Algorithmic Bias and the Law: Ensuring Fairness in Automated Decision-Making

Abstract

Algorithmic decision-making systems have become pervasive across critical domains including employment, housing, healthcare, and criminal justice. While these systems promise enhanced efficiency and objectivity, they increasingly demonstrate patterns of discrimination that perpetuate and amplify existing societal biases. This paper examines the evolving legal landscape governing algorithmic bias, analyzing recent regulatory developments, landmark litigation, and emerging compliance frameworks. Through comparative analysis of the fragmented U.S. approach and the European Union's comprehensive regulatory strategy, this study identifies persistent enforcement gaps and structural limitations in current legal frameworks. The research reveals that existing civil rights protections, while foundational, prove insufficient for addressing the novel challenges posed by automated decision-making systems. Key findings indicate that recent legal developments, including the Colorado AI Act and landmark cases such as Mobley v. Workday, represent significant progress toward establishing algorithmic accountability. However, substantial gaps remain in transparency requirements, technical standards for bias detection, and effective remediation mechanisms. This paper proposes an integrated legal framework combining rights-based protections, technical standards, and institutional oversight to ensure algorithmic fairness while fostering innovation.