{"title":"使用推荐系统的民间理论来告知以人为中心的可解释人工智能(HCXAI)","authors":"Michael Ridley","doi":"10.5206/cjils-rcsib.v46i2.15723","DOIUrl":null,"url":null,"abstract":"This study uses folk theories of the Spotify music recommender system to inform the principles of human-centered explainable AI (HCXAI). The results show that folk theories can reinforce, challenge, and augment these principles facilitating the development of more transparent and explainable recommender systems for the non-expert, lay public.","PeriodicalId":377680,"journal":{"name":"The Canadian Journal of Information and Library Science","volume":"28 6","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using folk theories of recommender systems to inform human-centered explainable AI (HCXAI)\",\"authors\":\"Michael Ridley\",\"doi\":\"10.5206/cjils-rcsib.v46i2.15723\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study uses folk theories of the Spotify music recommender system to inform the principles of human-centered explainable AI (HCXAI). The results show that folk theories can reinforce, challenge, and augment these principles facilitating the development of more transparent and explainable recommender systems for the non-expert, lay public.\",\"PeriodicalId\":377680,\"journal\":{\"name\":\"The Canadian Journal of Information and Library Science\",\"volume\":\"28 6\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Canadian Journal of Information and Library Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5206/cjils-rcsib.v46i2.15723\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Canadian Journal of Information and Library Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5206/cjils-rcsib.v46i2.15723","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Using folk theories of recommender systems to inform human-centered explainable AI (HCXAI)
This study uses folk theories of the Spotify music recommender system to inform the principles of human-centered explainable AI (HCXAI). The results show that folk theories can reinforce, challenge, and augment these principles facilitating the development of more transparent and explainable recommender systems for the non-expert, lay public.
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