{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:48:41Z","timestamp":1780609721221,"version":"3.54.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685489","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"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":[[2024,10,16]]},"abstract":"<jats:p>Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches. A recent paper has proposed a paradigm shift calling for hypothesis-driven XAI through a conceptual framework called evaluative AI that gives people evidence that supports or refutes hypotheses without necessarily giving a decision-aid recommendation. In this paper, we describe and evaluate an approach for hypothesis-driven XAI based on the Weight of Evidence (WoE) framework, which generates both positive and negative evidence for a given hypothesis. Through human behavioural experiments, we show that our hypothesis-driven approach increases decision accuracy and reduces reliance compared to a recommendation-driven approach and an AI-explanation-only baseline, but with a small increase in under-reliance compared to the recommendation-driven approach. Further, we show that participants used our hypothesis-driven approach in a materially different way to the two baselines.<\/jats:p>","DOI":"10.3233\/faia240571","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:50:55Z","timestamp":1729169455000},"source":"Crossref","is-referenced-by-count":7,"title":["Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence"],"prefix":"10.3233","author":[{"given":"Thao","family":"Le","sequence":"first","affiliation":[{"name":"School of Computing and Information Systems, The University of Melbourne"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tim","family":"Miller","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, The University of Queensland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liz","family":"Sonenberg","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, The University of Melbourne"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronal","family":"Singh","sequence":"additional","affiliation":[{"name":"CSIRO\u2019s Data61"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240571","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T12:50:55Z","timestamp":1729169455000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240571","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}