Fairness, Bias Mitigation, and Responsible AI in Production Questions

Understand bias sources in ML systems and fairness metrics (demographic parity, equalized odds, calibration across groups). Design bias testing and monitoring. Discuss mitigation strategies: diverse data, algorithmic debiasing, and post-processing. For Staff-level, embed responsible AI practices into organizational processes.