Fairness and bias in AI/ML systems

Abstract

As artificial intelligence (AI) and machine learning (ML) systems increasingly influence high- stakes decisions in areas such as healthcare, finance, and hiring, concerns regarding bias and fairness have emerged as critical issues. Despite advancements in AI technologies, ensuring equitable outcomes across diverse groups remains a challenge due to the potential for biases in data, algorithms, and model predictions. To address this, we propose a novel tool that evaluates the fairness of AI and ML models by analyzing their behavior on input datasets. Our tool provides a comprehensive fairness assessment by taking a trained model and dataset as input and producing a fairness score as output. The fairness score is calculated using established metrics such as demographic parity, equalized odds, and equal opportunity, offering insights into potential biases in the model’s predictions. The tool also supports subgroup analysis to identify disparities across protected and unprotected groups. By automating bias detection and providing actionable insights, our tool empowers practitioners to develop and deploy fairer models. This report details the design and implementation of the tool, the methodology behind fairness scoring, and case studies demonstrating its effectiveness. By highlighting the importance of fairness in AI, we aim to contribute to the development of more inclusive and ethical machine learning systems.