Putting Explainable AI to the Test: A Critical Look at AI Evaluation Approaches

As AI-powered recommendation systems become more embedded in everyday decisions, ensuring their transparency and trustworthiness is critical. A recent CSET report examines how researchers evaluate AI explainability, revealing inconsistencies in how explainability is defined and assessed. While some studies focus on whether AI explanations meet technical specifications, few evaluate whether these explanations actually help users make informed decisions. Policymakers are encouraged to develop clear standards and invest in AI safety expertise to ensure that explainability evaluations lead to meaningful, real-world improvements.