Ethical Challenges in AI Decision-Making Systems

Decision-making powered by artificial intelligence (AI) has become pervasive in high-stakes domains such as healthcare, criminal justice, finance, and human resource management. While AI systems promise greater efficiency, consistency, and objectivity, they introduce significant ethical risks including algorithmic bias, opacity, accountability gaps, and privacy erosion. This paper provides a comprehensive analysis of these challenges, examining the origins and manifestations of bias, the technical and ethical imperatives for explainability, responsibility diffusion in complex AI supply chains, and tensions between data-driven innovation and fundamental rights. It evaluates major regulatory responses, notably the European Union AI Act, and proposes a multi-stakeholder ethical governance framework. The study argues that responsible AI deployment requires continuous co-evolution of technical solutions, organizational practices, and adaptive regulation to ensure fairness, transparency, and human-centric outcomes.

Algorithmic bias, Explainable AI, AI ethics, Fairness, Transparency, Accountability, Data governance, EU AI Act

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