Governing Cultural Bias in Artificial Intelligence: Legal Accountability for Algorithmic Decision-Making in a Globalized Digital Society
The rapid integration of artificial intelligence into public administration, employment, financial services, education, migration control, and criminal justice has transformed algorithmic decision-making into an important problem of law and global governance. Although contemporary regulatory frameworks increasingly emphasize transparency, accountability, fairness, and human oversight, considerably less attention has been devoted to the cultural assumptions embedded in artificial intelligence systems. Algorithms developed using datasets, classifications, linguistic structures, and behavioral norms originating in particular societies may reproduce culturally specific understandings of identity, risk, merit, family, gender, social behavior, and acceptable conduct when deployed across different jurisdictions. This article examines the legal implications of cultural bias in artificial intelligence from the perspective of globalization and comparative regulatory governance. It asks whether existing principles of equality, non-discrimination, data protection, due process, and algorithmic accountability adequately address harms arising not merely from statistical discrimination but from the transnational transfer of culturally embedded computational models. Using a comparative doctrinal methodology, the study analyzes emerging approaches in the European Union, the United States, and selected Asian and Middle Eastern jurisdictions, while situating these regulatory developments within broader debates concerning cultural pluralism and digital globalization. The article argues that conventional technical definitions of algorithmic fairness are insufficient where artificial intelligence operates across culturally heterogeneous populations. It proposes a model of culturally responsive algorithmic governance based on contextual impact assessment, participatory design, explainability adapted to affected communities, localization safeguards, and enhanced obligations for high-risk cross-border AI systems. The study contributes to contemporary debates by connecting artificial intelligence regulation with cultural rights and legal pluralism rather than treating algorithmic bias exclusively as a technological or anti-discrimination problem. It concludes that effective global AI governance requires a shift from universalized technical fairness toward context-sensitive legal accountability capable of recognizing legitimate cultural diversity while preserving universal human rights standards.