{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T12:35:14Z","timestamp":1768998914847,"version":"3.49.0"},"reference-count":20,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["AI Matters"],"published-print":{"date-parts":[[2022,6]]},"abstract":"<jats:p>I always say to my students \"you are going to be the future data science leaders of the world\". Wherever they end up, I hope they apply critical (Cotter, 2020; Dasgupta &amp; Hill, 2020) and human-centered (Xu, 2019) thinking to the AI decisions they make. As AI algorithms become ever more omnipresent in our lives - from newsfeed organization to product recommendations and beyond - it is our responsibility as educators to equip our students with the necessary tools to interrogate the impacts of AI technology. Luckily, there has been a large push for the integration of ethics into AI curricula. Whether this is in Model AI assignments (Furey &amp; Martin, 2019) or entire conferences (such as FAccT), there is a demand for integrated and critical algorithmic literacies both in the classroom and outside of it.<\/jats:p>\n          <jats:p>As a social media researcher and AI educator, my work regularly contends with two pillars: Joy and Justice. In this article I intend to outline ways of integrating both play and critical interrogation into AI education, with examples from AI education scholarship (Druga, Vu, Likhith, &amp; Qiu, 2019; Ko et al., 2020) as well as a light experience report highlighting student work. My students join me on this article as they are the main inspiration for innovative joy and justice practices!<\/jats:p>","DOI":"10.1145\/3557785.3557790","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T18:21:12Z","timestamp":1669227672000},"page":"22-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["AI education matters"],"prefix":"10.1145","volume":"8","author":[{"given":"Yim","family":"Register","sequence":"first","affiliation":[{"name":"University of Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joseph William","family":"Tan Garcia","sequence":"additional","affiliation":[{"name":"University of Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nayan","family":"Kaushal","sequence":"additional","affiliation":[{"name":"University of Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dev","family":"Wilder","sequence":"additional","affiliation":[{"name":"University of Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobing","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Washington"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"1","volume-title":"International workshop on education in artificial intelligence k--12 (eduai'19)","author":"Ali S.","year":"2019","unstructured":"Ali , S. , Payne , B. H. , Williams , R. , Park , H. W. , & Breazeal , C. ( 2019 ). Constructionism, ethics, and creativity: Developing primary and middle school artificial intelligence education . In International workshop on education in artificial intelligence k--12 (eduai'19) (pp. 1 -- 4 ). Ali, S., Payne, B. H., Williams, R., Park, H. W., & Breazeal, C. (2019). Constructionism, ethics, and creativity: Developing primary and middle school artificial intelligence education. In International workshop on education in artificial intelligence k--12 (eduai'19) (pp. 1--4)."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43681-020-00002-7"},{"key":"e_1_2_1_3_1","volume-title":"Conference on fairness, accountability and transparency (pp. 77--91)","author":"Buolamwini J.","year":"2018","unstructured":"Buolamwini , J. , & Gebru , T. ( 2018 ). Gender shades: Intersectional accuracy disparities in commercial gender classification . In Conference on fairness, accountability and transparency (pp. 77--91) . Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on fairness, accountability and transparency (pp. 77--91)."},{"key":"e_1_2_1_4_1","volume-title":"Notes from the ai frontier: Insights from hundreds of use cases","author":"Chui M.","year":"2018","unstructured":"Chui , M. , Manyika , J. , Miremadi , M. , Henke , N. , Chung , R. , Nel , P. , & Malhotra , S. ( 2018 ). Notes from the ai frontier: Insights from hundreds of use cases . McKinsey Global Institute , 28. Chui, M., Manyika, J., Miremadi, M., Henke, N., Chung, R., Nel, P., & Malhotra, S. (2018). Notes from the ai frontier: Insights from hundreds of use cases. 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(2006). Components, curriculum, and community: Robots and robotics in undergraduate ai education. AI magazine, 27 (1), 11--11."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3311890.3311904"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3294008"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299758.3299764"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-6747-6"},{"key":"e_1_2_1_12_1","volume-title":"European conference on machine learning and principles and practice of knowledge discovery in databases (pp. 29--35)","author":"Huppenkothen D.","year":"2021","unstructured":"Huppenkothen , D. , & Eadie , G. ( 2021 ). Teaching the foundations of machine learning with candy . In European conference on machine learning and principles and practice of knowledge discovery in databases (pp. 29--35) . Huppenkothen, D., & Eadie, G. (2021). Teaching the foundations of machine learning with candy. 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