Algorithmic Discrimination and Legal Responsibility: Regulating AI in Employment and Law Enforcement - Juliana Ferreira; Nicole Yeo; Antoine Dubois (Journal of Transformative Legal and Social Studies, 2025)
The integration of artificial intelligence (AI) into employment screening and law enforcement decision-making has led to rising concerns about algorithmic discrimination and the opacity of automated processes. This paper critically examines how existing legal frameworks in the United States, the European Union, and Brazil address the challenges posed by AI-driven systems that disproportionately impact marginalized groups. While regulatory efforts such as the EU AI Act and Brazil’s General Data Protection Law signal progress, this study reveals significant gaps in legal accountability, transparency, and the enforceability of anti-discrimination norms in algorithmic contexts. The novelty of this research lies in its comparative analysis of global legal responses, coupled with an interdisciplinary framework that incorporates legal theory, critical race studies, and data justice perspectives. Unlike existing studies that treat AI bias as a technical glitch, this paper repositions algorithmic discrimination as a structural legal issue rooted in socio-political inequalities. It argues that prevailing regulatory models fail to account for the embeddedness of historical biases within datasets and institutional practices. The paper contributes to international legal scholarship by proposing a rights-based approach to AI governance that centers on due process, legal redress, and participatory oversight. It calls for a rethinking of liability standards and transparency obligations in algorithmic systems. In doing so, it advances the global debate on digital justice and provides actionable recommendations for developing inclusive, equity-centered regulatory frameworks across jurisdictions.