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dc.contributor.authorJaved, Rana Tallal
dc.contributor.authorNasir, Osama
dc.contributor.authorVanhée, Loïs
dc.contributor.authorBorit, Melania
dc.contributor.authorZea, Elias
dc.contributor.authorGupta, Shivam
dc.contributor.authorVinuesa, Ricardo
dc.contributor.authorQadir, Junaid
dc.date.accessioned2022-05-19T08:47:50Z
dc.date.available2022-05-19T08:47:50Z
dc.date.issued2022-03-26
dc.description.abstractThe domain of Artificial Intelligence (AI) ethics is not new, with discussions going back at least 40 years. Teaching the principles and requirements of ethical AI to students is considered an essential part of this domain, with an increasing number of technical AI courses taught at several higher-education institutions around the globe including content related to ethics. By using Latent Dirichlet Allocation (LDA), a generative probabilistic topic model, this study uncovers topics in teaching ethics in AI courses and their trends related to where the courses are taught, by whom, and at what level of cognitive complexity and specificity according to Bloom’s taxonomy. In this exploratory study based on unsupervised machine learning, we analyzed a total of 166 courses: 116 from North American universities, 11 from Asia, 36 from Europe, and 10 from other regions. Based on this analysis, we were able to synthesize a model of teaching approaches, which we call BAG (Build, Assess, and Govern), that combines specific cognitive levels, course content topics, and disciplines affiliated with the department(s) in charge of the course. We critically assess the implications of this teaching paradigm and provide suggestions about how to move away from these practices. We challenge teaching practitioners and program coordinators to reflect on their usual procedures so that they may expand their methodology beyond the confines of stereotypical thought and traditional biases regarding what disciplines should teach and how.en_US
dc.identifier.citationJaved, Nasir, Vanhée L, Borit M, Zea E, Gupta S, Vinuesa, Qadir. Get out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curricula. The journal of artificial intelligence research. 2022;73:933-965en_US
dc.identifier.cristinIDFRIDAID 2018449
dc.identifier.doihttps://doi.org/10.1613/jair.1.13425
dc.identifier.issn1076-9757
dc.identifier.issn1943-5037
dc.identifier.urihttps://hdl.handle.net/10037/25222
dc.language.isoengen_US
dc.publisherAI Access Foundationen_US
dc.publisherAssociation for the Advancement of Artificial Intelligence [Associate Organisation]en_US
dc.relation.journalThe journal of artificial intelligence research
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.titleGet out of the BAG! Silos in AI Ethics Education: Unsupervised Topic Modeling Analysis of Global AI Curriculaen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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