Algorithmic Fairness Auditing Using Explainable AI Techniques

Abstract

Algorithmic bias presents a significant challenge to the responsible deployment of machine learning models across various sectors. This paper proposes a novel framework for algorithmic fairness auditing that utilizes Explainable AI (XAI) techniques to enhance transparency and accountability. The core idea is to systematically examine machine learning models for potential biases by leveraging XAI methods to understand the decision-making processes within these models. This approach moves beyond simply measuring disparate impact to identifying the underlying reasons for biased predictions, offering actionable insights for mitigation. The framework incorporates techniques like SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-agnostic Explanations) to provide feature importance scores and local explanations, respectively. We detail the methodology, illustrating how XAI can be integrated into the auditing workflow, and discuss the potential impact of this approach on improving fairness in algorithmic systems. The framework aims to transform the often-opaque process of bias detection into a more transparent and understandable one, ultimately contributing to the development of fairer and more equitable AI systems.