An Introduction to Explainable Artificial Intelligence Applications for Industrial-Organizational Psychology
Bradley D. Pitcher, Pengda Wang · 2026
Abstract This chapter addresses the critical need for transparency and interpretability in artificial intelligence (AI) systems used for human resource management processes. The chapter defines explainable AI (XAI) as processes and methods that make AI systems more transparent and interpretable to satisfy stakeholder needs for understanding system functioning and outputs. The chapter presents a comprehensive framework addressing four key considerations: model characteristics, explanation approaches, method characteristics, and explanation quality evaluation. The chapter explores the performance-explainability tradeoff between complex black-box models and inherently interpretable models, discussing explanation-by-design and post-hoc explanation approaches. Key XAI method characteristics include explanation target, transportability, and breadth of application. The chapter demonstrates practical applications of XAI methods to AI selection systems, personnel management systems, training applications, and system audits. Important considerations include potential pitfalls such as inappropriate trust calibration. The chapter concludes with future research directions and practical implementation guidance.