Comprehensive Bias Mitigation in AI: Evaluating Pre-Processing, In-Processing, and Post-Processing Techniques for Fair Decision-Making

The issue of machine learning bias is a pressing ethical and societal concern surrounding the deployment of such models, especially in high-stakes scenarios like criminal justice risk assessment. We propose a holistic framework for bias mitigation through systematic evaluation of pre-processing (Reweighing), in-processing (Adversarial Debiasing), and postprocessing (Equalized Odds) techniques to provide fairness with predictive accuracy. Analyze each of these methods according to fairness metrics DISPARATE_IMPACT, S.P.D., E.O.D., and A.O.D, using COMPAS dataset. Baseline model muse over one time opinionated, Disparate Impact of 0.788 and Statistical Parity Difference of −0.1306 favoring privileged. Indeed, Reweighing only had marginal improvements in fairness while Adversarial Debiasing had strongly reduced bias. In the end, Equalized Odds post-processing provided the best tradeoff, improving fairness metrics (Disparate Impact <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$=0.950$</tex>, Statistical Parity Difference <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$=-0.0323$</tex>), while remaining in the ballpark of a realistic accuracy of 65.14 %. In the end, these results confirm our hypothesis of complementary effects of multiple bias mitigation strategies, and the need for care in selecting the appropriate methods to use together given the specific fairness-accuracy tradeoff desired. This study offers a framework for implementing fair and unbiased AI in decision-making models, assisting in the realization of ethical AI technologies.