{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T02:35:22Z","timestamp":1772937322569,"version":"3.50.1"},"reference-count":83,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:00:00Z","timestamp":1710374400000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"JST CREST","award":["JPMJCR19F2"],"award-info":[{"award-number":["JPMJCR19F2"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>While AI technology is becoming increasingly prevalent in our daily lives, the comprehension of machine learning (ML) among non-experts remains limited. Interactive machine learning (IML) has the potential to serve as a tool for end users, but many existing IML systems are designed for users with a certain level of expertise. Consequently, it remains unclear whether IML experiences can enhance the comprehension of ordinary users. In this study, we conducted a public event using an IML system to assess whether participants could gain technical comprehension through hands-on IML experiences. We implemented an interactive sound classification system featuring visualization of internal feature representation and invited visitors at a science museum to freely interact with it. By analyzing user behavior and questionnaire responses, we discuss the potential and limitations of IML systems as a tool for promoting technical comprehension among non-experts.<\/jats:p>","DOI":"10.1093\/iwc\/iwae007","type":"journal-article","created":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T16:24:13Z","timestamp":1710433453000},"page":"155-171","source":"Crossref","is-referenced-by-count":3,"title":["Technical Understanding from Interactive Machine Learning Experience: a Study Through a Public Event for Science Museum Visitors"],"prefix":"10.1093","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5547-3831","authenticated-orcid":false,"given":"Wataru","family":"Kawabe","sequence":"first","affiliation":[{"name":"The University of Tokyo Institute of Industrial Science, , 4-6-1, Meguro-ku, Tokyo 153-8505, Japan"}]},{"given":"Yuri","family":"Nakao","sequence":"additional","affiliation":[{"name":"Fujitsu Limited AI Trust Research Centre, , 4-1-1, Kamikodanaka, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan"}]},{"given":"Akihisa","family":"Shitara","sequence":"additional","affiliation":[{"name":"University of Tsukuba Graduate School of Library, Information and Media Studies, , 1-2, Kasuga, Tsukuba, Ibaraki, 305-8550, Japan"}]},{"given":"Yusuke","family":"Sugano","sequence":"additional","affiliation":[{"name":"The University of Tokyo Institute of Industrial Science, , 4-6-1, Meguro-ku, Tokyo 153-8505, Japan"}]}],"member":"286","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"2024071711304403000_ref1","first-page":"265","volume-title":"USENIX Symposium on Operating Systems Design and Implementation","author":"Abadi","year":"2016"},{"key":"2024071711304403000_ref2","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"2024071711304403000_ref3","first-page":"1","volume-title":"Proc. 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