{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T01:30:32Z","timestamp":1768181432669,"version":"3.49.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643682525","type":"print"},{"value":"9781643682532","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T00:00:00Z","timestamp":1638403200000},"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":[[2021,12,2]]},"abstract":"<jats:p>The justification of an algorithm\u2019s outcomes is important in many domains, and in particular in the law. However, previous research has shown that machine learning systems can make the right decisions for the wrong reasons: despite high accuracies, not all of the conditions that define the domain of the training data are learned. In this study, we investigate what the system does learn, using state-of-the-art explainable AI techniques. With the use of SHAP and LIME, we are able to show which features impact the decision making process and how the impact changes with different distributions of the training data. However, our results also show that even high accuracy and good relevant feature detection are no guarantee for a sound rationale. Hence these state-of-the-art explainable AI techniques cannot be used to fully expose unsound rationales, further advocating the need for a separate method for rationale evaluation.<\/jats:p>","DOI":"10.3233\/faia210341","type":"book-chapter","created":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T09:48:19Z","timestamp":1638870499000},"source":"Crossref","is-referenced-by-count":7,"title":["Rationale Discovery and Explainable AI"],"prefix":"10.3233","author":[{"given":"Cor","family":"Steging","sequence":"first","affiliation":[{"name":"Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University of Groningen"}]},{"given":"Silja","family":"Renooij","sequence":"additional","affiliation":[{"name":"Department of Information and Computing Sciences, Utrecht University"}]},{"given":"Bart","family":"Verheij","sequence":"additional","affiliation":[{"name":"Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University of Groningen"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Legal Knowledge and Information Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA210341","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,7]],"date-time":"2021-12-07T09:48:20Z","timestamp":1638870500000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA210341"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,2]]},"ISBN":["9781643682525","9781643682532"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia210341","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,2]]}}}