Paper Contents
Abstract
Machine Learning (ML) models are increasingly being deployed in critical applications such as healthcare, finance, recruitment, and criminal justice, where they directly influence human lives. However, these models often inherit biases from training datasets or algorithmic structures, leading to unfair and discriminatory outcomes. Such issues compromise the trustworthiness and ethical use of AI systems. This paper provides an in-depth analysis of bias in ML models, identifying its sources, examining fairness metrics, and evaluating mitigation techniques. We investigate the consequences of biased models in real-world case studies, including loan approvals, facial recognition, and healthcare diagnostics. Furthermore, the paper explores fairness-aware machine learning approaches at pre-processing, in-processing, and post-processing levels, demonstrating their impact on reducing bias while maintaining acceptable levels of accuracy. Our findings highlight the necessity of balancing fairness with performance, showing that responsible AI development must prioritize equity alongside efficiency.Keywords: Bias in AI, Algorithmic Fairness, Ethical AI, Data Preprocessing, Discrimination, Responsible Machine Learning, Fairness Metrics, Mitigation Strategies, Transparency, Trustworthy AI
Copyright
Copyright © 2025 RUTHIKA PARIMELAZHAGAN. This is an open access article distributed under the Creative Commons Attribution License.
