Assess students' ability to analyze how algorithmic hiring model design and bias‑mitigation strategies influence selection outcomes and workforce diversity in tech recruitment. Scope includes feature and label choices, algorithmic trade‑offs, fairness metrics, pre/in/post‑processing techniques, experimental and evaluation design, diagnostic methods (e.g., counterfactuals, subgroup analysis), and legal/ethical considerations; students must interpret metrics, identify bias sources, and propose evidence‑based mitigation with anticipated impacts on representation.