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Machine learning (ML) techniques are prevalent in the education sphere, from their use in Massive Open Online Courses (MOOCs), to admissions, to predicting student dropout. Recently, public institutions faced controversy for high-profile applications such as GRADE, used in graduate computer science admissions at the University of Texas, and predictive analytics for forecasting A-level grades in the U.K., because they exacerbate existing inequalities.

With these highly-publicized failures — and the rapid proliferation of ML technologies accelerated in part by the COVID-19 pandemic — an urgent need exists to investigate how ML supports holistic education principles and goals. In this talk, Serena Wang, Ph.D. candidate at UC Berkeley, will present results from a qualitative study based on interviews with education domain experts, grounded in ML for education papers published in several highly-regarded ML conferences over the past decade.

Our central research goal is to critically examine whether the stated or implied societal objectives of these papers are aligned with the ML problem formulation, objectives and interpretation of results. This work joins a growing number of meta-analytical studies as well as critical analyses of the societal impact of ML. Specifically, this work fills a cross-disciplinary gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.

Zoom registration: https://uncc.zoom.us/meeting/register/tJ0lcO6pqDIiGNa0xqhRX6WOZuRaiBnoThKd

This talk is part of the Biased AI series, which explores problems and solutions in making AI more just. The first talk was by Ben Green, Postdoctoral Scholar in the Society of Fellows at the Universty of Michigan, on Oct. 5. Talks will be archived on the Center's YouTube Channel.

 

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