EU non-discrimination law in the era of artificial intelligence: Mapping the challenges of algorithmic discrimination
While most studies on the topic of AI, algorithms and bias have been conducted from the point of view of ‘fairness’ in the field of information technologies and computer science, this chapter explores the question of algorithmic discrimination – a category that does not neatly overlap with algorithmic bias – from the specific perspective of non-discrimination law. In particular and by contrast to the majority of current research on the question of algorithms and discrimination, which focuses on the United States context, this chapter takes EU non-discrimination law as its object of enquiry. We pose the question of the resilience of the general principle of non-discrimination, that is, the capacity for EU equality law to respond effectively to the specific challenges posed by algorithmic discrimination. Because EU law represents an overarching framework and sets minimum safeguards for the protection against discrimination at national level in EU Member States, it is important to test ou
While most studies on the topic of AI, algorithms and bias have been conducted from the point of view of ‘fairness’ in the field of information technologies and computer science, this chapter explores the question of algorithmic discrimination – a category that does not neatly overlap with algorithmic bias – from the specific perspective of non-discrimination law. In particular and by contrast to the majority of current research on the question of algorithms and discrimination, which focuses on the United States context, this chapter takes EU non-discrimination law as its object of enquiry. We pose the question of the resilience of the general principle of non-discrimination, that is, the capacity for EU equality law to respond effectively to the specific challenges posed by algorithmic discrimination. Because EU law represents an overarching framework and sets minimum safeguards for the protection against discrimination at national level in EU Member States, it is important to test out the protection against the risks posed by the pervasive and increasing use of AI techniques in everyday life applications which this framework allows for. This chapter therefore maps the challenges arising from artificial intelligence for equality and non-discrimination, which are both a general principle and a fundamental right in EU law. First, we identify the specific risks of discrimination that AI-based decision-making, and in particular machine-learning algorithms, pose. Second, we review how EU non-discrimination law can capture algorithmic discrimination in terms of its substantive scope. Third, we conduct this review from a conceptual perspective, mapping the friction points that emerge from the perspective of the EU concepts of direct and indirect discrimination, as developed by the Court of Justice of the European Union (CJEU). In the final step, we identify the core challenges algorithmic discrimination poses at the enforcement level and propose potential ways forward.
Executive Summary
The article 'EU non-discrimination law in the era of artificial intelligence: Mapping the challenges of algorithmic discrimination' explores the intersection of EU non-discrimination law and the emerging challenges posed by artificial intelligence (AI) and algorithmic decision-making. The authors critically assess the resilience of EU non-discrimination principles in addressing algorithmic discrimination, which they distinguish from algorithmic bias. The study identifies specific risks of discrimination inherent in AI-based decision-making, evaluates the substantive scope of EU non-discrimination law in capturing algorithmic discrimination, and examines the conceptual friction points from the perspective of the Court of Justice of the European Union (CJEU). The article concludes by identifying core challenges at the enforcement level and proposing potential solutions.
Key Points
- ▸ The article distinguishes between algorithmic discrimination and algorithmic bias, focusing on the former from the perspective of EU non-discrimination law.
- ▸ The study evaluates the resilience of EU non-discrimination principles in addressing the challenges posed by AI and algorithmic decision-making.
- ▸ The authors identify specific risks of discrimination in AI-based decision-making and review how EU non-discrimination law can capture these risks.
- ▸ The article examines conceptual friction points from the perspective of the CJEU's definitions of direct and indirect discrimination.
- ▸ The study proposes potential ways forward to address the enforcement challenges posed by algorithmic discrimination.
Merits
Comprehensive Analysis
The article provides a thorough and nuanced analysis of the intersection between EU non-discrimination law and AI, offering a comprehensive review of the challenges and potential solutions.
Interdisciplinary Approach
The study bridges the gap between legal and technological perspectives, providing a valuable interdisciplinary analysis that is often lacking in discussions on AI and discrimination.
Practical Implications
The article not only identifies theoretical challenges but also proposes practical solutions, making it relevant for both academic and policy discussions.
Demerits
Limited Scope
The focus on EU non-discrimination law may limit the applicability of the findings to other jurisdictions, although the authors acknowledge this limitation.
Conceptual Complexity
The distinction between algorithmic discrimination and bias, while important, may be conceptually challenging for some readers, potentially diluting the impact of the analysis.
Expert Commentary
The article 'EU non-discrimination law in the era of artificial intelligence: Mapping the challenges of algorithmic discrimination' offers a timely and critical examination of the challenges posed by AI to the principles of non-discrimination in the EU. The authors' interdisciplinary approach is commendable, as it effectively bridges the gap between legal and technological perspectives, which is often a significant hurdle in discussions on AI and discrimination. The study's focus on the resilience of EU non-discrimination principles is particularly relevant, given the increasing pervasiveness of AI in everyday applications. The authors' distinction between algorithmic discrimination and bias is conceptually important, although it may require further elaboration to fully grasp its implications. The practical solutions proposed by the authors are valuable, but the article could benefit from a more detailed exploration of the enforcement mechanisms required to implement these solutions effectively. Overall, the article makes a significant contribution to the ongoing debate on AI and discrimination, providing a robust framework for future research and policy development.
Recommendations
- ✓ Future research should explore the comparative analysis of algorithmic discrimination in different jurisdictions, including the United States and other regions, to provide a more comprehensive understanding of the global challenges and solutions.
- ✓ The article could benefit from a more detailed discussion on the practical implementation of the proposed solutions, including the role of regulatory bodies and the potential challenges they may face.