{"id":32290,"date":"2026-01-21T17:01:38","date_gmt":"2026-01-21T17:01:38","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/from-scientific-theory-to-duality-of-predictive-artificial-intelligence-models\/"},"modified":"2026-11-13T11:04:29","modified_gmt":"2026-11-13T11:04:29","slug":"from-scientific-theory-to-duality-of-predictive-artificial-intelligence-models","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/from-scientific-theory-to-duality-of-predictive-artificial-intelligence-models\/","title":{"rendered":"From scientific theory to duality of predictive artificial intelligence models"},"content":{"rendered":"<p>In studies employing explainable artificial intelligence ({XAI}), model explanation, interpretation, and causality are often not clearly distinguished, leading to potential misunderstandings of model performance or relevance. For predictive {AI} models used in the natural sciences, the path leading from model explanation and interpretation to causal reasoning is of particular importance because it bridges theory and hypothesis-driven experimental design. Selected concepts from scientific theory can be taken into consideration to generate a conceptual framework for putting predictions into scientific perspective and recognizing potential caveats. For explainable models, it is argued that the scientific rationale underlying model derivation plays a decisive role in assessing and understanding predictions and exploring causal relationships, giving rise to the notion of model duality, as introduced herein.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>In studies employing explainable artificial intelligence ({XAI}), model explanation, interpretation, and causality are often not clearly distinguished, leading to potential misunderstandings of model performance or relevance. For predictive {AI} models used in the natural sciences, the path leading from model explanation and interpretation to causal reasoning is of particular importance because it bridges theory and hypothesis-driven experimental design. Selected concepts from scientific theory can be taken into consideration to generate [&hellip;]<\/p>\n","protected":false},"author":14,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"publication-year":[1681],"class_list":["post-32290","publication","type-publication","status-publish","hentry","publication-type-article","publication-year-1681"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32290","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":1,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32290\/revisions"}],"predecessor-version":[{"id":44205,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32290\/revisions\/44205"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32290"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32290"},{"taxonomy":"publication-year","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-year?post=32290"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}