Nake, who similarly insisted that his work emerged from a collaborative working-together of human and computer.) From the perspective of the copyright registrars, *Gaussian Quadratic* could only exist as a copyright-protectable work of art if Noll asserted his authorial control over the generative system.

Compared to the complex and data-intensive computational tools available to contemporary artists working with AI today, the algorithm used to generate *Gaussian Quadratic* is quite straightforward. I would nevertheless argue that the work plays with the speculative possibilities of the posthumanist agential assemblage and its instantiation in computational functions, effects, technologies, or processes whose outcome can be interpreted as creative. Arguably, the copyright registrars acted in recognition of just such an interpretation when they initially rejected Noll's request. As such, the work highlights how, in an environment

in which validation of a creative expression through IP mech- **61** anisms has become the norm, artistic experimentation with tools that are artificially intelligent (or interpreted as such) requires a rethinking of copyright issues in relation to agency and creativity.

Historically, the exclusive and exclusionary mechanisms through which agency and personhood have been granted (or withheld) have often served to disadvantage and exploit marginalized groups based on gender, race, ethnicity, sexual identity, political conviction, and other criteria. To dispute the agency of an individual, group, or community has often meant to withhold not only rights to personal freedom, self-determination, and freedom of expression, but also rights to own property. As discussed below, some humanist perspectives closely link agency and ownership, to the point of considering them as co-constitutive. In order to explore further how speculative figurations of agency in AI systems might disturb the aesthetic and socio-economic frameworks in which they are embedded, it is necessary to unpack how these frameworks themselves draw on anthropocentric conceptions of agency. This is my aim in the present chapter: to elaborate, based on underlying philosophical tenets and legal theory, conceptual links between agency, personhood, and ownership—first with a focus on how these links form a foundation of IP principles, and then in consideration of how such principles can (or cannot) be applied to AI. This paves the way for my discussion, in the subsequent chapters, of AI art projects that interfere drastically with contemporary ownership models.

#### **AI Authorship According to Hegel**

A good starting point is to explore links between legal concepts of property and philosophical conceptualizations of personality and personhood. I will do so by way of Hegel's personality theory (1821), which continues to serve as a significant reference point in explanatory and justificatory frameworks for private property. The theory assumes the following: unless a clear sense of self

**62** can be fixed in externalized manifestations, an individual cannot begin to know itself as such, cannot begin to fathom its own subjectivity, and as a consequence those around it will be unable to relate to it as an individual. When these externalized manifestations of self become an individual's property, the individual's legitimate ownership claims co-constitute its personality. Who would I be, in other words, without some kind of externalization of "me" that is expressed in material and/or immaterial constructs and artefacts over which I can assert control? This perspective thus posits property and personality as co-constitutive and inextricably linked. It underpins the anthropocentric ideal of the emergence of an autonomous agent that is philosophically, legally, socially, and economically recognized by its ability to own property.

As the legal philosopher Peter Drahos notes, property is so important in Hegel's concept of identity and personality because it is through its acquisition and management that the mind, in Hegelian terms a free and unbound entity, manages to "achieve [a] concrete form of existence in the world" (Drahos 1996, 76). This concrete form manifests as personality "through the appropriation of things" (ibid.), to which, Hegel writes, any individual should have an absolute right. Significantly, for Hegel it is not only material objects that are subject to such appropriation, nor are tangible artefacts the only medium in which the self can find expression. Rather, as Drahos observes, "mental aptitudes, erudition, [and] artistic skill" (77) play a similarly important role in Hegel's concept of the formation of personality. Once externalized in tangible form, these "inner possessions" therefore also render the individual as a person. The agency of an individual as constituted by its ownership of externalized artefacts thus hinges fundamentally on the "objectification" of the individual's will (cf. Palmer 2005, 139), or, in Hegelian terms, on the imposition of a concrete form ("occupancy") on an externality.

While some contemporary critical positions contest this Hegelian perspective, the logic of its compelling notion of authorship-as-ownership persists in IP theory and policy. Noll's **63** *Gaussian Quadratic* is a case in point. The artist's difficulties in registering the work exemplify legal requirements according to which creative expressions need to be traceable to an originator; if these requirements are not satisfied, the aesthetic artefact is not eligible for copyright protection, and it becomes difficult for it to be owned, traded or gifted within the parameters that intellectual property law provides. If, in other words, no recognizable author of an aesthetic artefact work can be identified, then for many intents and purposes the artefact in question is no "work" at all. But if such an aesthetic artefact is indeed recognized as the original, creative expression of an identifiable author, this has significant implications for the nature and identity of this author figure.

A question raised by the copyright eligibility issues surrounding *Gaussian Quadratic* is what complications might arise when artificially intelligent agential assemblages become involved in creating (im)material artefacts which, following the Hegelian perspective, can be understood to engender personhood. What happens when such assemblages begin to operate autonomously, and detach from human subjectivity and knowability to such a degree that it is no longer possible to say, with any degree of authority, whether their outputs are "my" creations, and whether their decision-making processes and interpretations of information emerge from "my" thinking?

When non-human agential entities come into the picture, the anthropocentric logic underlying the Hegelian perspective—that personality forms through the externalization of will and the subsequent instantiation of this externalization as property—can be short-circuited. In such a situation, the "becoming-agent" through which, in a humanist framework, the individual is cast as creator and owner of things, is subject to various disturbances. What if, for example, an AI system is used in the making of an artwork in such a way that the human artist involved can no longer with certainty be considered as the creator of the work in question?

**64** When Christie's promoted the auction sale of the AI-generated artwork *Portrait of Edmond Belamy* in October 2018 by invoking this suggestion, many observers argued that the sale could significantly disrupt the author- and ownership relations which conventionally structure the art world. While the work certainly raised a number of interesting questions, in the following chapter I will argue that the presumptive non-human creative agency manifest in *Portrait of Edmond Belamy* actually relies on strongly anthropocentric models of authorship, and ultimately perpetuates the ownership models the work was said to be disrupting. The example suggests, nevertheless, that the emergence of presumptively creative AI, even when it appears as part of agential assemblages that also include human artists, signals a potential decentering of human agency, and the destabilizing of the processes by which agency (and thus ownership) have been traditionally assigned and recognized.

If an AI system were to be accepted as creative in an anthropocentric sense of the term, what kind of status would it be assigned as it pushes beyond aesthetics and into the domains of ethics, law, and economy? Sketching out an answer to such a question requires a more detailed consideration of whether authorship/ownership claims could become vested in AI. As noted in the previous chapter, property is a fundamentally relational construct; it establishes and maintains relationships between owner and non-owners in respect of the owned property, and is demarcated by a clearly definable set of rights and responsibilities. But a property relation is also always dynamic, in the sense that at any given moment it is both fixed (i.e., property rights are clearly assigned and enforceable) and subject to change (i.e., property rights are generally alienable). Because property rights assume and/or assert the existence of an owner, they have conventionally relied on some concept of personhood through which the subject of the legal rights and duties linked to ownership can be defined. What is not always spelt out clearly, however, is whether the legal person thus constituted must in

fact be a natural person. In a key essay on the subject, the legal **65** theorist Margaret Radin has suggested that any entity can in theory hold property rights, as long as the law can recognize this entity as an owner (Radin 1982; for a critique of Radin's position, see Schnably 1993; an early exploration of these arguments in AI contexts can be found in Solum 1992). In practice, however, this recognition has long been tied to a *de facto* requirement that an owner must not only be a legal person but also human. There are, by now, a few contexts in which legal and economic theory have opened up traditional conceptualizations of personhood toward what might be described as a trans-human perspective on agency: non-human corporate entities, for example, are now recognized virtually everywhere as legal persons. AI, however, has for now not been afforded such a status. When it comes to intelligence and creativity, anthropocentric biases persist.

Legal theory generally links personhood to agency by way of the assumption that when an action occurs, it can be traced to an agent who has, presumptively, acted with intention. Hegel's personality theory illustrates how this perspective negotiates the line between philosophical perspectives on agency and legal theories of property; it also suggests that the conceptual foundations of intellectual property themselves express anthropocentric ideals of authorship and creativity (cf. Hughes 1988; Radin 1993; Schroeder 2006). The Hegelian perspective—"that the person becomes a real self only by engaging in a property relationship with something external" (Radin 1982, 972)—assumes both that an individual's personality is actualized externally through the institution of property, and that property functions as a prerequisite for the formation of an individual capable of recognizing itself and of being recognized as such by others (Hegel 1952, 42). When this logic is extended to AI, and when artificially intelligent technological systems are interpreted as exhibiting creative agency comparable to that of human agents, AI inevitably begins to undermine anthropocentric IP models.

**66** Because the Hegelian perspective relies on principles of individual personhood and self-ownership, it is possible to derive from it the assumption that both the creator of an IP artefact and its owner must be natural persons (cf. Hughes 1988). In digital contexts, such an assumption can become grounds for the argument that AI, because it cannot qualify for "natural person" status, cannot author artworks, nor hold property rights in its expressions. With regard to agency, a similar logic can be applied: considering the personhood status that is required so that individuals can be recognized as authors, the agency that enables an individual to own property rights becomes linked to the agency that enables persons to express themselves creatively. As a consequence, copyright law has tended to assign ownership rights over any given creative expression to its presumptively human creators. The *U.S. Compendium of Copyright Practices* spells this out explicitly: "The U.S. Copyright Office will register an original work of authorship, provided that the work was created by a human being" (§306). But this formulation is open to challenges in contexts where it is unclear whether the human agent involved in the expressive act can, indeed, be meaningfully acknowledged as the creator. A good example is *Naruto v. Slater* (2018), a legal dispute in which People for Ethical Treatment of Animals (PETA) accused a photographer of copyright infringement, suing him on behalf of a monkey that had used the photographer's unattended camera for a series of now-famous "monkey selfies" (see Jiang 2019).

#### **Can IP Law Accommodate Artistic AI?**

I have already suggested that to speculate on the creativity of AI means inevitably also to speculate on AI agency, and thus to contend with the possibility of authorship and ownership rights vested in AI. Without a certain kind of creative agency, one cannot create; without a certain kind of legal agency, one cannot own. But if the U.S. Library of Congress' copyright registrars found it difficult to determine the legal status of a relatively simple

generative graphic such as Noll's *Gaussian Quadratic*, then it is **67** clear that advanced AI raises the stakes significantly. Yet, as the law and technology scholar James Grimmelmann notes, in many legal systems copyright law appears to have concluded, at least for the moment, "that it is for humans only" (Grimmelmann 2016).

The history of legal philosophy indicates that in addressing questions of legal personhood, law has typically either ignored the biological status of its subjects or has been ignorant of it (cf. Chopra and White 2011, 153). Considering this lack of clear direction regarding requirements for a legal subject to be human, the law and technology scholar Robert van den Hoven van Genderen suggests that any non-human person could theoretically be "in an equal position as an individual and natural person, unless otherwise provided by law" (van den Hoven van Genderen 2018, 30, referencing Naffine 2003). Concerning the legal concept of personhood, van den Hoven van Genderen summarizes three noteworthy perspectives: 1) "personhood means nothing more than the formal capacity to be a carrier of legal rights and duties;" 2) "a (legal) person is any reasonable human creature;" 3) a person is a "rational, responsible actor." Only the first and the third of these definitions can potentially be extended to AI; the third, additionally, has the potential to exclude some human agents (ibid., 35ff.). The question, then, is whether these perspectives can be linked to the kind of agency implied by the broader concept of authorship, which is being ever more closely approximated by expressive AI assemblages that might involve human participants, software, hardware, and algorithmic operations alongside one another, in enacting what I have described as a relational, multi-modal kind of posthumanist creative agency in the previous chapter.

But debates concerning new ontologies of authorship that branch off from existing paradigms remain strongly marked by anthropocentric bias. Here, once again, AI-generated outputs tend to be compared to human-made expressions, which can result in some rather strange logic. The legal scholars

**68** Yanisfky-Ravid and Velez-Hernandez (2018), for example, suggest that many works produced by what they call creative robots would surely be copyrightable—if only they had been created by humans. This perspective may seem like a challenge to the above-cited sentiment that copyright is "for humans only," but it also assumes that machine creativity is, essentially, like human creativity. The authors' suggestion to extend copyright eligibility to machine-made artworks thus hinges on a self-contradictory anthropomorphization of expressive AI, robots, and other nonhuman entities as human-like.

Oliver Bown, a researcher in the field of computational creativity, is among many who have voiced concerns regarding such a perspective, by pointing out that if the possibility of non-human creative agency is taken seriously, it must be accepted that machine-creativity might not function "in particularly humanlike ways" (2015, 18). It might instead match what Ray Kurzweil and others (Kurzweil 2005; Bridle 2018) have invoked as the specter of fundamentally unknowable non-human intelligence. Thus returns the underlying issue of whether or not creativity itself is a fundamentally human quality. In the editorial introduction to a topical special issue of *Philosophy & Technology*, David Gunkel observes that "the one remaining bulwark of human exceptionalism appears to be creativity and artistry" (2017, 263). As Gunkel himself notes, this opinion of course does not stand unchallenged, and some might argue that invocations of such exceptionalism are, in fact, at the core of the anthropomorphizing rhetoric that is often applied to AI. If creativity is truly unique to humans, then any admission that AI could also act creatively is either a radical rethinking of "the human," or already an anthropomorphization of artificial intelligence. Accordingly, machine-generated artworks can help at least to "stress-test" (264) prevalent assumptions, definitions, and categories concerning what it means to be human (or not), what it means to have creative agency (or not), and how to disentangle complications regarding the anthropomorphization of AI.

A recent essay by the legal scholars Carys Craig and the late Ian **69** Kerr offers one of the most comprehensive critiques to date of the anthropomorphizing tendencies with which emerging AI technologies are sometimes approached (Craig and Kerr, 2021). Writing from an interdisciplinary perspective, the authors adopt Kate Darling's HCI/HRI concept of "anthropomorphic framing," which describes tendencies to conceive of AI functionality and "behavior" in human terms—for example by giving robot humanoid shapes, by naming AI systems, or by using anthropocentric terminology to describe AI activities (Darling 2017, cited in Craig and Kerr, 32).3 As Craig and Kerr show, much of the discourse that links artificial intelligence to creative expression works not only by way of anthropomorphizing AI, but also, importantly, by way of an elaborate replaying of the myth of the romantic author figure. In other words, one of the most powerful ways of positing AI as "author" is to attach to it romantic notions of creativity, such as the fictions of *ex nihilo* creation and of the unified, singular author figure. This approach is exemplified by Arthur I. Miller's (2019) recent book-length survey of AI art: the book is an impressively comprehensive endeavor, but its notion of creativity is predominantly derived from human "genius" figures, whose superior brains serve as the models to which the presumptive creativity of computer "brains" is compared. Here, an oddly inverted mirror image of the idea of creativity's "effective computability" (see chapter 1) emerges, in the form of Miller's insistence on the all-too human(ist) nature of genius.

A reliance on humanist ideals that have traditionally framed definitions of human creativity may be helpful for arguing that AI is capable of acting independently in producing expressions that satisfy the cognitive scientist Margaret Boden's widely used creativity criteria of novelty, originality, and unexpectedness

3 While my discussion adopts this concept, I use the phrase "anthropocentric framing" instead, to reflect the fact that my focus is less on the anthropomorphization of AI than it is on tendencies to design and interpret AI functionality from anthropocentric viewpoints.

**70** (1990). But just like the traditional models on which such a perspective draws, this line of argumentation will likely ignore the dialogic and relational processes that ultimately constitute human authorship. In other words, such a view on AI creativity perpetuates assumptions regarding the exceptional uniqueness and supremacy of the humanist, individualized subject that post-anthropocentric, posthumanist critical thinking wishes to critique. A conclusion to be drawn from Craig and Kerr's insightful discussion is thus that even when the idea of AI creativity is proposed as a radical counterpoint or extension to human creative agency, at its heart this often represents a particularly problematic type of anthropocentric framing.