Julia Pohle (WZB Berlin Social Science Center) and Thorsten Thiel (same) have posted “Digital Sovereignty” (in Herlo, et al. (eds.): Practicing Sovereignty, Digital Involvement in Times of Crises (2021)) on SSRN. Here is the abstract:

Over the last decade, digital sovereignty has become a central element in policy discourses on digital issues. Although it has become popular in both centralized/authoritarian and democratic countries alike, the concept remains highly contested. After investigating the challenges to sovereignty apparently posed by the digital transformation, this essay retraces how sovereignty has re-emerged as a key category with regard to the digital. By systematizing the various normative claims to digital sovereignty, it then goes on to show how, today, the concept is understood more as a discursive practice in politics and policy than as a legal or organizational concept.

Eric Alston (University of Colorado) has posted “Norms, Institutions and Digital Veils of Ignorance – Do Network Protocols Need Trust Anyway?” on SSRN. Here is the abstract:

In larger groups, social rules reduce individuals’ uncertainty regarding the choices other individual group members might make. But uncertainty varies as to the extent to which it is knowable and quantifiable ex-ante. Therefore, different classes of social rules deal with the future uncertainty of individuals’ conduct in structurally distinct ways, with institutions and norms being the hallmark example of this distinction. Institutions, through their costly definition and enforcement by a known organization, require specific delineation of behavior and penalties ex-ante, meaning they of necessity confront “known unknowns” (risks), or the conduct of members of an organization that can be predicted ex-ante. Norms, in contrast, are only effective in shaping behavior if sufficiently shared within a community. This makes the application of norms automatic in expectation to an individual ordering their conduct given potential norms. This makes norms apply to ex-ante known and unknown situations alike, relative to the precision that the articulation of institutions requires with respect to human behavior. Although digital governance carries the benefits (and costs) of considerable institutional “completeness”, governance by protocol is nonetheless incomplete in the face of the complex set of exogenous shocks and human actions that a given digital networked organization will experience. This means digital institutions need to mimic the adaptability of institutions more generally, through the institutional mechanisms of flexibility detailed in this analysis, considered with respect to their specific application to distributed blockchain and centralized networks alike. More generally, though, the fact that norms can serve as a complementary gap-filler in contexts where institutions do not reach suggest that digital organization designers cannot avoid simultaneous consideration of the human community of network users that will define the norms that become crucial in periods of true uncertainty for any organization.

Recommended.

Sofia Ranchordas (University of Groningen, Faculty of Law; LUISS) has posted “Experimental lawmaking in the EU: Regulatory Sandboxes” (EU Law Live) on SSRN. Here is the abstract:

Regulatory sandboxes, experimental clauses, and experimental regulations are relatively unknown terms in EU law. The term ‘experimental lawmaking’ is elusive and it is unclear how experimental laws and regulations fit within existing EU law frameworks. Regulatory sandboxes are a leading and recent example of experimental lawmaking which started at national level and is now slowly making its way into the EU law toolbox.

Regulatory sandboxes are experimental legal regimes which waive, modify national regulatory requirements (or implementation) or provide bespoke guidance on a temporary basis and for a limited number of actors in order to support businesses in their innovation endeavors. A regulatory sandbox offers safe testbeds for innovative products and services without putting the whole system at risk. Sandboxing aims to promote thus the advancement of technology, new policy solutions through the promotion of collaborative regulation, and novel compliance initiatives between innovators and regulators. After a brief experience of national implementation in the financial, energy, healthcare, telecommunications, and data protection sectors, the EU has embraced the potential of regulatory sandboxes in its AI Regulation Proposal. Nevertheless, there are still many unknowns in the world of EU experimental lawmaking. The definition, modus operandi, regulatory implications as well as the design and methodology of experimental regulations and regulatory sandboxes will determine whether this experimental approach to law and regulation will indeed be successful and help advance responsible innovation in the EU. In this contribution, I draw upon recent scholarship and national experiences with regulatory sandboxes to shed light on the legal nature, innovative potential, and methodology of this instrument.

Recommended.

Benjamin Alarie (University of Toronto – Faculty of Law) and Arthur J. Cockfield (Queen’s University – Faculty of Law) have posted “Will Machines Replace Us? Machine-Authored Texts and the Future of Scholarship” (Law, Technology and Humans, volume 3(2) (2021 Forthcoming) on SSRN. Here is the abstract:

We present here the first machine-generated law review article. Our self-interest motivates us to believe that knowledge workers who write complex articles drawing upon years of research and effort are safe from AI developments. However, how reasonable is it to persist in this belief given recent advances in AI research? With that topic in mind, we caused GPT-3, a state-of-the-art AI, to generate a paper that explains “why humans will always be better lawyers, drivers, CEOs, presidents, and law professors than artificial intelligence and robots can ever hope to be.” The resulting paper, with no edits apart from giving it a title and bolding the headings generated by GPT-3, is reproduced below. It is imperfect in a humorous way. Ironically, it is publishable “as-is” only because it is machine-generated. Nevertheless, the resulting paper is good enough to give us some pause for thought. Although GPT-3 is not up to the task of replacing law review authors currently, we are far less confident that GPT-5 or GPT- 100 might not be up to the task in the future.

Horst Eidenmueller (University of Oxford Law; ECGI) has posted “Why Personalized Law?” (U. Chi. L. Rev. Online (Forthcoming) on SSRN. Here is the abstract:

Big data and advances in Artificial Intelligence (AI) have made it possible to personalize legal rules. In this essay, I investigate the question of whether laws should be personalized. Omri Ben-Shahar and Ariel Porat argue that personalized law could be a “precision tool” to achieve whatever goal the lawmaker wants to achieve. This argument is not convincing. The most “natural” fit and best normative justification for a personalized law program is welfarism/utilitarianism. This is because personalized law and welfarism/utilitarianism are both based on normative individualism. But welfarism/utilitarianism is a highly problematic social philosophy. Against this background, it becomes clear why personalized law should only have a limited role to play in lawmaking. The focus of state action should not be the design and running of a personalized law program. Rather, it should be on controlling “wild personalization” by powerful private actors.

Michiel Poesen (KU Leuven – Faculty of Law) has posted “Regulating Artificial Intelligence in the European Union: Exploring the Role of Private International Law” on SSRN. Here is the abstract:

This paper explores the role that private international law could play in regulating artificial intelligence (AI) in the European Union (EU). It will conclude that private international law has the potential of being a piece in a complex regulatory puzzle, which nonetheless has the potential of offering an effective contribution to ensuring accountable AI.

Ellen P. Goodman (Rutgers Law) has posted “The Stakes of User Interface Design for Democracy” on SSRN. Here is the abstract:

Digital design choices such as color and font, the size and placement of action buttons, and the number of steps required to execute an action all shape the user experience (UX) and what information people absorb and release. Digital platforms and service providers shape the UX in ways that can be respectful of user autonomy and advance accurate, high-quality information, or in ways that subvert user choice and promote deception. Social media platforms have used “deceptive design” in many respects, making it easier to manipu­late users into taking actions, sacrificing data, and succumbing to beliefs they might not otherwise want to. This paper proposes that platforms replace “decep­tive design” with empowering or “democratic design.” Regulators have incorporated design best practices in a number of offline policies. This paper surveys key examples, ranging from emissions labels on cars to health warnings on cigarette packs where regulations were guided by design principles. Design best practices should inform online policy as well.

Ryan Calo (U Washington School of Law) has posted “Modeling Through” (Duke Law Journal, Vol. 72, Forthcoming 2021) on SSRN. Here is the abstract:

Theorists of justice have long imagined a decision-maker capable of acting wisely in every circumstance. Policymakers seldom live up to this ideal. They face well-understood limits, including an inability to anticipate the societal impacts of state intervention along a range of dimensions and values. Policymakers cannot see around corners or address societal problems at their roots. When it comes to regulation and policy-setting, policymakers are often forced, in the memorable words of political economist Charles Lindblom, to “muddle through” as best they can.

Powerful new affordances, from supercomputing to artificial intelligence, have arisen in the decades since Lindblom’s 1959 article that stand to enhance policymaking. Computer-aided modeling holds promise in delivering on the broader goals of forecasting and system analysis developed in the 1970s, arming policymakers with the means to anticipate the impacts of state intervention along several lines—to model, instead of muddle. A few policymakers have already dipped a toe into these waters, others are being told that the water is warm.

The prospect that economic, physical, and even social forces could be modeled by machines confronts policymakers with a paradox. Society may expect policymakers to avail themselves of techniques already usefully deployed in other sectors, especially where statutes or executive orders require the agency to anticipate the impact of new rules on particular values. At the same time, “modeling through” holds novel perils that policymakers may be ill-equipped to address. Concerns include privacy, brittleness, and automation bias of which law and technology scholars are keenly aware. They also include the extension and deepening of the quantifying turn in governance, a process that obscures normative judgments and recognizes only that which the machines can see. The water may be warm but there are sharks in it.

These tensions are not new. And there is danger in hewing to the status quo. (We should still pursue renewable energy even though wind turbines as presently configured waste energy and kill wildlife.) As modeling through gains traction, however, policymakers, constituents, and academic critics must remain vigilant. This being early days, American society is uniquely positioned to shape the transition from muddling to modeling.

Recommended.

Jens Ludwig (Georgetown University; NBER ) and Sendhil Mullainathan (University of Chicago) have posted “Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System” on SSRN. Here is an excerpt:

One reason for their fragility comes from important econometric problems that are often overlooked in building algorithms. Decades of empirical work by economists show that in almost every data application the data is incomplete, not fully representing either the objectives or the information that decision-makers possess. For example, judges rely on much more information
than is available to algorithms, and judges’ goals are often not well-represented by the outcomes provided to algorithms. These problems, familiar to economists, riddle every case where algorithms are being applied. […] Existing regulations provide
weak incentives for those building or buying algorithms, and little ability to police these choices.

For a method of providing stronger incentives for those building and buying algorithms, see Frank Fagan & Saul Levmore, Competing Algorithms for Law, 88 U. Chicago Law Rev.

Daniel Maggen (Yale Law School) has posted “Predict and Suspect: The Emergence of Artificial Legal Meaning” (North Carolina Journal of Law and Technology, Vol. 23, No. 1, 2021) on SSRN. Here is the abstract:

Recent theoretical writings on the possibility that algorithms would someday be able to create law have delayed algorithmic law-making, and the need to decide on its legitimacy, to some future time in which algorithms would be able to replace human lawmakers. This Article argues that such discussions risk essentializing an anthropomorphic image of the algorithmic lawmaker as a unified decision-maker and divert attention away from algorithmic systems that are already performing functions that together have a profound effect on legal implementation, interpretation, and development. Adding to the rich scholarship of the distortive effects of algorithmic systems, the Article suggests that state-of-the-art algorithms capable of limited legal analysis can have the effect of preventing legal development. Such algorithm-induced ossification, the Article argues, raises questions of legitimacy that are no less consequential than those raised by some futuristic algorithms that can actively create norms.

To demonstrate this point, the Article puts forward a hypothetical example of algorithms performing limited legal analysis to assist healthcare professionals in reporting suspected child maltreatment. Already in use are systems performing risk analysis to aid child protective services in screening maltreatment reports. Drawing on the example of algorithms increasingly used today in social media content moderation, the Article suggests that similar systems could be used for flagging cases that show signs of suspected abuse. Such assistive systems, the Article argues, will likely cement the prevailing legal meaning of maltreatment. As mandated reporters increasingly rely on such systems, the result would be the absence of legal evolution, preventing changes to contentious elements in the legal definition of reportable suspicion, including the scope of acceptable physical disciplining. Together with the familiar effect of existing systems, the effect of this hypothetical system could have a profound effect on the path of the law on child maltreatment, equivalent in its significance to the effect autonomous algorithmic adjudication would have.

Recommended.