Assess learners' ability to analyze how algorithmic bias arises in AI-driven criminal risk assessment tools, evaluate relevant regulatory frameworks (data protection, anti-discrimination, procurement and oversight), examine societal and distributive impacts on affected communities and procedural fairness, interpret fairness metrics and audit methods, and propose technical and policy mitigation strategies using case studies (e.g., COMPAS).