Author's: Prof. Vikram Jadhav
Abstract: As machine learning algorithms become increasingly integral to autonomous systems, ensuring fairness in their operations has emerged as a critical ethical concern. This paper delves into the concept of fairness in machine learning, examining the various dimensions of bias that can arise and their impact on decision-making processes. Through an analysis of different fairness metrics and techniques for bias mitigation, the paper provides a comprehensive overview of current approaches to achieving fairness in AI. Case studies from sectors such as criminal justice, hiring, and loan approval illustrate the real-world implications of biased algorithms and the importance of developing fair and transparent AI systems. Recommendations for future research and policy development are also discussed.
Keywords: Fairness in AI, Machine learning bias, Bias mitigation, Ethical AI Transparency
Full Issue
| View or download the full issue | PDF 8-15 |