Di Porto et al. on Mining EU Consultations through AI

Fabiana Di Porto (Law and Economics) et al. have posted “Mining EU Consultations through AI” (Artificial Intelligence and Law, 0; 2024 [10.1007/s10506-024-09426-6]) on SSRN. Here is the abstract:

Consultations are key to gather evidence that informs rulemaking. When analysing the feedback received, it is essential for the regulator to appropriately cluster stakeholders’ opinions, as misclustering may alter the representativeness of the positions, making some of them appear majoritarian when they might not be. The European Commission (EC)’s approach to clustering opinions in consultations lacks a standardized methodology, leading to reduced procedural transparency, while making use of computational tools only sporadically. This paper explores how natural language processing (NLP) technologies may enhance the way opinion clustering is currently conducted by the EC. We examine 830 responses to three legislative proposals (the Artificial Intelligence Act, the Digital Markets Act and the Digital Services Act) using both a lexical and semantic approach. We find that some groups (like small and medium companies) have low similarity across all datasets and methodologies despite being clustered in one opinion group by the EC. The same happens for citizens and consumer associations for the consultation run over the DSA. These results suggest that computational tools actually help reduce misclustering of stakeholders’ opinions and consequently allow greater representativeness of the different positions expressed in consultations. They further suggest that the EC could identify a convergent methodology for all its consultations, where such tools are employed in a consistent and replicable rather than occasionally. Ideally, it should also explain when one methodology is preferred to another. This effort should find its way into the Better Regulation toolbox (EC 2023). Our analysis also paves the way for further research to reach a transparent and consistent methodology for group clustering.

Delacroix on Transitional Conversational Spaces? LLMs and the Future of Collective Moral Perception

Sylvie Delacroix (King’s College London) has posted “Transitional Conversational Spaces? LLMs and the Future of Collective Moral Perception” on SSRN. Here is the abstract:

As large language models become regular interlocutors, they influence the conversational infrastructure through which communities collectively interpret their world. This mediation notably impacts moral understanding, which mostly develops through shared dialogic practices rather than abstract theorizing. Within these practices, ‘sense-making conversations’-wherein participants navigate the liminal space between felt ethical disquiet and its eventual conceptual articulation-function as a crucial yet under-theorized infrastructure for ethical development.

While routine exchanges treat uncertainty as a deficit to eliminate, these sensemaking conversations hinge on maintaining productive uncertainty long enough for new perceptions to emerge through patient attention. The systematic presence of LLMs as conversational partners stands to reshape how future generations engage with such productive uncertainty, potentially transforming the mechanisms through which communities recognize emerging moral challenges. Rather than treating technological participation as merely instrumental to system optimization, we argue for a substantive reconceptualization of the relationship between technological development and democratic practice, exploring how LLMs might constitute novel, transitional conversational spaces that serve democratic ends.

Cohen on Oligarchy, State, and Cryptopia

Julie E. Cohen (Georgetown U Law Center) has posted “Oligarchy, State, and Cryptopia” (94 Fordham L. Rev. (forthcoming)) on SSRN. Here is the abstract:

Theoretical accounts of power in networked digital environments typically do not give systematic attention to the phenomenon of oligarchy—to extreme concentrations of material wealth deployed to obtain and protect durable personal advantage. The biggest technology platform companies are dominated to a singular extent by a small group of very powerful and extremely wealthy men who have played uniquely influential roles in structuring technological development in particular ways that align with their personal beliefs and who now wield unprecedented informational, sociotechnical, and political power. Developing an account of oligarchy and, more specifically, of tech oligarchy within contemporary political economy therefore has become a project of considerable urgency. This essay undertakes that project.

As I will show, tech oligarchs’ power derives partly from legal entrepreneurship related to corporate governance and partly from the infrastructural character of the functions the largest technology platform firms now perform. It is transnational and multidimensional, producing a wide range of consequences that are impossible for millions (and sometimes billions) around the globe to avoid. And it is personal; tech oligarchs have never been required to trade increased scale for increased accountability.

This account of tech oligarchy has important implications for three large categories of hotly debated issues. First, it sheds new light on the much-remarked inability of nation states to govern giant global technology platform firms effectively using the traditional tools of economic regulation. Second, it illuminates an important difference between the way capitalists approach projects for regulatory capture and the way technology oligarchs approach them. The ordinary capture projects of most interest to tech oligarchs revolve around personal enrichment; the extraordinary experiment that the U.S. is now witnessing, however, also seeks to reconfigure the state and its constituent institutions in ways more amenable to oligarchic direction. Third, it counsels more careful attention to an array of other oligarchic projects—including especially dreams of space colonization and the quest to develop artificial general intelligence—that have struck many observers as fantastical. Through such projects, tech oligarchs are working to dismantle existing forms of social, economic, and political organization and define a human future that they alone determine.

Caputo on ‘Quiet’ Enjoyment: Uncovering the Hidden History of the Right to Attention in Private and Public

Nicholas A. Caputo (Oxford Martin) has posted “’Quiet’ Enjoyment: Uncovering the Hidden History of the Right to Attention in Private and Public” (Stanford Technology Law Review (forthcoming 2025)) on SSRN. Here is the abstract:

Legal scholars have largely neglected attention as a subject of legal rights, even as attention has become one of the most valuable economic resources of the modern era. This Article argues that a right to attention has existed implicitly in American law since the early twentieth century, emerging in response to technological, social, and economic changes in that period that made attention both increasingly valuable and increasingly impinged upon, as America shifted toward knowledge work and leisure activities that demanded sustained focus. By examining court decisions in private law doctrines around property and public law doctrines around speech that can only be explained by reference to an implicit right to attention, this Article begins to uncover the ways in which judges and lawmakers built out a set of legal protections that enabled people to invoke the law to protect their own attention while avoiding stifling the sometimes-disruptive conduct of others. In particular, I show that in private law, courts began recognizing “attentional nuisances,” nontrespassory invasions of land that did not cause physical but only attentional harm, thereby creating a framework for protecting a person’s attention on her own land. In public spaces, the new right to attention came into conflict with also-emerging free speech rights, which seem to require the ability to attract the attention of others in order to express oneself to them. There, the Supreme Court sought a balance through the development of frameworks like time, place, or manner doctrine, which allowed governments to try to regulate attention-grabbing stimuli without directly regulating speech, and through the uneven development of listeners’ rights. In closing, I argue that the right to attention developed in the early twentieth century provides a foundation upon which a modern right to attention addressed to the attention economy could be developed that is both rooted in the experience of the past and capable of meeting the novel challenges presented by digital technology and the rise of artificial intelligence, which promise another epochal technological revolution like that which gave rise to the right a century ago. Drawing out the right to attention buried in the caselaw gives scholars, lawmakers, and the public a set of tools that they can use to decide how to adapt it to the demands of the present. The future of attention relies upon the lessons of its past, and recognizing explicitly the so-far hidden right to attention provides better ways shaping its future.

Yeong on Accessing and Using Data for AI Systems

Zee Kin Yeong (Singapore Academy Law) has posted “Accessing and Using Data for AI Systems” (The 4th Judicial Roundtable, 23 – 26 April 2024, at Durham Law School) on SSRN. Here is the abstract:

This paper discusses the copyright and data protection issues relating to accessing and using data for AI systems. The discussion commences with the issues relating to processing of data for model development. Secondary use of data, data sharing with partners or collection of publicly available data for machine learning raise issues such text and data mining in copyright law and legitimate interests in data protection law. Different issues have to be considered for the deploymentr of trained models, eg pre-deployment tests may be required under data protection laws for compliance with data security requirements. AI systems may rely on repositories and will require input data from users: there are also pertinent copyright and data protection issues to consider, such as data subject rights.

Müller et al. on Integrators at War: Mediating in AI-assisted Resort-to-Force Decisions

Dennis Müller (Centre the Study Existential Risk) et al. have posted “Integrators at War: Mediating in AI-assisted Resort-to-Force Decisions” on SSRN. Here is the abstract:

The integration of AI systems into the military domain is changing the way war-related decisions are made. It binds together three disparate groups of actors-developers, integrators, users-and creates a relationship between these groups and the machine, embedded in the (pre-)existing organisational and system structures. In this article, we focus on the important, but often neglected, group of integrators within such a sociotechnical system. In complex human-machine configurations, integrators carry responsibility for linking the disparate groups of developers and users in the political and military system. To act as the mediating group requires a deep understanding of the other groups’ activities, perspectives and norms. We thus ask which challenges and shortcomings emerge from integrating AI systems into resort-to-force (RTF) decision-making processes, and how to address them. To answer this, we proceed in three steps. First, we conceptualise the relationship between different groups of actors and AI systems as a sociotechnical system. Second, we identify challenges within such systems for human-machine teaming in RTF decisions. We focus on challenges that arise a) from the technology itself, b) from the integrators’ role in the sociotechnical system, c) from the human-machine interaction. Third, we provide policy recommendations to address these shortcomings when integrating AI systems into RTF decision-making structures.

Mantegna on ARTificial: Why Copyright Is Not the Right Policy Tool to Deal with Generative AI

Micaela Mantegna (Berkman Klein Center) has posted “ARTificial: Why Copyright Is Not the Right Policy Tool to Deal with Generative AI” (The Yale Law Journal Forum | April 22, 2024) on SSRN. Here is the abstract:

The rapid advancement and widespread application of Generative Artificial Intelligence (GAI) raise complex issues regarding authorship, originality, and the ethical use of copyrighted materials for AI training.

As attempts to regulate AI proliferate, this Essay proposes a taxonomy of reasons, from the perspective of creatives and society alike, that explain why copyright law is ill-equipped to handle the nuances of AI-generated content.

Originally designed to incentivize creativity, copyright doctrine has been expanded in scope to cover new technological mediums. This expansion has proven to increase the complexity and uncertainty of copyright doctrine’s application—ironically leading to the stifling of innovation. In this Essay, I warn that further attempts to expand the interpretation of copyright doctrine to accommodate the particularities of GAI might well worsen that problem, all while failing to fulfill copyright’s stated goal of protecting creators’ rights to consent, attribution, and compensation.

Moreover, I argue that, in that expansion, there is the peril of overreaching copyright laws that will negatively impact society and the development of ethical AI. This Essay explores the philosophical, legal, and practical dimensions of these challenges in four parts.

Garrett on Artificial Intelligence and Procedural Due Process

Brandon L. Garrett (Duke U Law) has posted “Artificial Intelligence and Procedural Due Process” on SSRN. Here is the abstract:

Artificial intelligence (AI) violates procedural due process rights if the government uses it to deprive people of life, liberty, and property without adequate notice or an opportunity to be heard. A wide range of government agencies deploy AI systems, including in courts, law enforcement, public benefits administration, and national security. If the government refused to disclose the reasons why it denied a person bail, public benefits, or immigration status, there would be substantial due process concerns. If the government delegates such tasks to an AI system, due process analysis does not change. As in any other setting, we still need to ask whether a person received adequate notice and an opportunity to heard. And further, where applicable, we need to ask whether the risk of error and costs to rights justify not using interpretable and adequately tested AI.

Nor is it necessary for AI or other automated systems to operate in a “black box” manner without providing people with notice or a way to meaningfully contest decisions. There is a ready alternative: a “glass box” or interpretable AI systems present results so that users know what factors it relied on, what weight it gave to each, and the strengths and limitations of the association or prediction made. Whether it is a criminal investigation or a public benefits eligibility determination, interpretable AI can ensure that people have notice and can challenge any error, using the procedures available. And such a system can be more readily checked for errors. Due process demands a greater opportunity to contest government decisions that raise greater reliability concerns. We need to know how reliable an AI system performs, under realistic conditions, to assess the risk of error.

Longstanding due process protections and well-developed interpretable AI approaches can ensure that AI systems safeguard due process rights. Conversely, due process rights have little meaning if the government uses “black box” systems that are not fully interpretable or fully tested for reliability, and as a result, cannot comply with procedural due process requirements. So far, there has been little government self-regulation of AI. In response, judges have begun to enforce existing due process rights in AI and other automated decisionmaking settings. As judges consider due process challenges to AI, they should consider the interpretability and the reliability of AI systems. Similarly, as lawmakers and regulators examine government use of AI systems, they should ensure safeguards, including interpretability and reliability, to protect our due process rights in an increasingly AI-dominated world.

Bertomeu et al. on On Humans and AI: A Financial Reporting Dilemma

Jeremy Bertomeu (Washington U St. Louis John M. Olin Business) et al. have posted “On Humans and AI: A Financial Reporting Dilemma” on SSRN. Here is the abstract:

Artificial intelligence presents unprecedented challenges in domains requiring nuanced ethical reasoning, particularly where objective training data is insufficient for complex moral judgments. This study investigates the ethical decision-making capabilities of large language models (LLMs) versus humans in an experiment featuring a board member’s dilemma over an untruthful, albeit well-understood by investors, reporting practice. Our research probes three fundamental questions: Do LLMs exhibit ethical judgments similar to humans, do they consistently apply ethical principles, and can they articulate the reasoning behind their moral choices? By comparing AI and human responses to an ethically ambiguous scenario, we aim to provide preliminary insights into the emerging role of artificial intelligence in navigating subjective decision-making processes. Our evidence contributes to our understanding of AI’s potential to augment or challenge human ethical judgment in high-stakes professional environments.

Huntington on AI Companions and the Lessons of Family Law

Clare Huntington (Columbia Law) has posted “AI Companions and the Lessons of Family Law” (110 Minn. L. Rev. (Forthcoming 2025)) on SSRN. Here is the abstract:

Virtual friends and lovers powered by artificial intelligence are rapidly moving to the center of our emotional and social lives. Millions of people turn to AI companions every day for conversation, romance, sexual intimacy, therapy, and education. AI companionship holds promise, potentially reducing loneliness, supporting people without access to mental health treatment, helping students learn, and offering a judgment-free space for sensitive conversations. But AI companionship also raises significant concerns. The technology’s addictiveness can undermine human relationships. Therapy bots may prove more harmful than helpful. AI companions can be emotionally abusive. And their access to the most intimate aspects of users’ lives poses distinct privacy challenges.

As lawmakers and policy experts reckon with the benefits and serious risks of AI companionship, they must account for the distinctive aspects of AI companionship. Unlike interacting with other forms of AI—being driven in an autonomous vehicle, say, or getting help with coding—people are in a relationship with their AI companion. Any regulatory approach must address this relationality, especially the human drive to attach to others and the vulnerability that comes with that attachment.

Legal scholars have long argued that the regulation of technology must account for relationality, and this Article demonstrates that family law—the law of relationships—is a ready means to do so. As a foundational matter, any effort to regulate AI companionship must explain why the legal system should act. Family law helps answer this question by debunking the widespread belief that relationships are purely a private matter. Family law establishes the strong state interest in nurturing positive relationships and addressing harm in abusive and neglectful relationships. These state interests apply not only to human relationships but also to human-AI relationships.

Family law also helps answer the question of how to regulate AI companionship. Family law recognizes, for example, that legal intervention is often necessary to shift the power imbalance that facilitates harmful relationships—a lesson that should be applied to the power imbalance between technology companies and people using AI companions. And family law teaches that expertise and licensing are necessary for mental health experts to work with a person at any age, although AI companions marketed for therapeutic purposes have not been subject to similar gatekeeping. Finally, family law holds lessons for advocacy, showing that it is possible to advance reasonable regulation notwithstanding the polarized political climate and considerable antipathy to regulating the technology industry, at least at the federal level. Family law points, for example, towards state-level interventions rather than action by Congress or federal agencies, and it demonstrates the broader acceptance of regulations targeted at minors than at adults.

In short, AI companionship is a new kind of relationship, bringing profound and unrecognized change to the landscape of our intimate lives. Legal scholars and policymakers must start grappling with this new world now. Family law holds great promise to accelerate that reckoning.