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Fairness AI aims to detect and alleviate bias across the entire AIdevelopment life cycle, encompassing data curation, modeling, evaluation, anddeployment-a pivotal aspect of ethical AI implementation. Addressing data bias,particularly concerning sensitive attributes like gender and race, reweightingsamples proves efficient for fairness AI. This paper contributes a systematicexamination of reweighting samples for traditional machine learning (ML)models, employing five models for binary classification on the Adult Income andCOMPUS datasets with various protected attributes. The study evaluatesprediction results using five fairness metrics, uncovering the nuanced andmodel-specific nature of reweighting sample effectiveness in achieving fairnessin traditional ML models, as well as revealing the complexity of bias dynamics.

Comprehensive Validation on Reweighting Samples for Bias Mitigation via AIF360