{"id":32335,"date":"2026-01-21T17:01:45","date_gmt":"2026-01-21T17:01:45","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/put-it-to-the-test-getting-serious-about-explanation-in-explainable-artificial-intelligence\/"},"modified":"2026-09-17T11:23:43","modified_gmt":"2026-09-17T11:23:43","slug":"put-it-to-the-test-getting-serious-about-explanation-in-explainable-artificial-intelligence","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/put-it-to-the-test-getting-serious-about-explanation-in-explainable-artificial-intelligence\/","title":{"rendered":"Put it to the Test: Getting Serious about Explanation in Explainable Artificial Intelligence"},"content":{"rendered":"<p>Artificial Intelligence ({AI}) has become a topic of major interest to philosophers of science. Among the issues commonly discussed is {AI}\u2019s opacity. To remedy opacity, scientists have provided methods commonly subsumed under the label \u2018{eXplaibable} Artificial Intelligence\u2019 ({XAI}) that aim to make {AI} and its outputs \u2018interpretable\u2019 and \u2018explainable\u2019. 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\u2019 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Artificial Intelligence ({AI}) has become a topic of major interest to philosophers of science. Among the issues commonly discussed is {AI}\u2019s opacity. To remedy opacity, scientists have provided methods commonly subsumed under the label \u2018{eXplaibable} Artificial Intelligence\u2019 ({XAI}) that aim to make {AI} and its outputs \u2018interpretable\u2019 and \u2018explainable\u2019. However, there is little interaction between developments in {XAI} and philosophical debates on scientific explanation. We here improve on this situation [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32335","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32335","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/14"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32335\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32335"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32335"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}