model-evaluation

An audit is only useful if it produces a decision. This lesson covers the audit sequence end to end, the three families of mitigation and what each costs, why some findings cannot be fixed at the model layer, documenting a trade-off you can defend, and monitoring for the drift that reopens a closed finding.

Fairness has several formal definitions that sound equally reasonable and cannot all hold at once. This lesson covers demographic parity, equal opportunity, equalized odds and calibration, the impossibility result proved independently by Chouldechova and by Kleinberg and colleagues, and how to choose one deliberately and defend it.

The word bias carries three unrelated meanings that get argued past each other. This lesson separates them, then traces the six points where disparity enters a machine learning system, from historical data through label definition and objective choice to deployment, and explains why removing a protected attribute does not remove its influence.

A model that predicts well can still be impossible to justify. This lesson maps explainable AI: interpretable-by-design versus post-hoc, global versus local, model-specific versus model-agnostic. It covers the global workhorses (permutation importance, partial dependence, ICE), faithfulness versus plausibility, and the argument that post-hoc explanation is the wrong tool for high-stakes decisions.