Grennan on FinTech Regulation in the United States: Past, Present, and Future

Jillian Grennan (Duke University – Fuqua School of Business) has posted “FinTech Regulation in the United States: Past, Present, and Future” on SSRN. Here is the abstract:

This study reviews the regulatory issues developers and users of emerging financial technologies face as use cases expand. DeFi and DAOs, which build upon advances in AI and blockchain, reduce the cost of coordinating complex financial services. Yet the efficiency gains intertwine with potential legal risks associated with liability, financial crime, dispute resolution, jurisdiction, and taxes. Regulatory solutions may include adapted definitions and safe harbors, regulatory sandboxes, self-regulatory organizations, and/or policing misleading characterizations (e.g., regarding the extent of decentralization or agreed to data uses). As it will take time for regulators to implement effective policies, stakeholders can still influence policy.

Jones on A Civil Strategization of AI in Germany

Maurice Jones (Concordia University; Humboldt Institute for Internet and Society) has posted “Towards Civil Strategization of AI in Germany” on SSRN. Here is the abstract:

The involvement of civil society has been identified as key in ensuring ethical and equitable approaches towards the governance of AI by a variety of state and non-state actors. Civil society carries the potential to hold organisations and institutions accountable, to advocate for marginalised voices to be heard, to spearhead ethically sound applications of AI, and to mediate between a variety of different perspectives. Despite proclaimed ambitions and visible potentials, civil society actors face great challenges in actively engaging in the governance of AI. Based upon a survey of the involvement of civil society actors in the making of the German National Artificial Intelligence Strategy this discussion paper identifies and contextualises key challenges that hinder civil society’s fruitful participation in the governance of AI in Germany. These hurdles include existing structural challenges commonly faced by civil society actors, such as a notorious lack of financial and human resources, as well as broader questions of governance, such as interministerial competition, and a lack foresight in the design of participatory processes. Additional challenges related to technology governance, such as a lack of expertise not only in civil society but also among ministries and industry, are amplified within the rapidly evolving field of AI. Leveraging the potential of civil society’s involvement requires reevaluation of the relationship between civil society, state, and economic actors.

Lee on Investor Protection on Crowdfunding Platforms

Joseph Lee (School of Law, University of Manchester) has posted “Investor Protection on Crowdfunding Platforms” (The EU Crowdfunding Regulation, OUP) on SSRN. Here is the abstract:

This paper discusses the protection of investors on crowdfunding platforms under the Crowdfunding Regulation. Although there are many provisions in the regulation that protect investors, this paper concentrates specifically on those included under the heading of Caper IV ‘Investor protection’ of the Crowdfunding Regulation.

This paper focuses on how investor protection can contribute to the objectives of crowdfunding and, in particular, how the provisions of the Crowdfunding Regulation serve this purpose. To this end, Section 2 discusses the investor-focused objectives of crowdfunding, and the role that technology can play in realising these objectives. Section 3 considers the meaning of investor protection within the scope of the Crowdfunding Regulation, and identifies areas where the current regime might be extended in the future. Section 4 discusses the categorisation of investors and the relevance thereof for the investor protection. Major provisions pertinent to investor protection are subsequently discussed in Sections 5 to 9, including the information to be provided to clients, default rate disclosure, the entry knowledge test and the simulation of ability to bear loss, the pre-contractual reflection period, and the key investment information sheet. The Sections also contain reflections pertinent to the different topics discussed in order to put them in a greater context. Section 10 concludes.

Henderson Reviewing When Machines Can Be Judge, Jury, and Executioner

Stephen E. Henderson (University of Oklahoma – College of Law) has posted a review of Katherine Forrest’s “When Machines Can Be Judge, Jury, and Executioner” (Book Review: Criminal Law and Criminal Justice Books 2022) on SSRN. Here is the abstract:

There is much in Katherine Forrest’s claim—and thus in her new book—that is accurate and pressing. Forrest adds her voice to the many who have critiqued contemporary algorithmic criminal justice, and her seven years as a federal judge and decades of other experience make her perspective an important one. Many of her claims find support in kindred writings, such as her call for greater transparency, especially when private companies try to hide algorithmic details for reasons of greater profit. A for-profit motive is a fine thing in a private company, but it is anathema to our ideals of public trial. Algorithms are playing an increasingly dominant role in criminal justice, including in our systems of pretrial detention and sentencing. And as we criminal justice scholars routinely argue, there is much that is rather deeply wrong in that criminal justice.

But the relation between those two things—algorithms on the one hand and our systems of criminal justice on the other—is complicated, and it most certainly does not run any single direction. Just as often as numbers and formulae are driving the show (a right concern of Forrest’s), a terrible dearth of both leaves judges meting out sentences that, in the words of Ohio Supreme Court Justice Michael Donnelly, “have more to do with the proclivities of the judge you’re assigned to, rather than the rule of law.” Moreover, most of the algorithms we currently use—and even most of those we are contemplating using—are ‘intelligent’ in only the crudest sense. They constitute ‘artificial intelligence’ only if we term every algorithm run by, or developed with the assistance of, a computer to constitute AI, and that is hardly the kind of careful, precise definition that criminal justice deserves. A calculator is a machine that we most certainly want judges using, a truly intelligent machine is something we humans have so far entirely failed to create, and the spectrum between is filled with innumerable variations, each of which must be carefully, scientifically evaluated in the particular context of its use.

This brief review situates Forrest’s claims in these two regards. First, we must always compare apples to apples. We ought not compare a particular system of algorithmic justice to some elysian ideal, when the practical question is whether to replace and/or supplement a currently biased and logically-flawed system with that algorithmic counterpart. After all, the most potently opaque form of ‘intelligence’ we know is that we term human—we humans go so far as routine, affirmative deception—and that truth calls for a healthy dose of skepticism and humility when it comes to claims of human superiority. Comparisons must be, then, apples to apples. Second, when we speak of ‘artificial intelligence,’ we ought to speak carefully, in a scientifically precise manner. We will get nowhere good if we diverge into autonomous weapons when trying to decide, say, whether we ought to run certain historic facts about an arrestee through a formula as an aid to deciding whether she is likely to appear as required for trial. The same if we fail to understand the very science upon which any particular algorithm runs. We must use science for science.

Creemers on China’s Emerging Data Protection Framework

Rogier Creemers (Leiden University) has posted “China’s Emerging Data Protection Framework” on SSRN. Here is the abstract:

Over the past five years, the People’s Republic of China has accelerated efforts to establish a legal architecture for data protection. With the promulgation of the Personal Information Protection Law (PIPL) and the Data Protection Law (DSL) in the summer of 2021, the first phase of these efforts have been concluded. These will have a significant impact on data flows within China, but also merit foreign attention. They provide a new approach to data protection to be subjected to comparative analysis, and may influence the development of data protection legislation in other states, particularly those with close digital connections to China. Doing so requires a greater understanding of how this legislation is shaped by the Chinese political and economic context.

Drawing on a thorough review of government documents, supplemented by Chinese-language academic sources, this article reviews the evolution of the two pillars of China’s data protection architecture, from the early stage of fragmentation via the promulgation of the Cybersecurity Law in 2016, up to the present day. It finds that the PIPL and its attendant regulations serve to primarily regulate the relationship between large technology companies and consumers, as well as prevent cyber crime. It does not create meaningful constraints on data collection and use by the state. Even so, the PIPL bears a clear family resemblance to personal data protection regimes elsewhere in the world. In contrast, the DSL is a considerable innovation, attempting to prevent harm to national security and the public interest inflicted through data-enabled means. While implementing structures for this Law remain under construction, it will likely herald a thorough reorganization of the way through which data is collected, stored and managed within all kinds of Chinese actors.

Taddeo & Blanchard on Ethical Principles for Artificial Intelligence in National Defense

Mariarosaria Taddeo (Oxford Internet Institute) and Alexander Blanchard (The Alan Turing Institute) have posted “Ethical Principles for Artificial Intelligence in National Defence” (Philosophy & Technology) on SSRN. Here is the abstract:

Defence agencies across the globe identify artificial intelligence (AI) as a key technology to maintain an edge over adversaries. As a result, efforts to develop or acquire AI capabilities for defence are growing on a global scale. Unfortunately, they remain unmatched by efforts to define ethical frameworks to guide the use of AI in the defence domain. This article provides one such framework. It identifies five principles — justified and overridable uses; just and transparent systems and processes; human moral responsibility; meaningful human control; reliable AI systems – and related recommendations to foster ethically sound uses of AI for national defence purposes.

Green on The Flaws of Policies Requiring Human Oversight of Government Algorithms

Ben Green (University of Michigan at Ann Arbor) has posted “The Flaws of Policies Requiring Human Oversight of Government Algorithms” on SSRN. Here is the abstract:

Policymakers around the world are increasingly considering how to prevent government uses of algorithms from producing injustices. One mechanism that has become a centerpiece of global efforts to regulate government algorithms is to require human oversight of algorithmic decisions. Despite the widespread turn to human oversight, these policies rest on an uninterrogated assumption: that people are able to oversee algorithmic decision-making. In this article, I survey 40 policies that prescribe human oversight of government algorithms and find that they suffer from two significant flaws. First, evidence suggests that people are unable to perform the desired oversight functions. Second, as a result of the first flaw, human oversight policies legitimize government uses of faulty and controversial algorithms without addressing the fundamental issues with these tools. Thus, rather than protect against the potential harms of algorithmic decision-making in government, human oversight policies provide a false sense of security in adopting algorithms and enable vendors and agencies to shirk accountability for algorithmic harms. In light of these flaws, I propose a more stringent approach for determining whether and how to incorporate algorithms into government decision-making. First, policymakers must critically consider whether it is appropriate to use an algorithm at all in a specific context. Second, before deploying an algorithm alongside human oversight, agencies or vendors must conduct preliminary evaluations of whether people can effectively oversee the algorithm.

Watson et al. on Local Explanations Via Necessity and Sufficiency: Unifying Theory and Practice

David Watson (University College London) et al. have posted “Local Explanations Via Necessity and Sufficiency: Unifying Theory and Practice” on SSRN. Here is the abstract:

Necessity and sufficiency are the building blocks of all successful explanations. Yet despite their importance, these notions have been conceptually underdeveloped and inconsistently applied in explainable artificial intelligence (XAI), a fast-growing research area that is so far lacking in firm theoretical foundations. Building on work in logic, probability, and causality, we establish the central role of necessity and sufficiency in XAI, unifying seemingly disparate methods in a single formal framework. We provide a sound and complete algorithm for computing explanatory factors with respect to a given context, and demonstrate its flexibility and competitive performance against state of the art alternatives on various tasks.

Alarie & Griffin on Using Machine Learning to Crack the Tax Code

Benjamin Alarie (University of Toronto – Faculty of Law) and Bettina Xue Griffin (Blue J Legal) have posted “Using Machine Learning to Crack the Tax Code” (Tax Notes Federal, January 31, 2022, p. 661) on SSRN. Here is the abstract:

In this article, we provide general observations about how tax practitioners are beginning to learn how to leverage the insights of machine learning to “crack the tax code.” We also examine how tax practitioners are using machine learning to quantify risks for their clients and ensure that tax advice can properly withstand scrutiny from the IRS and the courts. The goal is to guide tax experts in their tax planning and to help them devise the most effective ways to resolve tax disputes, leveraging new tools and technologies.

Yeung on Constitutional Principles in a Networked Digital Society

Karen Yeung (The University of Birmingham) on “Constitutional Principles in a Networked Digital Society” on SSRN. Here is the abstract:

This is the text of a keynote address delivered at the International Association of Constitutional Law (IACL) Roundtable, The Impact of Digitization on Constitutional Law, Copenhagen on 31 January 2022. In this short address, I ask: are our existing constitutional principles fit for purpose in an increasingly datafied, networked digital age? I suggest that our constitutional principles, including our rights discourse, has the potential to adapt to meet the altered conditions of our increasingly digitised and datafied age, but whether they will succeed in doing so remains an open question.