Test graduate students' ability to analyze socio-technical drivers (data quality and representativeness, model design choices, organizational processes, labor market context, legal and cultural factors) that produce bias in algorithmic hiring systems and to evaluate mitigation strategies (data curation, fairness-aware algorithms, human-in-the-loop design, audits, governance and compliance). Assess skills in diagnosing root causes, comparing technical and non-technical interventions, designing mixed-method mitigation plans for case scenarios, and critiquing trade-offs, metrics, and policy implications.