Bias Detection and Mitigation in AI Models through Explainability Techniques

Abstract

Artificial Intelligence (AI) systems increasingly influence decisions in sensitive areas such as hiring, credit allocation, healthcare diagnostics, law enforcement, and education. While AI promises efficiency and objectivity, numerous real-world deployments have revealed systemic biases embedded within data, algorithms, and decision-making pipelines. Such biases can perpetuate social inequalities and undermine ethical principles of fairness and justice. Detecting and mitigating bias in complex machine learning models remains challenging, particularly when models function as black boxes. Explainable Artificial Intelligence (XAI) has emerged as a powerful approach to uncover hidden biases by making model behavior transparent and interpretable. This paper examines how explainability techniques can be used to detect, analyze, and mitigate bias in AI models. It explores sources of algorithmic bias, reviews prominent explainability methods, and demonstrates how these techniques support ethical and fair AI deployment. The study argues that explainability is not merely diagnostic but instrumental in designing bias-aware and socially responsible AI systems.

Keywords: Algorithmic Bias, Explainable AI, Fairness, Ethical AI, Transparency