Author: Frank Fagan

Almada on Regulating Machine Learning by Design

Marco Almada (European University Institute – Department of Law) has posted “Regulating Machine Learning by Design” (CPI TechREG Chronicle, February 2023)

The regulation of digital technologies around the world draws from various regulatory techniques. One such technique is regulation by design, in which regulation specify requirements that software designers must follow when creating any systems. This paper examines the suitability of regulation by design approaches to machine learning, arguing that they are potentially useful but have a narrow scope of application. Drawing from EU law examples, it shows how regulation by design relies on the delegation of normative definitions and enforcement to software designers, but such delegation is only effective if a few conditions are present. These conditions, however, are seldom met by applications of machine learning technologies in the real world, and so regulation by design cannot address many of the pressing concerns driving regulation. Nonetheless, by-design provisions can support regulation if applied to well-defined problems that lend themselves to clear expression in software code. Hence, regulation by design, within its proper limits, can be a powerful tool for regulators of machine learning technologies.

Bar-Gill, Sunstein & Talgam-Cohen on Algorithmic Harm in Consumer Markets

Oren Bar-Gill (Harvard Law), Cass R. Sunstein (Harvard Law; Harvard Kennedy School), and Inbal Talgam-Cohen (Technion-Israel Institute of Technology) have posted “Algorithmic Harm in Consumer Markets” on SSRN. Here is the abstract:

Machine learning algorithms are increasingly able to predict what goods and services particular people will buy, and at what price. It is possible to imagine a situation in which relatively uniform, or coarsely set, prices and product characteristics are replaced by far more in the way of individualization. Companies might, for example, offer people shirts and shoes that are particularly suited to their situations, that fit with their particular tastes, and that have prices that fit their personal valuations. In many cases, the use of algorithms promises to increase efficiency and to promote social welfare; it might also promote fair distribution. But when consumers suffer from an absence of information or from behavioral biases, algorithms can cause serious harm. Companies might, for example, exploit such biases in order to lead people to purchase products that have little or no value for them or to pay too much for products that do have value for them. Algorithmic harm, understood as the exploitation of an absence of information or of behavioral biases, can disproportionately affect members of identifiable groups, including women and people of color. Since algorithms exacerbate the harm caused to imperfectly informed and imperfectly rational consumers, their increasing use provides fresh support for existing efforts to reduce information and rationality deficits, especially through optimally designed disclosure mandates. In addition, there is a more particular need for algorithm-centered policy responses. Specifically, algorithmic transparency—transparency about the nature, uses, and consequences of algorithms—is both crucial and challenging; novel methods designed to open the algorithmic “black box” and “interpret” the algorithm’s decision-making process should play a key role. In appropriate cases, regulators should also police the design and implementation of algorithms, with a particular emphasis on exploitation of an absence of information or of behavioral biases.

Fagan on Law’s Computational Paradox

Frank Fagan (South Texas College of Law Houston) has posted “Law’s Computational Paradox” (Virginia Journal of Law and Technology, forthcoming) on SSRN. Here is the abstract:

Artificial intelligence (AI) and machine learning will bring about many changes to how law is practiced, made, and enforced. However, machines cannot do everything that humans can do, and law must face the limitations of computational learning just as much as any other human endeavor. For predictive learning, these limitations are permanent and can be used to ascertain the future of law. The basic tasks of lawyering, such as brief writing, oral argument, and witness coaching, will become increasingly precise, but that precision will eventually plateau, and the essential character of lawyering will remain largely unchanged. Similarly, where machines can be used to clarify application of law, they simply will limit judicial discretion consistent with moves from standards to rules or from rules to personalized law.

In each of these scenarios—lawyering and case clarification—enhanced precision is made possible through systemic closure of the machine’s domain and AI will ascend easily. In scenarios where law’s architecture is open, and systemic closure is not possible or worth it, machines will be frustrated by an inability to discern patterns, or by a powerlessness to comprehend the predictive power of previously discerned patterns in newly changed contexts. Lawmakers may add new variables to compensate and encourage attempts to model future environments, but open innovation and social change will undermine even a determined empiricism. In response to these limitations, lawmakers may attempt to actively impose closure of dynamic legal domains in an effort to enhance law’s precision. By limiting admissibility of evidence, black-listing variables, requiring specific thresholds of white-listed variables, and pursuing other formalist strategies of closure, law can elevate its predictive precision for a given environment, but this elevation comes at the expense of openness and innovation. This is law’s computational paradox.

This Article introduces the paradox across machine learning applications in lawmaking, enforcement, rights allocation, and lawyering, and shows that innovation serves as a self-corrective to the excessive mechanization of law. Because innovation, change, and open legal domains are necessary ingredients for continual technological ascendance in law and elsewhere, fears of AI-based law as an existential threat to human-centered law are exaggerated. It should be emphasized, however, that there is ample room for quantification and counting in both closed and open settings; the products of innovation will always undergo measurement and machine learning algorithms will always require updating and refinement. This is the process of technological becoming. The goal for law is to never fully arrive.

The uncertainty of dynamic legal environments, even if diminishing with growing predictive power in law, forms the basis of an interpersonal constitutional authority. Understanding that some disruptions will always be unplanned prevents the construction of blind pathways for longer-term legal error, and relatedly, prevents empirical and technical rationales from overrunning a human-centered public square. A growing awareness of paradoxical error generated by precise, but closed, computational environments will generate a societal response that seeks to balance the benefits of precision and innovation. This balancing—what might be termed a “computational legal ethics”—implies that tomorrow’s lawyers, more so than their counterparts of the past, will be called upon to discern what should be considered versus ignored.

Sunstein on The Use of Algorithms in Society

Cass R. Sunstein (Harvard Law School) has posted “The Use of Algorithms in Society” on SSRN. Here is the abstract:

The judgments of human beings can be biased; they can also be noisy. Across a wide range of settings, use of algorithms is likely to improve accuracy, because algorithms will reduce both bias and noise. Indeed, algorithms can help identify the role of human biases; they might even identify biases that have not been named before. As compared to algorithms, for example, human judges, deciding whether to give bail to criminal defendants, show Current Offense Bias and Mugshot Bias; as compared to algorithms, human doctors, deciding whether to test people for heart attacks, show Current Symptom Bias and Demographic Bias. These are cases in which large data sets are able to associate certain inputs with specific outcomes. But in important cases, algorithms struggle to make accurate predictions, not because they are algorithms but because they do not have enough data to answer the question at hand. Those cases often, though not always, involve complex systems. (1) Algorithms might not be able to foresee the effects of social interactions, which can depend on a large number of random or serendipitous factors, and which can lead in unanticipated and unpredictable directions. (2) Algorithms might not be able to foresee the effects of context, timing, or mood. (3) Algorithms might not be able to identify people’s preferences, which might be concealed or falsified, and which might be revealed at an unexpected time. (4) Algorithms might not be able to anticipate sudden or unprecedented leaps or shocks (a technological breakthrough, a successful terrorist attack, a pandemic, a black swan). (5) Algorithms might not have “local knowledge,” or private information, which human beings might have. Predictions about romantic attraction, about the success of cultural products, and about coming revolutions are cases in point. The limitations of algorithms are analogous to the limitations of planners, emphasized by Hayek in his famous critique of central planning. It is an unresolved question whether and to what extent some of the limitations of algorithms might be reduced or overcome over time, with more data or various improvements; calculations are improving in extraordinary ways, but some of the relevant challenges cannot be solved with ex ante calculations.

Coglianese on Regulating Machine Learning: The Challenge of Heterogeneity

Cary Coglianese (U Penn Law) has posetd “Regulating Machine Learning: The Challenge of Heterogeneity” (Competition Policy International: TechReg Chronicle, February 2023) on SSRN. Here is the abstract:

Machine learning, or artificial intelligence, refers to a vast array of different algorithms that are being put to highly varied uses, including in transportation, medicine, social media, marketing, and many other settings. Not only do machine-learning algorithms vary widely across their types and uses, but they are evolving constantly. Even the same algorithm can perform quite differently over time as it is fed new data. Due to the staggering heterogeneity of these algorithms, multiple regulatory agencies will be needed to regulate the use of machine learning, each within their own discrete area of specialization. Even these specialized expert agencies, though, will still face the challenge of heterogeneity and must approach their task of regulating machine learning with agility. They must build up their capacity in data sciences, deploy flexible strategies such as management-based regulation, and remain constantly vigilant. Regulators should also consider how they can use machine-learning tools themselves to enhance their ability to protect the public from the adverse effects of machine learning. Effective regulatory governance of machine learning should be possible, but it will depend on the constant pursuit of regulatory excellence.

Guerra-Pujol on Truth Markets

F. E. Guerra-Pujol (Pontifical Catholic University of Puerto Rico; University of Central Florida) has posted “Truth Markets” on SSRN. Here is the abstract:

A growing chorus of legal scholars and policy makers have decried the proliferation of false information on the Internet–e.g. fake news, conspiracy theories, and the like–while at the same time downplaying the dangers of Internet censorship, including shadow bans, arbitrary or selective enforcement of content moderation policies, and other forms of Internet speech suppression. This Article proposes a simple alternative to censorship: a truth market.

Solove & Hartzog on Data Vu: Why Breaches Involve the Same Stories Again and Again

Daniel J. Solove (George Washington University Law School) and Woodrow Hartzog (Boston University School of Law; Stanford Law School Center for Internet and Society) have posted “Data Vu: Why Breaches Involve the Same Stories Again and Again” (Scientific American (July 2022)) on SSRN. Here is the abstract:

This short essay discusses why data security law fails to effectively combat data breaches, which continue to increase. With a few exceptions, current laws about data security do not look too far beyond the blast radius of the most data breaches. Only so much marginal benefit can be had by increasing fines to breached entities. Instead, the law should target a broader set of risky actors, such as producers of insecure software and ad networks that facilitate the distribution of malware. Organizations that have breaches almost always could have done better, but there’s only so much marginal benefit from beating them up. Laws could focus on holding other actors more accountable, so responsibility is more aptly distributed.

Zuboff on Surveillance Capitalism or Democracy? The Death Match of Institutional Orders and the Politics of Knowledge

Shoshana Zuboff (Harvard Business School; Harvard Kennedy School) has posted “Surveillance Capitalism or Democracy? The Death Match of Institutional Orders and the Politics of Knowledge in Our Information Civilization” (Organization Theory, 3(3), 2022) on SSRN. Here is the abstract:

Surveillance capitalism is what happened when US democracy stood down. Two decades later, it fails any reasonable test of responsible global stewardship of digital information and communications. The abdication of the world’s information spaces to surveillance capitalism has become the meta-crisis of every republic because it obstructs solutions to all other crises. The surveillance capitalist giants–Google, Apple, Facebook, Amazon, Microsoft, and their ecosystems–now constitute a sweeping political-economic institutional order that exerts oligopolistic control over most digital information and communication spaces, systems, and processes.

The commodification of human behavior operationalized in the secret massive-scale extraction of human-generated data is the foundation of surveillance capitalism’s two-decade arc of institutional development. However, when revenue derives from commodification of the human, the classic economic equation is scrambled. Imperative economic operations entail accretions of governance functions and impose substantial social harms. Concentration of economic power produces collateral concentrations of governance and social powers. Oligopoly in the economic realm shades into oligarchy in the societal realm. Society’s ability to respond to these developments is thwarted by category errors. Governance incursions and social harms such as control over AI or rampant disinformation are too frequently seen as distinct crises and siloed, each with its own specialists and prescriptions, rather than understood as organic effects of causal economic operations.

In contrast, this paper explores surveillance capitalism as a unified field of institutional development. Its four already visible stages of development are examined through a two-decade lens on expanding economic operations and their societal effects, including extraction and the wholesale destruction of privacy, the consequences of blindness-by-design in human-to-human communications, the rise of AI dominance and epistemic inequality, novel achievements in remote behavioral actuation such as the Trump 2016 campaign, and Apple-Google’s leverage of digital infrastructure control to subjugate democratic governments desperate to fight a pandemic. Structurally, each stage creates the conditions and constructs the scaffolding for the next, and each builds on what went before. Substantively, each stage is characterized by three vectors of accomplishment: novel economic operations, governance carve-outs, and fresh social harms. These three dimensions weave together across time in a unified architecture of institutional development. Later-stage harms are revealed as effects of the foundational-stage economic operations required for commodification of the human.

Surveillance capitalism’s development is understood in the context of a larger contest with the democratic order—the only competing institutional order that poses an existential threat. The democratic order retains the legitimate authority to contradict, interrupt, and abolish surveillance capitalism’s foundational operations. Its unique advantages include the ability to inspire action and the necessary power to make, impose, and enforce the rule of law. While the liberal democracies have begun to engage with the challenges of regulating today’s privately owned information spaces, I argue that regulation of institutionalized processes that are innately catastrophic for democratic societies cannot produce desired outcomes. The unified field perspective suggests that effective democratic contradiction aimed at eliminating later-stage harms, such as “disinformation,” depends upon the abolition and reinvention of the early-stage economic operations that operationalize the commodification of the human, the source from which such harms originate.

The clash of institutional orders is a death match over the politics of knowledge in the digital century. Surveillance capitalism’s antidemocratic economic imperatives produce a zero-sum dynamic in which the deepening order of surveillance capitalism propagates democratic disorder and deinstitutionalization. Without new public institutions, charters of rights, and legal frameworks purpose-built for a democratic digital century, citizens march naked, easy prey for all who steal and hunt with human data. Only one of these contesting orders will emerge with the authority and power to rule, while the other will drift into deinstitutionalization, its functions absorbed by the victor. Will these contradictions ultimately defeat surveillance capitalism, or will democracy suffer the greater injury? It is possible to have surveillance capitalism, and it is possible to have a democracy. It is not possible to have both.

Custers on AI in Criminal Law

Bart Custers (Leiden University – Center for Law and Digital Technologies) has posted “AI in Criminal Law: An Overview of AI Applications in Substantive and Procedural Criminal Law” (in: B.H.M. Custers & E. Fosch Villaronga (eds.) Law and Artificial Intelligence, Heidelberg: Springer, p. 205-223.) on SSRN. Here is the abstract:

Both criminals and law enforcement are increasingly making use of the opportunities that AI may offer, opening a whole new chapter in the cat-and-mouse game of committing versus addressing crime. This chapter maps the major developments of AI use in both substantive criminal law and procedural criminal law. In substantive criminal law, A/B optimisation, deepfake technologies, and algorithmic profiling are examined, particularly the way in which these technologies contribute to existing and new types of crime. Also the role of AI in assessing the effectiveness of sanctions and other justice-related programs and practices is examined, particularly risk taxation instruments and evidence-based sanctioning. In procedural criminal law, AI can be used as a law enforcement technology, for instance, for predictive policing or as a cyber agent technology. Also the role of AI in evidence (data analytics after search and seizure, Bayesian statistics, developing scenarios) is examined. Finally, focus areas for further legal research are proposed.

Kolt on Algorithmic Black Swans

Noam Kolt (University of Toronto) has posted “Algorithmic Black Swans” (Washington University Law Review, Vol. 101, Forthcoming) on SSRN. Here is the abstract:

From biased lending algorithms to chatbots that spew violent hate speech, AI systems already pose many risks to society. While policymakers have a responsibility to tackle pressing issues of algorithmic fairness, privacy, and accountability, they also have a responsibility to consider broader, longer-term risks from AI technologies. In public health, climate science, and financial markets, anticipating and addressing societal-scale risks is crucial. As the COVID-19 pandemic demonstrates, overlooking catastrophic tail events — or “black swans” — is costly. The prospect of automated systems manipulating our information environment, distorting societal values, and destabilizing political institutions is increasingly palpable. At present, it appears unlikely that market forces will address this class of risks. Organizations building AI systems do not bear the costs of diffuse societal harms and have limited incentive to install adequate safeguards. Meanwhile, regulatory proposals such as the White House AI Bill of Rights and the European Union AI Act primarily target the immediate risks from AI, rather than broader, longer-term risks. To fill this governance gap, this Article offers a roadmap for “algorithmic preparedness” — a set of five forward-looking principles to guide the development of regulations that confront the prospect of algorithmic black swans and mitigate the harms they pose to society.