Volume 26, Issue 1

“Choice engines,” powered by Artificial Intelligence (AI) and authorized or required by law, might produce significant increases in human welfare. A key reason is that they can simultaneously (1) preserve autonomy and (2) help consumers to overcome inadequate information and behavioral biases, which can produce internalities, understood as costs that people impose on their future selves. Importantly, AI-powered choice engines might also take account of externalities, and they might nudge or require consumers to do so as well. Different consumers care about different things, of course, which is a reason to insist on a high degree of freedom of choice, even in the presence of internalities and externalities. Nonetheless, AI-powered choice engines might show behavioral biases, perhaps the same ones that human beings are known to show, perhaps others that have not been named yet, or perhaps new ones, not shown by human beings, that cannot be anticipated. It is also important to emphasize that AI-powered choice engines might be enlisted by insufficiently informed or self-interested actors, who might exploit inadequate information or behavioral biases, and thus reduce consumer welfare. AI-powered choice engines might also be deceptive or manipulative, and legal safeguards are necessary to reduce the relevant risks

Federal agencies have struggled to develop a comprehensive strategy for AI regulation due to the technology’s rapid development, its capacity to act autonomously, and the low barriers to entry and unpredictability of AI developments. Agencies across the federal government nonetheless are experimenting extensively with AI, operating as AI researchers, purchasers, users, and developers. In this Article, we juxtapose questions that agencies face in devising a regulatory scheme for AI with similar questions they have already had to answer in their experience as AI users. From adjudication to public engagement to law enforcement, federal agencies’ extensive experience integrating AI into their projects and missions can shed light on prudent courses of AI regulation. Moreover, governmental uses of AI can improve the credibility of the government as a regulator, increasing regulated entities’ confidence in the stability and effectiveness of the government’s regulatory schemes. Governmental uses of AI can inform the “why regulate” question by illustrating aspects of AI-induced market failures such as barriers to competition, information asymmetries, and externalities. Governmental uses of AI also offer lessons that bear on when regulators have enough information to regulate effectively and how to know if they are waiting too long. Finally, beyond informing regulation, governmental AI uses can shape private-sector AI use by catalyzing development of AI tools and by signaling appropriate AI uses and best practices to private-sector AI developers.

Data is a contextual phenomenon. It reflects the social and material context from which it is derived and in which it is generated. It embeds the purposes, assumptions and rationales of those who produce, collect, use, share and monetize it. In the AI and digital platform economy, data’s role is primarily infrastructural. Its core uses are internal to companies. Data only rarely serves as a medium of exchange or commodity, and more frequently serves to profile users, train models, produce predictions, and bundle and extend product capabilities, which in turn are sold to advertisers and other customers. Insofar as they focus on the former, many technical, economic and legal attempts at defining data have inspired reductive policy efforts that include data protection, data ownership, and limited data sharing remedies. This Article argues that understanding data as part of infrastructural pipelines can have significant conceptual and policy implications, and can redirect the way privacy, property and antitrust experts understand and govern data. This argument becomes more salient as market actors and regulators grapple with the catalyzing effects of neural networks and generative AI models on digital markets. In antitrust and competition law especially, regulators are consciously adopting a view of data as an infrastructural input into AI and other digital markets. Treating data as an input in regard to which certain firms have competitive advantages can have significant implications for nascent AI markets, and yet the views in antitrust remain too narrow. Understanding data infrastructurally means viewing it not only as a critical input but also as inseparable from other material digital resources such as protocols, algorithms, semiconductors, and platform interfaces; as having important collective functions; and as calling for public-interest regulation. Understanding data as infrastructure can move us past limited legal efforts and remedial solutions such as data separations, data sharing, and individual controls, and help reorient how data is produced, stored and managed toward public uses.

This Article presents an in-depth analysis of the challenges that competition law enforcement faces in light of the rapid advancements in AI, quantum computing, and synthetic biology. It delves into the various approaches that competition law institutions, such as competition agencies and courts, can adopt to address the uncertainties surrounding the competition impact of corporate strategies and conduct in developing and applying these new general purpose technologies. The Article focuses on the four key features of this “coming wave”: asymmetry, hyper-evolution, omni-use, and autonomy, all interconnected with the rise of complex systems that contribute to uncertainty. It explores the limitations of the ordinary risk management (ORM) approach typically followed in competition law, based on the expected utility framework. It advocates for the application of the precautionary principle as a more accurate description of the approach taken by competition authorities in this context and a more normatively adequate option for regulating threats of harm in complex systems while incorporating responsible innovation concerns. Moreover, the Article extensively examines how the precautionary principle can be seamlessly integrated into the design of competition law institutions and the substance of competition law, discussing the various containment tools used by competition authorities to address uncertainty.

The advent of competitors using a pricing algorithm supplied by the same software or data analytics company has created challenges for competition law. To begin, a third party may have an incentive to facilitate an agreement with subscribing firms to charge supracompetitive prices. A third party may even have an incentive to recommend supracompetitive prices without the support or knowledge of firms. Finally, and contrary to a canonical price-fixing agreement, a third party offers efficiencies when its pricing algorithm is capable of identifying prices more attuned to market conditions. These challenges are examined in the context of existing competition law—which is found to be inadequate—and some recently proposed remedies, which are found not to properly account for their impact on procompetitive efficiencies and may not even be effective in preventing anticompetitive harm.

Will AI technology disrupt the current Big Tech Barons, foster competition, and ensure future disruptive innovation that improves our wellbeing? Or might the technology help a few ecosystems become even more powerful? To explore this issue, our Article outlines the current digital market dynamics that lead to winner-take-most-or-all ecosystems. After examining the emerging AI foundation model supply chain, we consider several potential antitrust risks that may arise should specific layers become concentrated and firms extend their power across layers. After raising several countervailing factors that might lessen or prevent these antitrust risks, we conclude with suggestions for the policy agenda to promote both healthy competition and innovation in the AI supply chain.

Competition laws are influenced by economic presumptions regarding how markets operate. Such presumptions generally relate to how humans interact, such as how human decision-makers—whether acting as individuals or as a firm’s agents—gather information, send signals, and deal with complex, uncertain, or fast-changing market environments. The exponential growth in the use of algorithms by market participants to perform a myriad of tasks is challenging such presumptions. The lowering of access barriers to real-time data on market conditions, coupled with semi-automated decision-making by sophisticated and autonomous robo-economicus, requires us to rethink the economic presumptions embedded in our laws. Indeed, as we show, in many cases, the application of existing legal presumptions to markets in which decisions are made by sophisticated algorithms operating on big data increases the instances and harms of false negatives and, albeit less frequently, false positives. While research thus far has focused on the effects of algorithms on specific types of competition rules, this article suggests a general framework for identifying such effects. We employ decision theory to help determine how competition laws should be optimally framed in the age of algorithmic decision-making. As we show, once the use of sophisticated AI-empowered algorithms is assumed, legal presumptions with regard to some types of conduct must be changed. We suggest a typology of six different effects, ranging from no effect at all to a need for new prohibitions. Our theoretical analysis is aided by real-world examples, including cases where the introduction of sophisticated algorithms affects the choice between rules versus standards, the content of the prohibition, or procedural rules. We hope our meta-analysis brings more clarity to a much-needed reboot of our regulatory framework in the age of algorithms.

This Article addresses the trend, in the wake of advancing automation and AI technologies, of relegating human customer service to a premium service, which underscores the ethical, social, and legal challenges. It emphasizes the need to keep human interaction accessible and affordable for all, particularly for vulnerable populations, amidst this digital shift. The convenience and efficiency of automated systems such as IVR, chatbots, and virtual agents have transformed customer support, introducing significant cultural and moral challenges, notably the erosion of the personal touch and empathy that are vital for customer satisfaction and loyalty. The Article explores the evolution of customer service automation and its impact on workforce dynamics, consumers, and the quality of service. It highlights the hidden costs of diminished human interaction, particularly its adverse effects on disadvantaged, elderly, and disabled groups. Through case studies and examples, it showcases this trend’s negative consequences. Further, it discusses the human-in-the-loop concept, advocating for an approach that enhances customer experience with automation without sacrificing human interaction. It explores the considerations surrounding automated customer service, emphasizing the enforcement roles of agencies like the Federal Trade Commission (FTC) and Consumer Financial Protection Bureau (CFPB) in upholding consumer protection laws, and the need for regulations to ensure fairness, transparency, accessibility, and consent. In conclusion, the Article calls for technology to augment rather than replace human service, stressing the importance of clear regulations on the affordability of human interaction in customer support. It urges policymakers and businesses to ensure that automation does not marginalize those that need human assistance, advocating for equitable access to services.

The commercial launch of ChatGPT in November 2022 and the fast development of large language models have catapulted the regulation of artificial intelligence to the forefront of policy debates. A vast body of scholarship, white papers, and other policy analyses followed, outlining ideal regulatory regimes for AI. The European Union and other jurisdictions have moved forward by regulating AI and LLMs. One overlooked area is the political economy of these regulatory initiatives—or how countries and companies can behave strategically and use different regulatory levers to protect their interests in the international competition on how to regulate AI. This Article helps fill this gap by shedding light on the tradeoffs involved in the design of AI regulatory regimes in a world where (i) governments compete with other governments in using AI regulation, privacy, and intellectual property regimes to promote their national interests; and (ii) companies behave strategically in this competition, sometimes trying to capture the regulatory framework. We argue that this multilevel competition to lead AI technology will force governments and companies to trade off risks of regulatory arbitrage versus those of regulatory fragmentation. This may lead to pushes for international harmonization around clubs of countries that share similar interests. Still, international harmonization initiatives will face headwinds given the different interests and the high-stakes decisions at play, thereby pushing towards isolationism. To exemplify these dynamics, we build on historical examples from competition policy, privacy law, intellectual property, and cloud computing.

The emergence of algorithms poses fundamental challenges to competition law. The issues of algorithmic collusion and personalized pricing from the perspective of price discrimination have been extensively studied and are relatively well understood. What has escaped the attention of scholars and enforcers is the possibility that personalized pricing facilitated by algorithms may alter the way market power is exercised and abused. Predatory pricing is probably the most obvious candidate for an abuse whose nature may be altered, perhaps fundamentally so, by personalized pricing. It turns out that tying is another abuse that is ripe for transformation by algorithms. This Article explores how the market power threshold, the potential theories of harm, and possible pro-competitive justifications for tying may need to be reconceptualized as a result of personalized pricing.

In the artificial intelligence and cloud computing age, digital platforms like Meta, Google, and Amazon wield immense social, economic, and political power, shaping users’ daily lives. As these platforms gather vast amounts of user data and utilize sophisticated algorithms to personalize services, they also expose users to risks of bias and manipulation. Policymakers are seeking ways to hold platforms accountable, and algorithmic auditing is emerging as a key approach. However, existing regulations often rely on self-audits by the platforms themselves, leading to conflicts of interest. The shift towards third-party auditing is promising, but still falls short of resolving these conflicts. To address this challenge, this article introduces, typologizes, and explores a unique and underutilized approach to regulatory algorithmic oversight: “user-based algorithmic auditing.” According to this auditing approach, the platforms’ users lead the audit or assist external auditors in the process. User-based auditing is impartial and entirely independent of the audited platforms. User-based audits are also valuable for corroborating the information that the platforms provide in their self-auditing reports. The article explores regulatory frameworks, scrutinizes auditing approaches, and delves into the potential of user-based auditing to shape algorithmic oversight policies effectively.