Fairness & Bias

Mitigation strategy

1. Prioritize Data Governance and Representativeness Conduct rigorous auditing of training, validation, and testing datasets to assess for representativeness, identify under-representation, and eliminate historical or societal biases embedded in the data. Employ techniques such as reweighting, resampling, or synthetic data generation to ensure balanced and diverse input distribution across all sensitive attributes. 2. Implement Multi-Stage Algorithmic Mitigation Apply technical fairness solutions across the AI lifecycle, including pre-processing (data transformation), in-processing (utilizing fairness-aware algorithms, regularization, or adversarial debiasing), and post-processing (adjusting model outputs via re-ranking or threshold optimization) to actively reduce identified disparities in model performance and outcomes. 3. Establish Continuous Monitoring and Human Oversight Mandate post-deployment monitoring systems to continuously assess the AI system's performance and fairness metrics in real-world conditions for signs of emergent bias drift. Integrate human-in-the-loop mechanisms and clear transparency documentation to facilitate accountability, user feedback, and timely corrective action.