Test students' ability to analyze trade-offs among model accuracy, fairness (group and individual), and privacy when deploying ML-based credit scoring systems; scope includes technical metrics and mitigations (fairness definitions, regularization, differential privacy, secure computation), legal/regulatory constraints (e.g., anti-discrimination laws, FCRA, GDPR), economic impacts (credit access, lender risk, adverse selection, compliance costs), evaluation frameworks, and formulating justified deployment and policy recommendations through short case-based analyses.