Concerns about “black box” machine learning algorithms have led many data protection laws and regulations to establish a right to human intervention on decision-making supported by artificial intelligence. Such interventions provide data subjects with means to protect their rights, freedoms, and legitimate interests, either as a bare minimum requirement for data processing or as a central norm governing decision-aiding artificial intelligence. In this paper, I present contestability by design as an approach to two kinds of issues with current legal implementations of the right to human intervention. The first kind is the uncertainty about what kind of decision should be covered by this right: while a narrow reading of rules such as GDPR Article 22(3) would include all sorts of fully-automated decisions, I show how a broader interpretation can provide more effective protection for data subjects against side-effects of automated decision-making. The second class of issues ensues from practical effects, or the lack thereof, of this right to intervention: even within a clear conceptual framework, data subjects might still lack the information they need to the concrete exercise of their right, or the human intervention itself might introduce biases and limitations that result in undesirable outcomes. After discussing how those effects can be identified and measured, I then explore how the proper protection of the rights of data subjects is possible only if the possibility of contesting decisions based solely on automated processing is not an afterthought, but instead a requirement at each stage of an artificial intelligence system's lifecycle.

This timely book explores the legal and practical challenges created by the increasingly automated decision-making procedures underpinning EU multilevel cooperation, for example, in the fields of border control and law enforcement. It argues that such procedures impact not only the rights to privacy and data protection, but fundamentally challenge the EU constitutional promise of effective judicial protection

Exploring issues from big-data to robotics, this volume is the first to comprehensively examine the regulatory implications of AI technology.

A received wisdom is that automated decision-making serves as an anti-bias intervention. The conceit is that removing humans from the decision-making process will also eliminate human bias. The paradox, however, is that in some instances, automated decision-making has served to replicate and amplify bias. With a case study of the algorithmic capture of hiring as heuristic device, this Article provides a taxonomy of problematic features associated with algorithmic decision-making as anti-bias intervention and argues that those features are at odds with the fundamental principle of equal opportunity in employment. To examine these problematic features within the context of algorithmic hiring and to explore potential legal approaches to rectifying them, the Article brings together two streams of legal scholarship: law and technology studies and employment & labor law.Counterintuitively, the Article contends that the framing of algorithmic bias as a technical problem is misguided. Rather, the Article's central claim is that bias is introduced in the hiring process, in large part, due to an American legal tradition of deference to employers, especially allowing for such nebulous hiring criterion as “cultural fit.” The Article observes the lack of legal frameworks that take into account the emerging technological capabilities of hiring tools which make it difficult to detect disparate impact. The Article thus argues for a re-thinking of legal frameworks that take into account both the liability of employers and those of the makers of algorithmic hiring systems who, as brokers, owe a fiduciary duty of care. Particularly related to Title VII, the Article proposes that in legal reasoning corollary to extant tort doctrines, an employer's failure to audit and correct its automated hiring platforms for disparate impact could serve as prima facie evidence of discriminatory intent, leading to the development of the doctrine of discrimination per se. The article also considers other approaches separate from employment law such as establishing consumer legal protections for job applicants that would mandate their access to the dossier of information consulted by automated hiring systems in making the employment decision.

Human decision-making often transcends our formal models of "rationality." Designing intelligent agents that interact proficiently with people necessitates the modeling of human behavior and the prediction of their decisions. In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures—from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making.

Governance of Automated Decision-Making and EU Law presents a comprehensive and nuanced exploration of the intricate relationship between technological innovation and democratic governance in Europe. Focused on preserving constitutional values within the European Union, the book rigorously examines the profound impact of information technologies on rule-making and decision-making processes. The dual objectives of the volume are to comprehensively explore the impact of innovative information technologies on the EU's public law and to devise future-proof regulatory strategies in the face of rapid technological advancements. Addressing the spread of information technology and automated decision-making processes across EU policy sectors, the work delves into potential risks to democratic principles and accountability standards. Advocating for a comprehensive approach, the volume integrates legal, policy, and technological considerations to establish accountability standards for automated decision-making systems. Tailored for academics, researchers, and policymakers, Governance of Automated Decision-Making and EU Law provides a vital resource for understanding the complexities and opportunities associated with the digitalization of shared administration in the EU. It contributes significantly to the ongoing discourse on safeguarding constitutional values and principles of good governance in the digital era. The findings underscore the interconnectedness of information systems across EU-regulated policy areas and the risks posed by automated decision-making systems. Urging attention to transparency and accountability, the book addresses these concerns through eleven chapters, offering insights into normative requirements, administrative procedures, market regulation, digital health, borders and immigration, political advertising, interoperability framework, AI technology, and their intersection with legal principles. This is an open-access title available under the terms of a CC BY-NC-ND 4.0 International license.

The book presents observations concerning automated decision-making from a general point of view at the same time as it analyses the manner in which praxis in some jurisdictions has evolved as concerns automated decision-making and how the requirements that are placed by the legal orders on it are formulated. The principle of the rule of law should apply in the context of automated decision-making of public authorities just as much as when the decision-makers are physical persons. In sync with increasing automatization of decision-making in public authorities, problematizing questions about the appropriate legal basis for algorithmic decision-making have started emerge. How should the principle of the rule of law apply within the area of automated decision-making, how should automated decision-making be regulated so that it satisfies the requirements created by the principle of the rule of law, and how should the principle of the rule of law be made concrete in decision-making that is based on algorithms? The proposal for an AI Act launched by the European Commission in April 2021, including an identification of high-risk uses of algorithmic techniques, raises further questions concerning practices and interpretations related to automated decision-making. The state based on the rule of law proceeds from the maxim that public powers are exercised within a legal frame that makes the exercise of public powers foreseeable in light of legal norms. Also, a state based on the rule of law requires that the contents of the exercise of public powers is regulated by legal norms, which means that the citizens must be able to know everything that is relevant about how the powers will be exercised, not only who it is that will exercise the powers. Because of rules and principles of this kind, including non-discrimination and proportionality, the exercise of powers will not become arbitrary.