{"id":32217,"date":"2026-01-21T17:01:31","date_gmt":"2026-01-21T17:01:31","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/integrating-human-knowledge-for-explainable-ai\/"},"modified":"2026-06-08T13:18:35","modified_gmt":"2026-06-08T13:18:35","slug":"integrating-human-knowledge-for-explainable-ai","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/integrating-human-knowledge-for-explainable-ai\/","title":{"rendered":"Integrating human knowledge for explainable {AI}"},"content":{"rendered":"<p>This paper presents a methodology for integrating human expert knowledge into machine learning ({ML}) workflows to improve both model interpretability and the quality of explanations produced by explainable {AI} ({XAI}) techniques. We strive to enhance standard {ML} and {XAI} pipelines without modifying underlying algorithms, focusing instead on embedding domain knowledge at two stages: (1) during model development through expert-guided data structuring and feature engineering, and (2) during explanation generation via domain-aware synthetic neighbourhoods. Visual analytics is used to support experts in transforming raw data into semantically richer representations. We validate the methodology in two case studies: predicting {COVID}-19 incidence and classifying vessel movement patterns. The studies demonstrated improved alignment of models with expert reasoning and better quality of synthetic neighbourhoods. We also explore using large language models ({LLMs}) to assist experts in developing domain-compliant data generators. Our findings highlight both the benefits and limitations of existing {XAI} methods and point to a research direction for addressing these gaps.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a methodology for integrating human expert knowledge into machine learning ({ML}) workflows to improve both model interpretability and the quality of explanations produced by explainable {AI} ({XAI}) techniques. We strive to enhance standard {ML} and {XAI} pipelines without modifying underlying algorithms, focusing instead on embedding domain knowledge at two stages: (1) during model development through expert-guided data structuring and feature engineering, and (2) during explanation generation via [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-32217","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32217","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\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32217\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32217"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32217"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}