{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:14Z","timestamp":1761176114317,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>In this paper, I describe lessons learned in nearly 20 years of evaluating explanation interfaces with people. In my work, I have observed that tailoring the explanation to the user (e.g., a domain expert or a layperson) and task (e.g., decision support or model improvement), or context (e.g., under time pressure) is necessary for meaningful assessment of explanation quality (e.g., correct decisions or better understanding). Learning from trends in empirical research methods (natural language processing, information retrieval, and machine learning), I further argue that this is an issue for both human-computer interaction and machine learning. Real-world factors influencing the performance of systems are at best implicitly encoded when inferring probabilities from observations, and at worst, no longer applicable to the settings for which they are used. In seeking a balanced approach between this reality and a pragmatic, data-driven and (by necessity) reductionist approach, I make constructive suggestions for evaluating the quality of explainable artificial intelligence.<\/jats:p>","DOI":"10.3233\/faia250781","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:42:07Z","timestamp":1761126127000},"source":"Crossref","is-referenced-by-count":0,"title":["Measuring Explanation Quality \u2013 A Path Forward"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1663-1627","authenticated-orcid":false,"given":"Nava","family":"Tintarev","sequence":"first","affiliation":[{"name":"Department of Advanced Computing Sciences, Maastricht University, the Netherlands"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250781","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:42:08Z","timestamp":1761126128000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250781"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250781","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}