Weitzel on Defining Artificial Intelligence

Paul D. Weitzel (U Nebraska College Law) has posted “Defining Artificial Intelligence” on SSRN. Here is the abstract:

Nations and international bodies are attempting to regulate artificial intelligence (AI), but few are able to define what artificial intelligence is. This article reviews how we define AI.

The article first reviews definitions provided by AI researchers, noting three difficulties. First, there is no agreed upon definition to identify intelligence, so it’s unlikely we can identify its counterfeit. Second, we shift the goalposts. As AI becomes more capable, tasks that were previously believed to require intelligence are now seen as mere memorization and computation. Chess was thought to be a strong signal of intelligence, but now that chess bots dominate, chess mastery is seen as mere computation. Third, many judge the intelligence of an AI model by the mistakes that it makes. A model that can solve Ph.D.-level science questions and outperform doctors in diagnosis is considered unintelligent because it cannot count the number of Rs in strawberry or do simple multiplication. Because we judge a machine to be as intelligent as its silliest errors, we underestimate machine capabilities.

Next, the article surveys the definitions used by AI researchers, then analyzes 105 definitions of artificial intelligence used in regulations, legislation, national strategies and international agreements among 62 jurisdictions and international bodies. The review finds that most definitions of artificial intelligence lack a basic understanding of the technology, which makes them inapplicable, vague or overinclusive. A quarter of the jurisdictions surveyed use definitions that would treat a sundial as artificial intelligence. Another third do not define artificial intelligence at all.

The article also shows why definitions of AI are likely to fail, making sectoral regulation of AI over- or under-inclusive. Artificial intelligence raises critical concerns about privacy, employment, bias, creativity, autonomous weapons, misinformation, wealth inequality, human rights and what it means to be human. But we cannot address these concerns if we cannot define what it is. This article defines the terms of the coming debate.

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.

Gordon-Tapiero on A Liability Framework for AI Companions

Ayelet Gordon-Tapiero (Hebrew U Jerusalem Benin Computer Science and Engineering) has posted “A Liability Framework for AI Companions” (1 Geo. Wash. J. L. & Tech (Forthcoming)) on SSRN. Here is the abstract:

Everyday tens of millions of people engage in online conversations. These virtual interactions range from casual chats about daily life to deeply personal exchanges, where individuals share secrets, vulnerabilities, sexual fantasies, hopes and dreams. Through these conversations users receive emotional support and empathetic responses, get practical advice and productivity tips. Most importantly they feel seen, heard, and less alone. These people are not chatting with friends or family members. They are corresponding with AI-powered chatbots, also known as AI companions, which have gained immense popularity recently. AI companions offer a range of benefits to users including providing a feeling of friendship, emotional support, and organization of everyday tasks. But AI companions also harbor a darker side. They are designed by large corporations with the goal of maximizing their profits and collecting more data on which to train future models. Users often find themselves subject to manipulation, growing emotional dependence, and even addiction. Tragically, it is the most vulnerable users that are most susceptible to these harms. In a horrific case, a teenager even took his own life after being encouraged to do so by his AI companion.

Against this backdrop, this Article argues for the urgent need to develop a comprehensive legal response to the emerging ecosystem of AI companions. Specifically, it proposes applying products-liability law to AI companions as a promising legal avenue. This Article also offers a typology of the promises and perils associated with the use of AI companions. Recognizing both the benefits and harms stemming from a technology is a crucial first step in crafting a regulatory response that preserves its advantages while mitigating its risks.

AI companions are designed to maximize the profits of the companies that develop them by facilitating engagement and fostering dependency, which can lead to addiction. In this reality, users’ interests are secondary at best. Courts have long recognized two types of product defects that can give rise to liability: design defects and failure to warn. Thus, an AI companion designed to maximize user engagement, encourage user-dependence and facilitate addiction could be considered to have been defectively designed. Similarly, companies deploying AI companions known to harm vulnerable users should, at the very least, warn them of these risks.

Products-liability law offers an appropriate and necessary framework for addressing the challenges posed by AI companions. It allows courts to gradually establish standards for what should be considered a defective product, while holding companies accountable for their failure to warn users about potential dangers. This approach incentivizes companies to design safer products, limiting the harms generated by AI companions, while allowing users to continue enjoying the benefits offered by them.

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.