Matching Code and Law: Achieving Algorithmic Fairness with Optimal Transport

Increasingly, discrimination by algorithms is perceived as a societal and legal problem. As a response, a number of criteria for implementing algorithmic fairness in machine learning have been developed in the literature. This paper proposes the Continuous Fairness Algorithm (CFA$\theta$) which enables a continuous interpolation between different fairness definitions. More specifically, we make three main contributions to the existing literature. First, our approach allows the decision maker to continuously vary between specific concepts of individual and group fairness. As a consequence, the algorithm enables the decision maker to adopt intermediate ``worldviews'' on the degree of discrimination encoded in algorithmic processes, adding nuance to the extreme cases of ``we're all equal'' (WAE) and ``what you see is what you get'' (WYSIWYG) proposed so far in the literature. Second, we use optimal transport theory, and specifically the concept of the barycenter, to maximize decision maker utility under the chosen fairness constraints. Third, the algorithm is able to handle cases of intersectionality, i.e., of multi-dimensional discrimination of certain groups on grounds of several criteria. We discuss three main examples (credit applications; college admissions; insurance contracts) and map out the legal and policy implications of our approach. The explicit formalization of the trade-off between individual and group fairness allows this post-processing approach to be tailored to different situational contexts in which one or the other fairness criterion may take precedence. Finally, we evaluate our model experimentally.Comment: Vastly extended new version, now including computational experiment

AI Regulation in Europe: From the AI Act to Future Regulatory Challenges

This chapter provides a comprehensive discussion on AI regulation in the European Union, contrasting it with the more sectoral and self-regulatory approach in the UK. It argues for a hybrid regulatory strategy that combines elements from both philosophies, emphasizing the need for agility and safe harbors to ease compliance. The paper examines the AI Act as a pioneering legislative effort to address the multifaceted challenges posed by AI, asserting that, while the Act is a step in the right direction, it has shortcomings that could hinder the advancement of AI technologies. The paper also anticipates upcoming regulatory challenges, such as the management of toxic content, environmental concerns, and hybrid threats. It advocates for immediate action to create protocols for regulated access to high-performance, potentially open-source AI systems. Although the AI Act is a significant legislative milestone, it needs additional refinement and global collaboration for the effective governance of rapidly evolving AI technologies.Comment: Final version forthcoming in: Ifeoma Ajunwa & Jeremias Adams-Prassl (eds), Oxford Handbook of Algorithmic Governance and the Law, Oxford University Press, 202

The European AI Liability Directives -- Critique of a Half-Hearted Approach and Lessons for the Future

As ChatGPT et al. conquer the world, the optimal liability framework for AI systems remains an unsolved problem across the globe. In a much-anticipated move, the European Commission advanced two proposals outlining the European approach to AI liability in September 2022: a novel AI Liability Directive and a revision of the Product Liability Directive. They constitute the final cornerstone of EU AI regulation. Crucially, the liability proposals and the EU AI Act are inherently intertwined: the latter does not contain any individual rights of affected persons, and the former lack specific, substantive rules on AI development and deployment. Taken together, these acts may well trigger a Brussels Effect in AI regulation, with significant consequences for the US and beyond. This paper makes three novel contributions. First, it examines in detail the Commission proposals and shows that, while making steps in the right direction, they ultimately represent a half-hearted approach: if enacted as foreseen, AI liability in the EU will primarily rest on disclosure of evidence mechanisms and a set of narrowly defined presumptions concerning fault, defectiveness and causality. Hence, second, the article suggests amendments, which are collected in an Annex at the end of the paper. Third, based on an analysis of the key risks AI poses, the final part of the paper maps out a road for the future of AI liability and regulation, in the EU and beyond. This includes: a comprehensive framework for AI liability; provisions to support innovation; an extension to non-discrimination/algorithmic fairness, as well as explainable AI; and sustainability. I propose to jump-start sustainable AI regulation via sustainability impact assessments in the AI Act and sustainable design defects in the liability regime. In this way, the law may help spur not only fair AI and XAI, but potentially also sustainable AI (SAI).Comment: under peer-review; contains 3 Table

Sustainable AI Regulation

This paper suggests that AI regulation needs a shift from trustworthiness to sustainability. With the carbon footprint of large generative AI models like ChatGPT or GPT-4 adding urgency to this goal, the paper develops a roadmap to make AI, and technology more broadly, environmentally sustainable. It explores two key dimensions: legal instruments to make AI greener; and methods to render AI regulation more sustainable. Concerning the former, transparency mechanisms, such as the disclosure of the GHG footprint under Article 11 AI Act, could be a first step. However, given the well-known limitations of disclosure, regulation needs to go beyond transparency. Hence, I propose a mix of co-regulation strategies; sustainability by design; restrictions on training data; and consumption caps. This regulatory toolkit may then, in a second step, serve as a blueprint for other information technologies and infrastructures facing significant sustainability challenges due to their high GHG emissions, e.g.: blockchain; metaverse applications; and data centers. The second dimension consists in efforts to render AI regulation, and by implication the law itself, more sustainable. Certain rights we have come to take for granted, such as the right to erasure (Article 17 GDPR), may have to be limited due to sustainability considerations. For example, the subjective right to erasure, in some situations, has to be balanced against the collective interest in mitigating climate change. The paper formulates guidelines to strike this balance equitably, discusses specific use cases, and identifies doctrinal legal methods for incorporating such a "sustainability limitation" into existing (e.g., Art. 17(3) GDPR) and future law (e.g., AI Act). Ultimately, law, computer science and sustainability studies need to team up to effectively address the dual large-scale transformations of digitization and sustainability.Comment: Privacy Law Scholars Conference 202

UberPop, UberBlack, and the regulation of digital platforms after the Asociación Profesional Elite Taxi judgment of the CJEU: judgment of the Court (Grand Chamber) 20 December 2017, Asociación Profesional Elite Taxi (C-434/15)

The case concerning a smartphone transport application provided by Uber, decided by the Grand Chamber of the CJEU on 20 December 2017, has the potential to reshape the regulation of contracting in the digital economy. More precisely, it specifies the rules applicable to online platforms serving as intermediaries between parties demanding and offering services. The criteria the CJEU uses to reach its conclusion are likely to have repercussions far beyond the area of transportation applications. This Case Note starts by presenting the facts of the case and the legal background of those EU law provisions potentially governing digital intermediaries. It then explores the criteria the Court uses to distinguish Uber from simple intermediation services, followed by a discussion and critique of these criteria. In the last two sections, it maps out the implications of the judgment for the platform economy, and suggests that a decisive impetus of the judgment should be a thorough review of regulations governing the provision of services in the EU

Standing on Shaky Ground: Americans' Experiences With Economic Insecurity

Based on 2009 Surveys of Economic Risk Perceptions and Insecurity, examines Americans' experience of economic insecurity, such as frequency and duration, buffers against hardship, and concerns by income, family structure, race/ethnicity, and education

Digital Technology as a Challenge to European Contract Law

Offering an overview of the interactions between digital technologies and contract law, we identify three pillars in this architecture: the regulatory framework; digital interventions over the life cycle of the contract; and digital objects of contracting. The regulatory framework, which itself may draw on digital technology to effectively pursue its ends, shapes, and is shaped by, the other two pillars. More specifically, on the one hand, we show how four key technologies – digital platforms, Big Data analytics, artificial intelligence, and blockchain – are being used at different stages of the contractual process (from the screening for contractual partners to formation, enforcement and interpretation) and engender novel market dynamics that, in many instances, necessitate regulatory responses. On the other hand, digitally facilitated contracting increasingly relates to digital content as the object of the contract; however, while this area has notably been the subject of the proposed Directive on Contracts for the Supply of Digital Content and thus has received some first ‘European structure’, we argue that a number of important blind spots remain that fail to be addressed by the directive. All in all, the use of digital technology in contracting will likely reinforce an adaptive, relational view and practice of contracting. This increased fluidity engenders a vast potential for preference-conforming, time-sensitive contracts; however, to the extent that it also mirrors novel asymmetries of information and power, the ordering mechanisms of the law may simultaneously be more needed than ever.Peer Reviewe

European Union Litigation

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.This article provides an overview of cases decided by the Court of Justice of the European Union concerning contract law. The present issue covers the period between the beginning of July 2019 and the end of December 2019.Peer Reviewe

Learning and the Law: Improving Behavioral Regulation from an International and Comparative Perspective

Various disciplines are increasingly discovering the power of learning. However, the potential and the complexities of learning theory in decision-making contexts have so far been neglected by scholarship in law and economics as well as behavioral law and economics: either learning is uncritically assumed to occur and to mitigate biases, or it is generally claimed that learning is insufficient to overcome cognitive biases. Even where learning is considered, the scope is merely limited to individual or social learning. Learning by and across institutions, a crucial factor for effective regulation, is largely ignored. That type of learning should be paramount, however, as an increasing number of institutions at the international and domestic level are adopting behavioral regulation, which prides itself on facilitating “smart decisions.” This Article argues that legal analysis should tap the precious resource of learning to facilitate lasting and beneficial real-world effects. It draws on social and cognitive psychology, behavioral game theory, and organizational science to show that there are vast effectiveness and efficiency gains to be made from an integration of learning theory into regulatory and private law contexts. Interdisciplinary learning theory suggests that such gains can be made through learning by doing, observational learning, as well as recursive and generative learning. These learning methods can be used at the individual, social, team, and institutional level, which is demonstrated using case studies from international law, as well as American and European Union law. As an overarching category subsuming these forms of learning, the Article develops the concept of systemic learning. It suggests that the law should introduce systemic learning patterns in public and private law contexts through feedback loops and institutionalized systemic learning facilities. Finally, it proposes the institutionalization of an Agency for Systemic Learning Management. Having ignored learning theory in the past, future behavioral regulation should put learning efforts center stage as it unfolds on a global scale