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