Algorithmic Bias as a Core Legal Dilemma in the Age of Artificial Intelligence: Conceptual Basis and the Current State of Regulation

This article examines algorithmic bias as a pressing legal challenge, situating the issue within the broader context of artificial intelligence (AI) governance. We employed comparative legal analysis and reviewed pertinent regulatory documents to examine how the fragmented U.S. approaches and the EU’s user-centric legal frameworks, such as the GDPR, DSA, and AI Act, address the systemic risks posed by biased algorithms. The findings underscore persistent enforcement gaps, particularly concerning opaque black-box algorithmic design, which hampers bias detection and remediation. The paper highlights how current regulatory efforts disproportionately affect marginalized communities and fail to provide effective protection across jurisdictions. It also identifies structural imbalances in legal instruments, particularly in relation to risk classification, transparency, and fairness standards. Notably, emerging regulations often lack the technical and ethical capacity for implementation. We argue that global cooperation is not only necessary but inevitable, as regional solutions alone are insufficient to govern transnational AI systems. Without harmonized international standards, algorithmic bias will continue to reproduce existing inequalities under the guise of objectivity. The article advocates for inclusive, cross-sectoral collaboration among governments, developers, and civil society to ensure the responsible development of AI and uphold fundamental rights.