Review of Bias and Fairness in Artificial Intelligence

Paper ID: EIJTEM_2025_12_1_19-24

Author's Name: Kiruthika B, Hardik Jadhav, Sumeet Aanakod

Volume: 12

Issue: 1

Year: 2025

Page No: 19-24

Abstract:

This study explores fairness and bias in AI with a technical solutions approach that draws on conceptual analysis and case studies, specifically covering algorithmic transparency, bias audits, and data management. Ethical considerations incorporate accountability and alignment of value together with policy recommendation, including framework of regulation along with interdisciplinary collaboration; case studies highlighting systemic biases involving racial disparities from facial recognition technologies to gender discriminative hiring decisions and unfair lending practices. Conclusively, the study indicates the societal ramifications of AI and suggests ways it can be improved in the future. Future research should include transparency, adaptive bias correction, ethical AI certifications, and regional data ownership for the development of fair and accountable AI systems.

Keywords: Ethical AI, Value alignment, Facial recognition, Bias mitigation strategies

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