{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T00:06:17Z","timestamp":1773101177483,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643683089","type":"print"},{"value":"9781643683096","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,9,19]],"date-time":"2022-09-19T00:00:00Z","timestamp":1663545600000},"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":[[2022,9,19]]},"abstract":"<jats:p>Widespread application of uninterpretable machine learning systems for sensitive purposes has spurred research into elucidating the decision making process of these systems. These efforts have their background in many different disciplines, one of which is the field of AI &amp; law. In particular, recent works have observed that machine learning training data can be interpreted as legal cases. Under this interpretation the formalism developed to study case law, called the theory of precedential constraint, can be used to analyze the way in which machine learning systems draw on training data \u2013 or should draw on them \u2013 to make decisions. These works predominantly stay on the theoretical level, hence in the present work the formalism is evaluated on a real world dataset. Through this analysis we identify a significant new concept which we call landmark cases, and use it to characterize the types of datasets that are more or less suitable to be described by the theory.<\/jats:p>","DOI":"10.3233\/faia220200","type":"book-chapter","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T07:48:29Z","timestamp":1663832909000},"source":"Crossref","is-referenced-by-count":5,"title":["Landmarks in Case-Based Reasoning: From Theory to Data"],"prefix":"10.3233","author":[{"given":"Wijnand","family":"van Woerkom","sequence":"first","affiliation":[{"name":"Department of Information and Computing Sciences, Utrecht University, The Netherlands"}]},{"given":"Davide","family":"Grossi","sequence":"additional","affiliation":[{"name":"Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, The Netherlands"},{"name":"Amsterdam Center for Law and Economics, University of Amsterdam, The Netherlands"},{"name":"Institute for Logic, Language and Computation, University of Amsterdam, The Netherlands"}]},{"given":"Henry","family":"Prakken","sequence":"additional","affiliation":[{"name":"Department of Information and Computing Sciences, Utrecht University, The Netherlands"},{"name":"Faculty of Law, University of Groningen, The Netherlands"}]},{"given":"Bart","family":"Verheij","sequence":"additional","affiliation":[{"name":"Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, The Netherlands"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","HHAI2022: Augmenting Human Intellect"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA220200","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T07:48:52Z","timestamp":1663832932000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA220200"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,19]]},"ISBN":["9781643683089","9781643683096"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia220200","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,19]]}}}