Assess students' ability to apply machine learning techniques and ethical frameworks to identify, quantify, and mitigate algorithmic bias in automated hiring systems; tasks include data auditing, selecting and justifying fairness metrics, applying pre/in/post-processing mitigation methods, using model interpretability and causal analysis to diagnose sources of bias, and designing evaluation plans that weigh legal, ethical, and utility trade-offs in realistic case scenarios.