Put it to the Test: Getting Serious About Explanation in Explainable Artificial Intelligence

Abstract Artificial intelligence (AI) has become a topic of major interest to philosophers of science. Among the issues commonly discussed is AI’s opacity . To remedy opacity, scientists have provided methods commonly subsumed under the label ‘eXplaibable Artificial Intelligence’ (XAI) that aim to make AI and its outputs ‘interpretable’ and ‘explainable’. However, there is little interaction between developments in XAI and philosophical debates on scientific explanation. We here improve on this situation and argue for a descriptive and a normative thesis: (i) When suitably embedded into scientific research processes, XAI methods’ outputs can facilitate genuine scientific understanding. (ii) In order for XAI outputs to fulfill this function, they should be made testable . We will support our theses by building on recent and long-standing ideas from philosophy of science, by comparing them to a recent framework from the XAI community, and by showcasing their applicability to case studies from the life sciences.