A set of processes and methods that allows human users to comprehend and trust the results and outputs created by the machine learning algorithms of AI systems. Instead of simply producing a score or recommendation, explainable AI shows which factors the algorithm considered and how much weight each factor received.

Many AI hiring tools operate as “black boxes,” making it difficult to determine why a candidate was selected or rejected. Explainable AI mitigates this problem by offering visibility into how the model functions, allowing employers to identify potential sources of discrimination and to demonstrate accountability in hiring practices. This interpretability enables users—such as employers, regulators, and job applicants—to comprehend, trust, and audit algorithmic outcomes. This is critical  for ensuring fairness, detecting bias, maintaining compliance with anti-discrimination laws, and ensuring transparency and accountability. A growing concern is that many employers purchase AI systems from vendors without fully understanding how those systems function. This lack of insight can lead to unintended legal and ethical consequences when algorithmic decisions replicate or amplify bias.

See: AI Hiring Discrimination, Lawsuits & Accountability | Learn & Work Ecosystem Library