Advancing the Academic Discourse on Algorithmic Bias: Unpacking Conceptual Conflicts and Mitigation

Abstract

Algorithmic bias remains a challenge in artificial intelligence (AI), which has implications for technological development and societal equity. Despite substantial progress in identifying sources of bias and developing mitigation strategies, the literature exhibits fragmentation, with technical and social perspectives often treated in isolation. Our study addresses this gap by proposing an interdisciplinary theoretical framework integrating computational sciences, social theory, and ethics constructs to examine the interplay between bias sources and mitigation strategies. By introducing constructs such as “normative data influence” and “adaptive fairness metrics,” the framework highlights the co-evolution of technical solutions and social norms. The findings emphasize the importance of participatory design and inclusive governance to ensure the fairness and accountability of AI systems. This research offers a holistic perspective on algorithmic fairness, advances theoretical insights, and provides a practical roadmap for designing transparent and inclusive AI-based decision-making systems, thereby contributing to the academic discourse.

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