Regulating Algorithmic Bias: Normative Challenges of AI Ethics in Automated Decision-Making
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Algorithmic Bias, AI Ethics, Legal Regulation
Abstract
Background. The integration of artificial intelligence (AI) into automated decision-making systems has introduced significant ethical and legal concerns, particularly regarding algorithmic bias.
Purpose. These biases can perpetuate systemic discrimination, distort outcomes in critical sectors such as healthcare, finance, and criminal justice, and challenge the foundational principles of fairness and transparency. Despite widespread recognition of the issue, there remains a normative gap in regulatory responses across jurisdictions.
Method. This study aims to explore the ethical challenges of algorithmic bias and assess the adequacy of existing legal frameworks in addressing these concerns.
Results. Using a normative legal research design, the study employs comparative analysis across selected regulatory regimes in the EU, US, and Asia, supported by doctrinal analysis of AI-related policies and ethical codes. Findings reveal fragmented regulatory landscapes, a lack of binding accountability mechanisms, and insufficient integration of ethical principles into enforceable legal norms.
Conclusion. The study concludes that an interdisciplinary approach—merging ethical theory with legal doctrine—is essential to regulate algorithmic bias effectively. A normative framework grounded in transparency, accountability, and inclusivity is proposed to guide future legislation and policy development.
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References
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Copyright (c) 2025 Magnus Andersson, Maria Lindström, Johan Nilsson
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