On the risk of confusing interpretability with explicability
Christian Herzog
Abstract
Abstract This Comment explores the implications of a lack of tools that facilitate an explicable utilization of epistemologically richer, but also more involved white-box approaches in AI. In contrast, advances in explainable artificial intelligence for black-box approaches have led to the availability of semi-standardized and attractive toolchains that offer a seemingly competitive edge over inherently interpretable white-box models in terms of intelligibility towards users. Consequently, there is a need for research on efficient tools for rendering interpretable white-box approaches in AI explicable to facilitate responsible use.
Topics & Concepts
InterpretabilityRendering (computer graphics)White boxBlack boxComputer scienceIntelligibility (philosophy)Artificial intelligenceData scienceMachine learningEpistemologyPhilosophyExplainable Artificial Intelligence (XAI)Adversarial Robustness in Machine LearningEthics and Social Impacts of AI