Assess students' ability to analyze sources of bias (data, label, model, proxy, and human decision-making) and their impacts in AI-driven executive talent acquisition; evaluate and compare mitigation strategies (data curation, fairness-aware algorithms, explainability, human-in-the-loop, adversarial testing, governance and auditing), design test protocols and metrics, and justify trade-offs in regulatory and business contexts.