Games Economists Play

" Games Economists Play ," 53 Vand. L. Rev. 1821 (2000) Abstract In contrast to Americans, there is a paucity of interest in economic theory among continental scholars. One view is that the explanation lies in another aspect of the civil versus common law distinction - a difference in the mode of reasoning which makes the formalism of certain kinds of economic theory more congenial to common law lawyers than to their civil counterparts. If this is true - if economic analysis appeals to American lawyers partly because of its kinship to common law methodology - then there are several implications for law and economics. One is that the subjects of modeling need to be chosen carefully: if models have special salience for legal thinkers, then it is important to be offering models that are geared to the issues lawyers are thinking about. A second implication of seeing common law jurisprudence as particularly susceptible to economic analysis lies in making sure that the common features of the formalism in common law and in economics do not obscure a crucial distinction between the two. Common law's analogic reasoning looks at how particular rules play on specified facts; it is not terribly relevant whether the facts leading up to the rule matched the historical truth about the case where the rule was articulated. This is not so when law is derived from models-there, underlying facts matter.

"Games Economists Play," 53 *Vand. L. Rev.* 1821 (2000)

									Abstract

In contrast to Americans, there is a paucity of interest in economic theory among continental scholars. One view is that the explanation lies in another aspect of the civil versus common law distinction - a difference in the mode of reasoning which makes the formalism of certain kinds of economic theory more congenial to common law lawyers than to their civil counterparts. If this is true - if economic analysis appeals to American lawyers partly because of its kinship to common law methodology - then there are several implications for law and economics. One is that the subjects of modeling need to be chosen carefully: if models have special salience for legal thinkers, then it is important to be offering models that are geared to the issues lawyers are thinking about. A second implication of seeing common law jurisprudence as particularly susceptible to economic analysis lies in making sure that the common features of the formalism in common law and in economics do not obscure a crucial distinction between the two. Common law's analogic reasoning looks at how particular rules play on specified facts; it is not terribly relevant whether the facts leading up to the rule matched the historical truth about the case where the rule was articulated. This is not so when law is derived from models-there, underlying facts matter.

Rochelle C. Dreyfuss, Games Economists Play, 53 Vand. L. Rev. 1821 (2000)

Rochelle Cooper Dreyfuss* When Professor Reichman called me about this symposium, I was intrigued. With the successive introduction of the photocopy machine, the videotape, computerization, digitization, the Internet, as well as a host of biotechnological discoveries, the problems facing the creative industries have changed dramatically. This accumulation of developments has altered the economic foundations on which intellectual property law is based and has pushed those of us in the field into a period of reconceptualization in which economic analysis is particularly fruitful. Thus, I was quite taken with the idea of bringing intellectual property and economics scholars together to promulgate a research agenda and I was, of course, delighted to be asked to contribute my thoughts. Before I set these out, I would like to begin with a question that may seem far afield, but which will, I believe, shed light on the agenda I propose. The question is this: why are there no continental lawyers here? Given Professor Reichman's prominence in international-particularly European-circles, it should seem quite puzzling. Or rather, it would be quite puzzling to me, but for my experience at the Max Planck Institute for Foreign and International Patent, Copyright and Competition Law in Munich, Germany. The Institute does wonderful, insightful work, including excellent empirical studies. But while there, I was struck by the paucity of interest in economic theory among its scholars. As one of my colleagues there explained, continental legal theorists certainly examine economic facts, but they are not of the view that economic theory has a large role to play in lawmaking. This thinking is, of course, very much at odds with the core premises of those assembled here, and I would like to take a moment to explore its basis. One reason for the difference in views on economic analysis may be specific to intellectual property. The argument here would be that although Europeans can be as theory-oriented as Americans, continental thinking about intellectual property has traditionally focused on moral arguments-claims about the personality of the author and his intimate connection to his work.' In contrast, intellectual property regimes in the United States are constitutionally defined as resting on instrumental-economic-precepts. 2 As a result, economic analysis arguably has more to contribute here than it does abroad. But this cannot be the whole answer. It does not, for example, explain apparent continental indifference to the use of economic theory with respect to other legal issues, such as tort questions. Moreover, the rationales underlying intellectual property regimes are coalescing: as Professor Thomas Dreier has pointed out, there is more economics in continental rationales for protection than is usually acknowledged, 3 and the TRIPS Agreement has brought these systems into even closer alignment. 4 Thus, a mode of analysis that is utilized in the U.S. should now be relevant to Europe, even if it lacks intuitive appeal there. A more general reason for the difference may have something to do with civil versus common law traditions. In civil law countries, judges are expert fact finders, but as to law, they are said to do no more than apply what the legislature has set out; there is no need for economic thinking because there is no room to deviate from statutory directives. 5 Here, the situation is quite different: [Vol. 53:6:1821 common law judges do make law, and they have come to realize that it cannot be done well without input from other disciplines. 6 But this cannot be the whole of the matter either. Judicial activism is another thing that is not often acknowledged abroad but exists anyway. Particularly as new technologies are introduced, continental judges are faced with questions that their legislatures could not have anticipated. Without an acknowledged law-making tradition to fall back on, economic thinking should be rather attractive to them, especially when the need for new law derives from the economic upheavals currently experienced by the information industries. Besides, legal scholars do not confine their influence to courts: the policymakers and bureaucrats writing new law for the European Union are rather obvious targets for economic analyses. Of course, the enterprise of creating a union might itself account for some of the difference. In that environment, concerns about such matters as currency equalization and free movement of goods may crowd out the kind of economic analysis prevalent in U.S. circles. But since even the European Union's primary concerns would benefit from economic elucidation, it remains a mystery why economic theory has so little traction for Europeans. My own view is that the explanation lies in another aspect of the civil versus common law distinction-a difference in the mode of reasoning which makes the formalism of certain kinds of economic theory more congenial to common law lawyers than to their civil counterparts. The kind of analysis I am thinking about starts with a model or a game: a stylized version of the facts, to which legal rules are applied, outcomes examined, and policy implications developed. Argument largely centers on whether the application of the rules to the model actually yields the results suggested, and on how to structure law to make the best use of the insights the model offers. The assumptions are then "relaxed"-that is, the facts of the model or game are altered-to see how far the insights of the model extend. 7 From a formal standpoint, this enterprise actually has a great deal in common with the sort of appellate advocacy for which common law legal scholars, judges, and even most legislators, are trained. A trial court finds the facts; the appellate court tests a va- riety of legal rules on the facts and chooses the one that best furthers policy. Outcomes are questioned in just the same way as the outcomes of modeling: were they properly assessed; was the best way to further policy chosen? And, just as economists relax assumptions to see how far their analyses stretch, so too common lawyers use a series of fact-altering hypotheticals to determine how far the chosen legal rule should extend. Presenting common law lawyers with a model is, in other words, not too different from presenting them with a case. The model's teachings are easily incorporated into the common law because acting on the basis of a model feels little different to a common law jurist from being persuaded-or bound-by a prior case. Civil lawyers are not as accustomed to case-by-case adjudication; they do not explicitly consider how the outcome of one case affects another. Reasoning there is more deductive than analogical, and so it should be no surprise that they would find it hard to incorporate economic modeling into their lawmaking. 8 If this is true-if economic analysis appeals to American lawyers partly because of its kinship to common law methodologythen there are several implications for law and economics. One is that the subjects of modeling need to be chosen carefully: if models have special salience for legal thinkers, then it is important to be offering models that are geared to the issues lawyers are thinking about. To put this another way, the field of intellectual property has, in one sense, been especially blessed by the attention of economists. But the attention has come from features of intellectual property law (such as its clear economic basis) that make for "cool" models; the questions examined are the ones where the mathematics has been worked out or the economic data necessary to use the model are most readily available. 9 These are not, unfortunately, always the areas where a readily utilizable analysis is especially needed. Thus, a great deal of work has been done on the economics of motivating people to engage in risky innovation-to move from the conception of a difficult problem to the commercialization of an end product.1 0 .Much less effort has been devoted to the more prosaic 8. I do not mean to say that this is the entire reason that American legal theorists are currently so attracted to economics; for a more comprehensive intellectual history, see Hovenkamp, supra note 6. businesses of marginal (or, as Reichman puts it, small grain 11 ) advances or moving product from lab bench to marketplace-investments that cannot be protected with patents. 12 Emerging industries attract attention; 13 the needs of maturing industries-indeed the question whether mature industries have different legal needs from emerging ones-have been largely ignored. 14 Phenomena that generate data, like collective rights organizations, are amply studied; 15 amorphous doctrines, such as fair use, which have enormous legal significance but little by way of hard facts, do not receive as much attention from pure economists. 16 Revising the criteria for choosing projects to make the need for attention a paramount consideration would go a long way to putting economists' input to better use within the legal community.17 There is a second, and to my mind, more important implication of seeing common law jurisprudence as particularly susceptible to economic analysis. It lies in making sure that the common features of the formalism in common law and in economics do not ob- (1974). The issue, however, is for lawyers using economic analysis, not for economists themselves. Cf. James Boyle, A Politics of Intellectual Property: Environmentalism for the Net?, 47 DUKE L.J. 87 (1997) (arguing for a movement among user groups for effective regulation of the information "environmene' akin to the movement that produced environmental protection legislation). See also Fisher, supra note 16, at 1697-98. scure a crucial distinction between the two. Common law's analogic reasoning looks at how particular rules play on specified facts; it is not terribly relevant whether the facts leading up to the rule matched the historical truth about the case where the rule was articulated. This is not so when law is derived from models-there, underlying facts matter. Consider Case X, in which an appellate court articulated Rule 1. A court considering whether to apply Rule 1 to the facts of Case Y may review the facts of Case Y (probably on a deferential basis 8 ). However, the wisdom of applying Rule 1 to the case is entirely independent of whether the facts of Case X were rightly determined. The court asks, given how Rule 1 was used under the facts as we have them in X, how should Y come out? In contrast, the insights of a model are entirely contingent on what underlies it. Whether Rule 2, derived from an economic model, should be applied to Case Y depends on what underlies the model-whether it captures the critical elements of the issue common to X and Y, and whether its assumptions are valid. Thus, the court should not treat the decision to apply Rule 2 in exactly the same way as it treats the decision to apply Rule 1: it needs to examine the factual basis of Rule 2 in a way it does not need to examine the facts underlying Rule 1. To the extent the commonality of the formalism obscures that point to lawyers using models, the economists who develop the models must take up the slack. They must be the ones to examine the moders underlying facts and assumptions before they promulgate them as solutions to legal problems. There are many examples of issues that could use further development along these lines. But where I would most like to see more scholarship focus is in work on bargaining. As it happens, there are many issues arising in intellectual property law-the need for compulsory licensing, the scope of patent claims, the standard of inventiveness-that would benefit from an understanding of when transactions are likely to occur. In a sense, there are now two camps: transactional optimists, exemplified by the likes of Professors Rob Merges and Suzanne Scotchmer, 19 who believe that with strong, clear rights, bargaining will generally occur, and transac- [Vol. 53:6:1821 tional pessimists, such as Professors Rebecca Eisenberg and Pamela Samuelson, who think law should be shaped by the concern that agreements will not always take place easily. 20 Right now, the state of play is that the optimists are winning. The Digital Millennium Copyright Act and the Uniform Computer Information Transactions Act are just two examples of laws structured on the optimistic view: they provide very little by way of public interest safeguards on the theory that access interests can be taken care of through bargaining. 21 Yet there has been very little examination of the assumptions underlying the pro-transaction model. What I believe has happened is that economists have done an excellent job analyzing prisoners' dilemmas-situations involving information asymmetries, where the models (correctly) predict that it will be difficult for participants to coordinate their efforts for mutual benefit. The success here has, however, led to a certain complacency about dealing with the converse situation, where information is symmetrically disposed. But the fact that informational asymmetry leads to transactional difficulty does not imply that transactions will always be easy so long as information is equally available to all participants. In fact, many intellectual property situations involve such dealings-transactions between the owners of complementary patent assets, such as a pioneer patentee and the holder of a patent on an improvement; the patentee of a partial genomic sequence and the party with a patent on the other part, or on an effective delivery vector; the firm with a pharmaceutical patent and the holder of the orphan drug right on the same compound. Each situation involves, essentially, a bilateral monopoly. Because patents disclose, both sides have similar information about the inventions and their potential uses; there is no (or very little) private information. Most importantly, there are clear gains from trade. That is, both sides know there is value in putting their rights together to create a single monopoly, and both sides know that if they fail, the value is (near) zero. In these situations, transactional optimists (most of whom are economists) predict a bargain will be struck; yet (as the lawyers who largely make up the pessimist camp know), we do not always observe these trades occurring. 22 Often there is no agreement at all, or the agreement is much delayed. In the case of patents, it is sometimes so far into the patent term that significant value is dissipated. There are several reasons why lawyers' and industries' actual experiences might differ so sharply from what economic analysis would predict. Clearly, one problem is valuation. Prisoners' dilemmas are not very sensitive to the values each player attaches to outcomes. Over a broad range of values, these models will correctly predict when there will be a problem coordinating behavior (indeed, the uncertainty over value probably increases the likelihood of a problem). But as Eisenberg's work tends to show, it may be that in "nondilemmas"-that is, when outcomes *(the inventions produced by experimentation) are known, valuation problems matter a great deal. 23 Current models may not be accounting for this level of sensitivity to accuracy. Outlook is another factor that might make a difference. Transactional optimists tend to base their views on experiences with tangibles, such as water and cattle. 24 The participants in these arrangements share expectations and enjoy a common understanding of background rules. These factors may make it easier for them to reach agreement than it is for actors in the innovation industries, who will often come from different disciplines and not be situated in geographic proximity. 25 And then there is the issue of repeat play: models that count on repetitive situations (the kind that produce standard contracts, tailored solutions, and an etiquette for bargaining) may not be apt for analyzing innovation, where there are many one-off deals and where it may not make financial sense to invest effort in predicting what could happen or in negotiating over every contingency. In any event, my point here is not to criticize particular models and certainly not to offer instructions on how to conduct economics research (it is not my field, and likely there are many features that I have not discussed that will turn out to matter). Nor do I think there is a real choice between transactional pessimism and optimism. Rather, I want to suggest that if greater attention is paid to bargaining in the presence of information symmetry, it should be possible to figure out when optimism is justified and when it is not, and to find ways to facilitate transactions when circumstances warrant the adoption of a pessimistic attitude. Such an understanding would be a big help to those in the legal community who have taken on the task of fashioning rules that promote the optimum usage of intellectual production. Many of the advances posing great challenge to intellectual property theory result from the confluence of developments within previously separate disciplines-biology and chemistry, computer science and business. Meeting these challenges will likewise require cross-disciplinary interchange. This symposium is, hopefully, the beginning of that process for intellectual property lawyers and economists interested in innovation. , e.g., MORTON J. HORwITz, THE TRANSFORMATION OF AMERICAN LAw, 1870-1960: THE CRISIS OF LEGAL ORTHODOXY 169-92 (1992); KARL LLEWELLYN, JURISPRUDENCE: REALISM IN THEORY AND PRACTICE (1962); JOHN H. SCHLEGEL, AMERICAN LEGAL REALISM AND EMPIRICAL SOCIAL SCIENCE (1995); Herbert Hovenkamp, Knowledge About Welfare." Legal Realism and the Separation of Law and Economics, 4 MINN. L. REV. 805, 854-56 & n.162 (2000). 7. See, e.g., Lewis A. neers, Imitators, and Generics-A Simulation Model of Schumpeterian Competition, 102 Q. J. ECON. 491 (1987). 11. See Jerome Reichman, Of Green Tulips Legal Kudzu Repackaging Rights in Subpatentable Innovation, 53 VAND. L. REV. 1743 (2000). 12. See, e.g., Ignatius Horstmann et al., Patents as Information Transfer Mechanisms: To Patent or (Maybe) Not to Patent, 93 J. POL. ECON. 837 (1985) (noting that, generally, non-patent oriented behavior is not studied enough); Brian D. Wright, The Economics of Invention Incentives: Patents, Prizes, and Research Contracts, 73 AM. ECON. REV. 691 (1983). 13. See, e.g., Robert P. Merges & Richard R. Nelson, On the Complex Economics of Patent Scope, 90 COLUM. L. REV. 839, 884-97 (1990) (discussing the effect of broad-scoped pioneer patents on subsequent innovations in various industries); Lloyd L. Weinreb, Copyright for Functional Expression, 111 HARV. L. REV. 1149, 1151 n.4 (1998) (surveying the extensive literature on the copyrightability of computer programs). 14. See generally Steven Klepper, Entry, Exit, Growth, and Innovation over the Product Life Cycle, 86 AM. ECON. REV. 562 (1996). 15. See, e.g., Stanley M. Besen et al., An Economic Analysis of Copyright Collectives, 78 VA. L. REV. 383 (1992); Robert P. Merges, Contracting into Liability Rules: Intellectual Property Rights and Collective Rights Organizations, 84 CAL. L. REV.


Generative AI is a Crisis for Copyright Law

" Generative AI is a Crisis for Copyright Law ," Issues Sci. & Tech. , Winter 2024, at 79 (with Kate Crawford)

"Generative AI is a Crisis for Copyright Law," *Issues Sci. & Tech.*, Winter 2024, at 79 (with Kate Crawford)

Kate Crawford & Jason M. Schultz, Generative AI is a Crisis for Copyright Law, Issues Sci. & Tech. (Winter 2024), https://issues.org/wp-content/uploads/2023/12/76-88-An-AI-Society-Winter-2024.pdf

stories it can generate-and what stories it will not generate. It's no secret that AI is biased. Researchers recently asked the image generator Midjourney to create images of Black physicians treating impoverished white children, but the system would only return images depicting the children as Black. Even a er several iterations, Midjourney failed to produce the speci ed results. e closest it got to the prompt was a shirtless medicine man with feathers, leather bands, and beads, gazing at a similarly garbed blond child. Here's something that hits close to home: the potential loss of Cardamom Town. orbjørn Egner's Folk og røvere i Kardemomme by (When the Robbers Came to Cardamom Town) is a children's book and musical well known to anyone who grew up in Norway or Denmark a er 1955. e songs and stories have been played, read, and sung in homes and preschools for decades; there's even a theme park inspired by the book in the city of Kristiansand. e story features three comical thieves who steal food because they are hungry and Amy Karle is a contemporary artist who uses artificial intelligence as both a medium and a subject in her work. Karle has been deeply engaged with AI, artificial neural networking, machine learning, and generative design since 2015. She poses critical questions about AI, illuminates future visions, and encourages us to actively shape the future we desire. One of Karle's projects focuses on how AI can help design and grow biomaterials and biosubstrates, including guiding the growth of mycelium-based materials. Her approach uses AI to identify, design, and develop diverse bioengineered and bioinspired structures and forms and to refine and improve the structure of biomaterials for greater functionality and sustainability. Another project is inspired by the seductive form of corals. Karle's speculative biomimetic corals leverage AI-assisted biodesign in conjunction with what she terms "computational ecology" to capture, transport, store, and use carbon dioxide. Her goal with this series is to help mitigate carbon dioxide emissions from industrial sources such as power plants and refineries and to clean up highly polluted areas. Amy Karle: AI-Assisted Biodesign "The future with AI does not have to be something that happens to us, it is something that we can cocreate." -Amy Karle don't understand that work is necessary. A er being caught stealing sausages and chocolate, they are rehabilitated by the kind police o cer and townsfolk, then end up saving the town from a re. is story is more than a shared cultural reference-it supports the Norwegian criminal justice system's priority of rehabilitation over punishment. It is distinct from Disney movies, with their unambiguous villains who are punished at the end, and from Hollywood bank heists and gangster movies that glorify criminals. Generative AI might well bury stories like Cardamom Town by stu ng chatbot responses and search results worldwide with homogenized American narratives. Narrative archetypes give us templates to live by. Depending on the stories we hear, share, and create, we shape possibilities for action and for understanding. We learn that criminals can be rehabilitated, or that they deserve to come to a bad end. Generative arti cial intelligence is driving copyright into a crisis. More than a dozen copyright cases about AI were led in the United States last year, up severalfold from all lings from 2020 to 2022. In early 2023, the US Copyright O ce launched the most comprehensive review of the entire copyright system in 50 years, with a focus on generative AI. Simply put, the widespread use of AI is poised to force a substantial reworking of how, where, and to whom copyright should apply. Starting with the 1710 British statute, "An Act for the Encouragement of Learning," Anglo-American copyright law has provided a framework around creative production and ownership. Copyright is even embedded in the US Constitution as a tool "to promote the Progress of Science and useful Arts." Now generative AI is destabilizing the foundational concepts of copyright law as it was originally conceived. Typical copyright lawsuits focus on a single work and a single unauthorized copy, or "output," to determine if infringement has occurred. When it comes to the capture of online data to train AI systems, the sheer scale and scope of these datasets overwhelms traditional analysis. e LAION 5-B dataset, used to train the AI image generator Stable Di usion, contains 5 billion images and text captions harvested from the internet, while CommonPool (a collection of datasets released by nonpro t LAION in April to democratize machine learning), o ers 12.8 billion images and captions. Generative AI systems have used datasets like these to produce billions of outputs. For many artists and designers, this feels like an existential threat. eir work is being used to train AI systems, which can then create images and texts that replicate their artistic style. But to date, no court has considered AI training to be copyright infringement: following the Google Books case in 2015, which assessed scanning books to create a searchable index, US courts are likely to nd that training AI systems on copyrighted works is acceptable under the fair use exemption, which allows for limited use of copyrighted works without permission in some cases when the use serves the public interest. It is also permitted in the European Union under the text and data mining exception of EU digital copyright law. Copyright law has also struggled with authorship by AI systems. Anglo-American law presumes that work has an "author" somewhere. To encourage human creativity, some authors need the economic incentive of a time-limited monopoly on making, selling, and showing their work. But algorithms don't need incentives. So according to the US Copyright O ce they aren't entitled to copyright. e same reasoning applied to other cases involving nonhuman authors, including the case where a macaque took sel es using a nature photographer's camera. Generative AI is the latest in a line of nonhumans deemed un t to hold copyright. Nor are human prompters likely to have copyrights in AIgenerated work. e algorithms and neural net architectures behind generative AI algorithms produce outputs that are inherently unpredictable, and any human prompter has less control over a creation than the model does. Where does this leave us? For the moment, in limbo. e billions of works produced by generative AI are unowned and can be used anywhere, by anyone, for any purpose. Whether a ChatGPT novella or a Stable Di usion artwork, output now exists as unclaimable content in the commercial workings of copyright itself. is is a radical moment in creative production: a stream of works without any legally recognizable author. ere is an equivalent crisis in proving copyright infringement. Historically, this has been easy, but when a generative AI system produces infringing content, be it an image of Mickey Mouse or Pikachu, courts will struggle with the question of who is initiating the copying. e AI researchers who gathered the training dataset? e company that trained the model? e user who prompted the model? It's unclear where agency and accountability lie, so how can courts order an appropriate remedy? Copyright law was developed by eighteenth-century capitalists to intertwine art with commerce. In the twentyrst century, it is being used by technology companies to allow them to exploit all the works of human creativity that are digitized and online. But the destabilization around generative AI is also an opportunity for a more radical reassessment of the social, legal, and cultural frameworks underpinning creative production. What expectations of consent, credit, or compensation should human creators have going forward, when their online work is routinely incorporated into training sets? What happens when humans make works using generative AI that cannot have copyright protection? And how does our understanding of the value of human creativity change when it is increasingly mediated by technology, be it the pen, paintbrush, Photoshop, or DALL-E? It may be time to develop concepts of intellectual property with a stronger focus on equity and creativity as opposed to economic incentives for media corporations. We are seeing early prototypes emerge from the recent collective bargaining agreements for writers, actors, and directors, many of whom lack copyrights but are nonetheless at the creative core of lmmaking. e lessons we learn from them could set a powerful precedent for how to pluralize intellectual property. Making a better world will require a deeper philosophical engagement with what it is to create, who has a say in how creations can be used, and who should pro t. Kate Crawford is a research professor at the University of Southern California Annenberg, a senior principal researcher at Microso Research, the inaugural chair of AI & Justice at the École Normale Supérieure, and the author of Atlas of AI (Yale University Press, 2021). Jason Schultz is a clinical professor of law at New York University, the codirector of the Engleberg Center on Innovation, Law, and Policy, and the author of e End of Ownership ( e MIT Press, 2016). Following Nazi medical experiments in World War II and outrage over the US Public Health Service's fourdecade-long Tuskegee syphilis study, bioethicists laid out frameworks, such as the 1947 Nuremberg Code and the 1979 Belmont Report, to regulate medical experimentation on human subjects. Today social media-and, increasingly, generative arti cial intelligence-are constantly experimenting on human subjects, but without institutional checks to prevent harm. In fact, over the last two decades, individuals have become so used to being part of large-scale testing that society has essentially been con gured to produce human laboratories for AI. Examples include experiments with biometric and payment systems in refugee camps (designed to investigate use cases for blockchain applications), urban living labs where families are o ered rent-free housing in exchange for serving as human subjects in a permanent marketing and branding experiment, and a mobile money research and development program where mobile providers o er their African consumers to rms looking to test new biometric and ntech applications. Originally put forward as a simpler way to test applications, the convention of so ware as "continual beta" rather than more discrete releases has enabled business models that depend on the given that it is already being incorporated into the base layers of applications we would be hard-pressed to avoid, the boundaries between the sandbox and the world are unclear. Generative AI is an extreme case of unregulated experimentation-as-innovation, with no formal mechanism for considering potential harms. ese experiments are already producing unforeseen ruptures in professional practice and knowledge: students are using ChatGPT to cheat on exams, and lawyers are ling AI-dra ed briefs with fabricated case citations. Generative AI also undermines the public's grip on the notion of "ground truth" by hallucinating false information in subtle and unpredictable ways. ese two breakdowns constitute an abrupt removal of what philosopher Regina Rini has termed "the epistemic backstop,"-that is, the benchmark for considering something real. Generative AI subverts information-seeking practices that professional domains such as law, policy, and medicine rely on; it also corrupts the ability to draw on common truth in public debates. Ironically, that disruption is being classed as success by the developers of such systems, emphasizing that this is not an experiment we are conducting but one that is being conducted upon us. is is problematic from a governance point of view because much of current regulation places the responsibility for AI safety on individuals, whereas in reality they are the subjects of an experiment being conducted across society. e challenge this creates for researchers is to identify the kinds of rupture generative AI can cause and at what scales, and then translate the problem into a regulatory one. en authorities can formalize and impose accountability, rather than creating di use and ill-de ned forms of responsibility for individuals. Getting this right will guide how the technology develops and set the risks AI will pose in the medium and longer term. Much like what happened with biomedical experimentation in the twentieth century, the work of de ning boundaries for AI experimentation goes beyond "AI safety" to AI legitimacy, and this is the next frontier of conceptual social scienti c work. Sectors, disciplines, and regulatory authorities must work to update the de nition of experimentation so that it includes digitally enabled and data-driven forms of testing. It can no longer be assumed that experimentation is a bounded activity with impacts only on a single, visible group of people. Experimentation at scale is frequently invisible to its subjects, but this does not render it any less problematic or absolve regulators from creating ways of scrutinizing and controlling it. Linnet Taylor is a professor of international data governance at Tilburg University, Netherlands, and leads the European Research Council-funded Global Data Justice project. LUCY SUCHMAN cial intelligence is the latest promise of a technological solution to the intractable "fog of war." In Ukraine and Gaza, enthusiasts have proclaimed the advent of AI-driven war ghting. In October 2023, Ukrainian technologists con rmed that AI-enabled drones identify and target 64 types of Russian "military objects" without a human operator; meanwhile the Israeli Defense Forces website states that an AI system generates recommended targets, reportedly at an unprecedented rate. Enormous questions arise regarding the validity of the assumptions built into these systems about who comprises an imminent threat and about the legitimacy of their targeting functions under the Geneva Conventions and the laws of war. Considering military investments in AI as part of a sociotechnical imaginary is helpful here. Developed within the eld of science and technology studies, the concept of sociotechnical imaginaries describes collectively imagined forms of social order as materialized by scienti c and technological projects. ese include aspirational futures that sustain investments in the military-industrial-academic complex. Iconic examples of AI-enabled war ghting in the present moment include battle management interfaces like Palantir's AI platform. To function in the real world, these platforms require very large, up-to-date datasets (of labeled "military objects" or biometric pro les of "persons of interest," for example), from which models can be developed. In the case of threat prediction and targeting, neither the US Department of Defense nor allied militaries make public the details necessary to assess validity. But in the case of predictive policing, an investigation by e Markup found that fewer than 1% of data-based predictions actually lined up with reported crimes. And generative AI introduces new uncertainties: both the provenance of the data and reliability of information are hard to check. at is particularly dangerous for "actionable military intelligence," which is used for targeting and to designate imminent threats. We should be deeply skeptical of the promotion of AI as a solution to the fog of war, which imagines that the right technology will nd the important signals amid the noise. is faith in technology constitutes a kind of willful ignorance, as if AI is a talisman that sustains the wider magical thinking of militarism as a path to security. In the words of performance artist Laurie Anderson (quoting her meditation teacher), "If you think technology will solve your problems, then you don't understand technology-and you don't understand your problems." Critical inquiry into the realities of war can help challenge the logics through which militarism perpetuates its imaginary of rational and controllable state violence while obscuring war's ungovernable chaos and unjusti able injuries. Although there are valid reasons that military forces exist in today's world, we should question the narratives that underwrite the billions of dollars funnelled into algorithmically based war ghting. We need to redirect resources to creative projects in de-escalation, negotiated settlements that o er true security for all, and eventual demilitarization. While the techno-solutionist imaginaries of militarism are longstanding, so are their limits as a basis for sustainable peace. Lucy Suchman is professor emerita of the anthropology of science and technology at Lancaster University in the United Kingdom. Already, content generated by arti cial intelligence populates the advertisements, news, and entertainment people see every day. According to OpenAI's cofounder Greg Brockman, the technology could fundamentally transform mass culture, making it possible, for example, to customize TV shows for individual viewers: "Imagine if you could ask your AI to make a new ending … maybe even put yourself in there as a main character." Brockman meant this as a sort of paradise of customization, but it's not hard to see how such tools could also spew misinformation and other content that would disrupt civic life and undermine democracy. Bad content would drive out good, enacting "Gresham's Law"-the principle that "bad money drives out good"-on steroids. Even top AI executives are begging for regulation, albeit at the level of individual products and their potential dangers. I think a more productive way to frame regulation is as a means of protecting the shared information environment. In decades past, the rationale for regulating the information space pivoted on the limited availability of broadcast channels, or "channel scarcity." Public attention can also be considered a nite resource, rationed by what information theorist Tiziana Terranova describes as "the limits inherent to the neurophysiology of perception and artificial intelligence the social limitations to time available for consumption." For democracy to function, people need to pay attention to matters of public import. In an information environment swamped with automatically generated content, attention becomes the scarce resource. A world in which attention is monopolized by an endless ow of personalized entertainment might be a consumers' paradise-but it would be a citizen's nightmare. e tech sector has already proposed a model for dispensing with public attention, one that is far from democratic. In 2016, a team at Google envisioned a "Sel sh Ledger"-a data pro le that would infer individuals' goals and then prompt aligned behavior, such as buying healthier food or locally grown produce, and seek more data to tweak the customized model. Similarly, physicist César Hidalgo suggested providing every citizen with a so ware agent that could infer political preferences and act on their behalf. In such a world, the algorithm would pay attention for us: no need for people to learn about the issues or even directly express their opinions. Such proposals show how important it is for citizens to actively regulate the information commons. Preserving scarce attention is essential to recapturing an increasingly elusive sense of shared, overlapping, and common interests. e world is moving toward a state where the data we generate can be used to further capture and channel our attention according to priorities that are neither our own, nor those of civic life. So ware, and whoever it serves, cannot be allowed to substitute for citizenship, and the economic might of tech giants must be balanced by citizens' ability to access the information they need to exercise their political power. Mark Andrejevic is a professor of communication and media studies in the School of Media, Film, and Journalism at Monash University, Australia. He is the author of Facial Recognition (Polity Press, 2022). As part of my job, I give talks about how arti cial intelligence a ects human rights: to criminology experts, schoolteachers, retirees, union members, First Peoples, and more. Across these diverse groups, I hear common themes. One is that although AI programs could impact how they do their jobs and live their lives, people feel their experience and expertise are completely le out before programs are deployed. Some worry, legitimately, about facing legal action if they protest. Plans and policies to regulate AI systems in Europe, Canada, and the United States are not likely to improve the situation. Europe plans to assign regulatory requirements based on application. For example, the high-risk category includes technology used in hiring decisions, police checks, banking, and education. Canadian legislation, still under review by the House of Commons, is based on the same risk assessment. e US president has outlined demands for rigorous safety testing, with results reported to the government. e problem is that these plans focus on laying out guardrails for anticipated threats without establishing an early warning system for citizens' actual experiences or concerns. Regulatory schemes based on a rigid set of anticipated outcomes might be a good rst step, but they are not enough. For one thing, some harms are only now emerging. And they could become most entrenched for marginalized, underserved groups because generative AI is trained on biased datasets that then generate new datasets that perpetuate the vicious cycle. A 2021 paper shows how prediction tools in education systems incorporate not just statistical biases (by gender, race, ethnicity, or language) but also understudied sociological ones such as urbanity. For instance, rural learners in Brazil are likely to di er from their urban counterparts with regards to uency in the o cial state language and their access to relevant educational materials, up-to-date facilities, and teaching sta . But because there aren't enough data on speci c groups' learning and schooling issues, their needs would be aggregated into a larger dataset and made invisible. Given the lack of knowledge, it would be di cult to even predict any kind of bias. What's needed are mechanisms that support citizens' direct engagement with AI deployments to document, from the ground, potentially high-risk impacts on collective equity. ere are democratic formats already in place to support citizens' perspectives. In Canada, for example, the mandate of the general solicitor or privacy commissioner could be strengthened to review AI deployments in the public sector (audits of datasets, mandatory impact assessments, etc.). ese mechanisms would provide transparent and accountable standards to keep citizens adequately informed about AI deployments, help balance the civic power dynamic, and strengthen social justice. Citizens' direct engagement could also be supported through access to courts. ere are few (if any) direct legal recourses available for ordinary people to challenge algorithmic harms in current AI regulatory schemes. Access to courts-and implicitly to justice-could send a clear message about citizens' power to corporations, governments, and, most importantly, to citizens artificial intelligence themselves. In combination with other mechanisms to increase citizen oversight, legal suits would o er not only access to rightful reparations, but also give societal recognition of citizens' rights. Sometimes at my talks people tell me they feel illegitimate asking questions about AI's impacts, given their lack of expertise. What I tell them is that they don't need to be a mechanic to know how bad it would be to be hit by a car. Harms from AI are bound to be more subtle, but the point stands. Citizens are the ones primarily a ected, so they must have an active role within AI governance. Emerging regulatory systems should highlight the role of citizens as social actors who contribute-as they should-to the collective good. Karine Gentelet is an associate professor of social sciences at the Université du Québec en Outaouais, Gatineau, Canada. including tech luminaries such as Elon Musk and Steve Wozniak, signed a call to "pause giant AI experiments" to deal with "profound risks to society." But the question is more complex than restraint versus unfettered technological development. It is about di erent ways to articulate ethical values and, above all, di erent visions of what society should be. A double interview in the French journal Le Monde illustrates the distinction. e interviewees, Yoshua Bengio and Yann Le Cun, are friends and collaborators who both received the 2018 Turing Award for their contributions to computer science. But they have radically di erent views on the future of generative AI. Bengio, who works at a nonpro t AI think tank in Montreal, believes ChatGPT is revolutionary. at's why he sees it as dangerous. ChatGPT and other generative AI systems work in ways that cannot be fully understood and o en produce results that are simultaneously wrong and credible, which threatens news and information sources and democracy at large. His argument mirrors philosopher Hans Jonas's precautionary principle: since humanity is better at producing new technological tools than foreseeing their future consequences, extreme caution about what AI can do to humanity is warranted. e solution is to establish ethical guidelines for generative AI, a task that the European Group on Ethics, the Organisation for Economic Co-operation and Development, UNESCO, and other global entities have already embraced. Le Cun, who works for Meta, does not consider ChatGPT revolutionary. It depends on neural networks trained on very large databases-all technologies that are several years old. Yes, it can produce fake news, but dissemination-not production-is the real risk. Techniques can be developed to ag AI-generated outputs and reveal what text and images have been manipulated, creating something akin to antispam so ware today. For Le Cun, the way to quash the dangers of generative AI will rely on AI. It is not the problem but the solution-a tool humanity can use to make better decisions. But who de nes what is a "better decision"? Which set of values will prevail? Here I see in Le Cun's arguments parallels to the economist and innovation scholar Joseph Schumpeter, who argued that within a democracy, the tools humans use to institutionalize values are the law and government. In other words, regulation of AI is essential. ese radically disparate views land on solutions that are similar in at least one aspect: whether generative AI is seen as a technological revolution or not, it is always embedded within a wider set of values. When seen as a for humanity, ethics are mobilized. When social values are threatened, the law is brought in. Either way, the solution is oversight of the corporations building AI. The Question Isn't Asset or Threat; It's Oversight As part of a research group studying generative AI with France's Académie Nationale de Médecine, I was surprised by some clinicians' technological determinism-their immediate assumption that this technology would, on its own, act against humans' wishes. e anxiety is not limited to physicians. In spring 2023, thousands of individuals, Arti cial intelligence algorithms are human-made, cultural constructs, something I saw rst-hand as a scholar and technician embedded with AI teams for 30 months. Among the many concrete practices and materials these algorithms need in order to come into existence are sets of numerical values that enable machine learning. ese referential repositories are o en called "ground truths," and when computer scientists construct or use these datasets to design new algorithms and attest to their e ciency, the process is called "ground-truthing." Understanding how ground-truthing works can reveal inherent limitations of algorithms-how they enable the spread of false information, pass biased judgments, or otherwise erode society's agency-and this could also catalyze more thoughtful regulation. As long as groundtruthing remains clouded and abstract, society will struggle to prevent algorithms from causing harm and to optimize algorithms for the greater good. Ground-truth datasets de ne AI algorithms' fundamental goal of reliably predicting and generating a speci c output-say, an image with requested speci cations that resembles other input, such as web-crawled images. In other words, ground-truth datasets are deliberately constructed. As such, they, along with their resultant algorithms, are limited and arbitrary and bear the sociocultural ngerprints of the teams that made them. Ground-truth datasets fall into at least two subsets: input data (what the algorithm should process) and output targets (what the algorithm should produce). In supervised machine learning, computer scientists start by building new algorithms using one part of the output targets annotated by human labelers, before evaluating their built algorithms on the remaining part. In the unsupervised (or "selfsupervised") machine learning that underpins most generative AI, output targets are used only to evaluate new algorithms. Most production-grade generative AI systems are assemblages of algorithms built from both supervised and self-supervised machine learning. For example, an AI image generator depends on self-supervised di usion algorithms (which create a new set of data based on a given set) and supervised noise reduction algorithms. In other words, generative AI is thoroughly dependent on ground truths and their socioculturally oriented nature, even if it is o en presented-and rightly so-as a signi cant application of self-supervised learning. Why does that matter? Much of AI punditry asserts that we live in a post-classi cation, post-socially constructed world in which computers have free access to "raw data," which they re ne into actionable truth. Yet data are never raw, and consequently actionable truth is never totally objective. Algorithms do not create so much as retrieve what has been supplied and de ned-albeit repurposed and with varying levels of human intervention. is observation rebuts certain promises around AI and may sound like a disadvantage, but I believe that it could instead be an opportunity for social scientists to begin new collaborations with computer scientists. is could take the form of a professional social activity, people working together to describe the ground-truthing processes that underpin new algorithms, and so help make them more accountable and worthy. Florian Jaton is a senior researcher and lecturer in sociology of science and technology at the Geneva Graduate Institute of International and Development Studies, Switzerland. He is the author of e Constitution of Algorithms: Ground-Truthing, Programming, Formulating ( e MIT Press, 2021). Current technical approaches to preventing harm from arti cial intelligence and machine learning largely focus on bias in training data and careless (even malicious) misuse. To be sure, these are crucial steps, but they are not su cient solutions. Many risks from AI are not simply due to awed executions of an otherwise sound strategy: AI's artificial intelligence penchant for enabling bias and misinformation is built into its "data-driven" modeling paradigm. is paradigm forms the foundation of present-day machine learning. It relies on data-intensive pattern recognition techniques that generalize from past examples without direct reference to, or even knowledge about, what is being modeled. In other words, data-driven methods are designed to predict the probable output of processes that they can't describe or explain. at deliberate omission of explanatory models leaves these methods particularly receptive to misdirection. Today, this data-intensive, brute-force approach to machine learning has become largely synonymous with arti cial intelligence and computational modeling as a whole. Yet history shows that the rise of data-driven machine learning was neither natural nor inevitable. Even machine learning itself was not always so data-centric. Today's dominant paradigm of data-driven machine learning in key areas such as natural language processing represents what Alfred Spector, then Google's vice president for research, lauded in 2010 as "almost a 180-degree turn in the established approaches to speech recognition." rough its early decades, AI research in the United States xated on replicating human cognitive faculties, based on an assumption that, as historian Stephanie Dick puts it, "computers and minds were the same kind of thing." e devotion to this human analogy began to change in the 1970s with a highly unorthodox "statistical approach" to speech recognition at IBM. In a stark departure from the established "knowledge-based" approaches of the period, IBM researchers abandoned elaborate formal representations of linguistic knowledge and used statistical pattern recognition techniques to predict the most likely sequence of words, based on large quantities of sample data. ose very researchers described to me how this work owed much of its success to the unique computing resources available at IBM, where they had access to more computing power than anyone else. Even more importantly, they had access to more training data in a period where digitized text was vanishingly scarce by today's standards. During a federal antitrust case against the company from 1969 to 1982, IBM had manually digitized over 100,000 pages of witness testimony using a warehouse facility full of keypunch operators to manually encode text onto Hollerith punched cards. is material was repurposed into a training corpus of unprecedented size for the period, at around 100 million words. What resulted was an abandonment of knowledge-based approaches aimed at simulating human decision processes in favor of data-driven approaches aimed solely at predicting their output. is signaled a fundamental reimagining of the relation between human and machine intelligence. Director of IBM's Continuous Speech Recognition group Fred Jelinek described their approach in 1987 as "the natural way for the machine," quipping that "if a machine has to y, it does so as an airplane does-not by apping its wings." e success of this approach directly triggered a shi to data-driven approaches across natural language processing as well as machine vision, bioinformatics, and other domains. In 2009, top Google researchers pointed the earlier success of the statistical approach to speech recognition as proof that "invariably, simple models and a lot of data trump more elaborate models based on less data." machine intelligence as something fundamentally distinct from, if not antithetical to, human understanding set a powerful precedent for replacing expert knowledge with data-driven approximation in computational modeling. Generative AI takes this logic a crucial step further, using data not only to model the world, but to actively remake it. Large language models are both ignorant of and indi erent toward the substance of the statements they generate; they gauge only how likely it is for a sequence of text to appear. Which is to say, if the results pushed to our social media feeds are decided by algorithms that are intentionally designed to only predict patterns, but not to understand them, can the ourishing of misinformation really come as such a surprise? A failure to recognize how such problems may be intrinsic to the very logic of data-driven machine learning inspires omisguided technical xes, such as increased data collection and tracking, which can lead to harms such as predatory inclusion (in which outwardly democratizing schemes further exploit already marginalized groups). Such approaches are limited because they presume more machine learning to be the best recourse. But the lens of history helps us break out of this circular thinking. e perpetual expansion of data-driven machine learning should not be seen as a foregone conclusion. Its rise to prominence was embedded in certain assumptions and Many risks from AI are not simply due to awed executions of an otherwise sound strategy: AI's penchant for enabling bias and misinformation is built into its "data-driven" modeling paradigm. priorities that became entrenched in its technical framework and normalized over time. Instead of defaulting to tactics that augment machine learning, we need to consider that in some circumstances the very logic of machine learning might be fundamentally unsuitable to our aims. We are watching "intelligence" being rede ned as the tasks that an arti cial intelligence can do. Time and again, generative AI is pitted against human counterparts, with textual and visual outputs measured against human abilities, standards, and exemplars. AI is asked to mimic, and then to better, human performance on law and graduate school admission tests, advanced placement exams and more-even as those tests are being abandoned because they perpetuate inequality and are inadequate to the task of truly measuring human capacity. e narratives trumpeting AI's progress obscure an underlying logic requiring that everything be translated into the technology's terms. If it is not addressed, that hegemonic logic will continue to narrow viewpoints, hamper human aspirations, and foreclose possible futures by condemning us to repeat-rather than learn from-past mistakes. e problem has deep roots. As AI evolved in the 1950s and '60s, researchers o en made human comparisons. Some suggested that computers would become "mentors" and "colleagues," others "assistants," "servants," or "slaves." As science and technology scholars Neda Atanasoski, Kalindi Vora, and Ron Eglash have shown, these comparisons shaped the perceived value not only of AI, but also of human labor. ose relegating AI to the latter categories usually did so because they believed computers would be limited to menial, repetitive, and mindless labor. ey were also reproducing the ction that human assistants are merely mechanical, menial, and mindless. On the other hand, those celebrating potential mentors and colleagues were tacitly assuming that human counterparts could be stripped of everything beyond e cient reasoning. Comparisons between AI and human performance o en correlate with social hierarchy. As society and technology scholars Janet Abbate, Mar Hicks, and Alison Adam have shown, in the 1960s and 1970s, women and minorities were encouraged to advance in society by learning to code-but those skills were then devalued, while domains dominated by white men were seen as the realm of the truly technically skilled. More recent OpenAI measures of AI against standardized exams endorse a positivist, adversarial, and bureaucratic understanding of human intelligence and potential. Similarly, AI-generated "case interviews" and artworks encode mimicry as the de nition of intelligence. For a result from generative AI to be validated as true-or to shock others as "true"-it has to be plausible, that is, recognizable in terms of past values or experiences. But looking backward and smoothing out outliers forecloses the rich wellsprings of humanity's imagination for the future. Such practices will ultimately a ect who and what is perceived as intelligent, and that will profoundly change society, discourse, politics, and power. For example, in "AI ethics," complex concepts such as "fairness" and "equality" are recon gured as mathematical constraints on predictions, collapsed onto the underlying logic of machine learning. In another example, the development of machine learning systems for game-playing has led to a reductive rede nition of "play" as simply making permissible moves in search of victory. Anyone who has played Go or chess or poker against another person knowns that, for humans, "play" includes so much more. e portrayal of AI's history is usually one of progress, where constellations of algorithms attain humanlike general intelligence and creativity. But that narrative might be more accurately inverted with a shrinking de nition of intelligence artificial intelligence that excludes many human capabilities. is narrows the horizon of intelligence to tasks that can be accomplished with pattern recognition, prediction from data, and the like. We fear this could set limits for human aspirations and for core ideals like knowledge, creativity, imagination, and democracymaking for a poorer, more constrained human future. O en, problems that seem narrow and purely technical are best tackled if they're recast as "public problems," a concept put forth almost a century ago by philosopher and educator John Dewey. Examples of public problems include dirty air, polluted water, global warming, and childhood education. Public problems bring harms that are not always felt individually but that nonetheless shape what it means to be a thriving person in a thriving society. ese problems need to be noticed, discussed, and collectively managed. In contrast to problems that are personal, private, or technical, Dewey wrote, public problems happen when people experience "indirect consequences" that need to be collectively and "systematically cared for," regardless of an individual's circumstance, wealth, privilege, or interests. Public problems de ne our shared realities. Although generative AI has been framed as a technical problem, recasting it as a public problem o ers new avenues for action. Generative AI is quickly becoming a language for telling society's collective stories and teaching us about each other. If you ask generative AI to make a story or video that explains climate change, you are actually asking a probabilistic machine learning model to create a statistically acceptable account of a public problem. Tools such as ChatGPT and Midjourney are fast becoming languages for understanding public problems, but with little analysis of their power to shape the stories that humans use to understand the shared consequences that Dewey told us create public life. To grapple with generative AI e ectively, consumers and developers alike need to see it not only as biased datasets and machine learning run amok-we need to see it as a language that people are using to learn, make sense of their worlds, and communicate with others. In other words, it needs to be seen as a public problem. First, researchers need to see generative AI as a powerful language-as "boundaries," "infrastructures," and "hinges" that scholars of science and technology tell us create technologies. is means tracing the connections among the people and machines that make synthetic language: engineers who build machine learning systems, for example, entrepreneurs who pitch business models, journalists who make synthetic news stories, audiences who struggle to know what to believe. ese are the complex and largely invisible assumptions that make generative AI a language for representing knowledge, fueling innovation, telling stories, and creating shared realities. Second, as a society, we need to analyze the harms created by generative AI. When statistical hallucinations invent facts, chatbots misattribute authorship, or computational summaries bungle analyses, they produce dangerously wrong language that has all the con dence of a seemingly neutral, computational certainty. ese errors are not just rare and idiosyncratic curiosities of misinformation; their real and imagined existence makes people see media as unstable, unreliable, and untrusted. Society's information sources-and ability to gauge reality-are destabilized. Finally, all members of society should reject the assertions of technology companies and AI "godfathers" who claim that generative AI is both an existential threat and a problem that only technologists can manage. Public problems are collectively debated, accounted for, and managed; they are not the purview of private companies or self-identi ed caretakers who work on their own timelines with proprietary knowledge. Truly public problems are never outsourced to private interests or charismatic authorities. A public problem is not merely a technical curiosity, a moral panic, or an inevitable future. It is a system of relationships between people and machines that creates language, makes mistakes, and needs to be systematically cared for. Once we understand generative AI as a vital language for creating shared realities and tackling collective challenges, we can start to see it as a public problem, and then we will be in a better place to solve it. These articles arose from a working group on arti cial intelligence and justice convened by Kate Crawford at the École Normale Supérieure. AMY KARLE, BioAI-Formed Mycelium, 2023

Amy Karle: AI-Assisted Biodesign "The future with AI does not have to be something that happens to us, it is something that we can cocreate." -Amy Karle don't understand that work is necessary. A er being caught stealing sausages and chocolate, they are rehabilitated by the kind police o cer and townsfolk, then end up saving the town from a re.

Narrative archetypes give us templates to live by. Depending on the stories we hear, share, and create, we shape possibilities for action and for understanding. We learn that criminals can be rehabilitated, or that they deserve to come to a bad end.

Generative AI systems have used datasets like these to produce billions of outputs.

Nor are human prompters likely to have copyrights in AIgenerated work. e algorithms and neural net architectures behind generative AI algorithms produce outputs that are inherently unpredictable, and any human prompter has less control over a creation than the model does.

is is problematic from a governance point of view because much of current regulation places the responsibility for AI safety on individuals, whereas in reality they are the subjects of an experiment being conducted across society.

Linnet Taylor is a professor of international data governance at Tilburg University, Netherlands, and leads the European Research Council-funded Global Data Justice project.

AI Aids the Pretense of Military "Precision"

Lucy Suchman is professor emerita of the anthropology of science and technology at Lancaster University in the United Kingdom.

Such proposals show how important it is for citizens to actively regulate the information commons. Preserving scarce attention is essential to recapturing an increasingly elusive sense of shared, overlapping, and common interests.

Mark Andrejevic is a professor of communication and media studies in the School of Media, Film, and Journalism at Monash University, Australia. He is the author of Facial Recognition (Polity Press, 2022).

Karine Gentelet is an associate professor of social sciences at the Université du Québec en Outaouais, Gatineau, Canada.

including tech luminaries such as Elon Musk and Steve Wozniak, signed a call to "pause giant AI experiments" to deal with "profound risks to society." But the question is more complex than restraint versus unfettered technological development. It is about di erent ways to articulate ethical values and, above all, di erent visions of what society should be.

A double interview in the French journal Le Monde illustrates the distinction. e interviewees, Yoshua Bengio and Yann Le Cun, are friends and collaborators who both received the 2018 Turing Award for their contributions to computer science. But they have radically di erent views on the future of generative AI.

The Question Isn't Asset or Threat; It's Oversight

As part of a research group studying generative AI with France's Académie Nationale de Médecine, I was surprised by some clinicians' technological determinism-their immediate assumption that this technology would, on its own, act against humans' wishes. e anxiety is not limited to physicians. In spring 2023, thousands of individuals,

Why does that matter? Much of AI punditry asserts that we live in a post-classi cation, post-socially constructed world in which computers have free access to "raw data," which they re ne into actionable truth. Yet data are never raw, and consequently actionable truth is never totally objective.

Florian Jaton is a senior researcher and lecturer in sociology of science and technology at the Geneva Graduate Institute of International and Development Studies, Switzerland. He is the author of e Constitution of Algorithms: Ground-Truthing, Programming, Formulating ( e MIT Press, 2021).

priorities that became entrenched in its technical framework and normalized over time. Instead of defaulting to tactics that augment machine learning, we need to consider that in some circumstances the very logic of machine learning might be fundamentally unsuitable to our aims.

STEPHANIE DICK, WENDY HUI KYONG CHUN, AND MATT CANUTE

To grapple with generative AI e ectively, consumers and developers alike need to see it not only as biased datasets and machine learning run amok-we need to see it as a language that people are using to learn, make sense of their worlds, and communicate with others. In other words, it needs to be seen as a public problem.

AMY KARLE, BioAI-Formed Mycelium, 2023