https://www.spreaker.com/podcast/fairness-and-equity-in-ai-for-education--5082837enSocial SciencesCopyright Walter L. Leitehttps://d3wo5wojvuv7l.cloudfront.net/t_rss_itunes_square_1400/images.spreaker.com/original/7ed20d2b7a4c35bbcde6af582789d060.jpghttps://www.spreaker.com/podcast/fairness-and-equity-in-ai-for-education--5082837Fri, 18 Feb 2022 15:00:20 +0000Walter L. LeiteWalter L. Leitewalter.leite@coe.ufl.eduThe opportunities and dangers of using educational products driven by Artificial Intelligence (AI) in schools and universities are explored through the lenses of fairness and equity. This is a limited podcast series with five interviews with...falsea65db8e5-92b2-5042-a764-eddcfafc43fdserialhttps://www.spreaker.com/episode/episode-1-alan-rubel-the-dangers-of-offloading-decisions-to-ai--46351788https://api.spreaker.com/episode/46351788Thu, 02 Sep 2021 23:53:02 +0000Walter L. LeiteDr. Alan Rubel provides examples of implications of AI systems for fairness in both education and the criminal justice system. He compares human bias with AI bias, but also talks about the dangers of administrators offloading decisions to AI. Dr....2368artificial-intelligence,bias,data-mining,dropout-prediction,education,education-technology,e-learning,equity,ethics,fairness,gender,high-stakes-decisions,law,learning-analytics,machine-learning,offloading,online-learning,race,recommendation-systems,stealth-assessmentfalsefullhttps://www.spreaker.com/episode/episode-2-beverly-woolf-including-diversity-in-ai--46351795https://api.spreaker.com/episode/46351795Thu, 02 Sep 2021 23:54:06 +0000Walter L. LeiteDr. Beverly Woolf talks about the importance of obtaining a sample of students when training AI models that is representative of the population the system is intended to serve. She also talks about how AI can be useful to teachers and the importance...1432artificial-intelligence,bias,data-mining,diversity,education,education-technology,e-learning,equity,ethics,fairness,gender,intelligent-tutoring-systems,learning-analytics,machine-learning,online-learning,race,recommendation-systems,representative-sample,teachers,virtual-environmentsfalsefullhttps://www.spreaker.com/episode/episode-3-kyle-jones-giving-students-voice-in-applications-of-ai--46351797https://api.spreaker.com/episode/46351797Thu, 02 Sep 2021 23:55:21 +0000Walter L. LeiteDr. Kyle Jones talks about the importance of giving students choice on what data is collected about them for use in learning analytics. He also talks about responsibilities of institutions of higher education about justifying their use of student...2368artificial-intelligence,bias,data-mining,dropout-prediction,education-technology,e-learning,equity,ethics,fairness,higher-education,intelligent-tutoring-systems,learning-analytics,machine-learning,online-learning,race,recommendation-systems,stealth-assessment,student-voice,virtual-environments,virtual-schoolsfalsefullhttps://www.spreaker.com/episode/episode-4-rene-kizilcec-scalable-ai-to-broaden-participation--46351796https://api.spreaker.com/episode/46351796Thu, 02 Sep 2021 23:56:11 +0000Walter L. LeiteDr. Rene Kizilcec describes many sources of unfair outcomes of AI in education. He also describes the difficulties of transferability of AI models across contexts, and the fairness concerns that come not from the structure of AI but from how it is...2319achievement-gaps,artificial-intelligence,bias-sources,broaden-participation,data-mining,dropout-prediction,education,education-technology,equity,ethics,fairness,gender,learning-analytics,machine-learning,race,scalable-ai,transferability,unfair-outcomes,virtual-environments,virtual-schoolsfalsefullhttps://www.spreaker.com/episode/episode-5-michael-madaio-social-and-technical-solutions-to-fairness-in-ai--46351794https://api.spreaker.com/episode/46351794Thu, 02 Sep 2021 23:57:10 +0000Walter L. LeiteDr. Michael Madaio talks about how solutions to ensure fairness in AI are both technical and social and provides several examples of directions of research on fairness in AI. He points out the limitations of transparency in AI systems for education as...2132artificial-intelligence,bias,data-mining,dropout-prediction,education,education-technology,e-learning,equity,ethics,fairness,gender,human-computer-interaction,intelligent-tutoring-systems,learning-analytics,machine-learning,online-learning,race,recommendation-systems,transparency,virtual-environmentsfalsefull