Framework for Fairness in Machine Learning Using Detecting and Mitigating Bias in AI Algorithms

Abstract

The importance of artificial intelligence (AI) and machine learning (ML) is on the rise as they are increasingly being used in critical decision-making in areas including healthcare, finance, and criminal justice. Their effectiveness, however, is often compromised as these systems replicate and even worsen pre-existing biases from historical data or from the designs of models, ultimately causing unfair and undesirable results. This paper describes a systematic analysis for bias detection and mitigation in machine learning algorithms. The approach includes preprocessing of data, algorithmic changes, and changes during post-processing for improvement of anticipated inequalities while keeping the performance of the model intact. The model's applicability has been illustrated in the areas of healthcare, finance, and criminal justice with case studies demonstrating the need for a trade-off between the model's accuracy and fairness. Besides, AI ethics are also advanced through transparency methods like SHAP and GradCAM, which strengthen trust in AI systems as ethical principles are ensured in the deployment of the AI systems. Though these achievements are significant, challenges on the issue of scaling, fairness definitions, and contextual variations still persist with regard to the application of AI for fairness promoting purposes, suggesting interdisciplinary directions as ways to mitigate the challenges.

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