NIST AI RMF Podcast 05 - NISTIR 8367 - Psychological Foundations Of Explainability & Interpretability In AI

National Institute of Standards and Technology Interagency Report 8367, Psychological Foundations of Explainability and Interpretability in AI, examines explainable AI through the lens of human cognition and decision-making. Rather than focusing solely on technical transparency, the report emphasizes how people understand, trust, and interact with AI systems. It explains that effective explanations must align with human mental models, account for cognitive limitations, and support users’ goals, context, and expertise levels. The publication distinguishes between interpretability (how well a system’s internal mechanics can be understood) and explainability (how well a system communicates its reasoning to humans), highlighting that explanations must be accurate, meaningful, and usable to improve trust calibration rather than blind trust. Ultimately, NISTIR 8367 argues that explainable AI is not purely a technical challenge but a socio-technical one, requiring interdisciplinary collaboration across AI development, psychology, and human-computer interaction to ensure AI systems are understandable, accountable, and appropriately trusted.