Fairness & Bias Tooling Summary Four-stage fairness tooling: pre-training (data distribution analysis, representation assessment, proxy variable detection), post-training (Fairlearn MetricFrame/Aequitas for disaggregated metrics), production (continuous fairness metric computation, weekly or monthly), and missing demographic data (proxy estimation, deployer surveys, external benchmarks). See (pre-deployment) and (production) for detailed treatment. Key outputs

• Four-stage fairness assessment
• Fairlearn/Aequitas tooling
• Missing demographic data strategies