Bias and Discrimination

Mitigation strategy

1. Systematically audit and enhance training data quality by ensuring demographic diversity and representativeness, employing techniques such as reweighting or stratified sampling for underrepresented subgroups to prevent the propagation of historical or sampling bias into the model. 2. Integrate fairness-aware machine learning constraints during model training, utilizing in-processing methods such as adversarial debiasing, fair representation learning, or optimization against fairness metrics (e.g., Equalized Odds) to minimize differential predictive performance across sensitive attributes. 3. Institute a continuous monitoring and auditing framework for the deployed AI system, including real-time performance testing for statistical parity across sensitive groups and incorporating a human-in-the-loop oversight mechanism to review and override potentially biased, high-stakes decisions.