Machine Learning (ML) systems are increasingly shaping decisions in healthcare, finance, hiring, and other critical domains. However, biases in data and algorithms can lead to unfair outcomes, exacerbating societal inequalities. This paper explores the sources of bias in AI models, methods for bias mitigation, and frameworks for ethical AI development. We discuss techniques such as fairness-aware learning, adversarial debiasing, and explainability approaches to ensure accountability. Finally, we outline future directions for research in making AI systems more equitable and trustworthy.
Ethical AI, Bias Mitigation, Fairness in ML, Algorithmic Bias, AI Ethics