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Sociotechnically Grounded Responsible Machine Learning
IRB 5137 and Zoom: https://umd.zoom.us/j/99805654842?pwd=c21PRDh6c0MvYXhDa09qb1dTck9CZz09
Friday, November 17, 2023, 1:00-2:00 pm Calendar
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With the widespread proliferation of machine learning, there arises both the opportunity for societal benefit as well as the risk of harm. This problem requires sociotechnically grounded approaches to thoughtfully confront. However, this is hard as technical approaches tend to overfit to mathematical definitions of fairness which may correlate poorly to real-world constructs of fairness, and normative approaches may be too abstract to translate well into practice. In my research I re-orient technical ML research by interrogating the neglected normative concerns behind canonical assumptions, ground technical work in disciplines outside of computer science like psychology that have a long history of studying questions of inequality and harm, and confront practical issues that arise in the research to reality gap. I thus use my technical expertise to engage with the work of computer scientists, social scientists, and real world practitioners to develop realistic, equitable, and impactful ML fairness interventions.


Angelina Wang is a PhD student at Princeton University advised by Olga Russakovsky. Her research is in the area of machine learning fairness and algorithmic bias. Her work has been published at ICML and AAAI (machine learning), ICCV and IJCV (computer vision), FAccT and JRC (responsible computing), and Big Data & Society (interdisciplinary). She has been recognized by the NSF GRFP, EECS Rising Stars, and Siebel Scholarship. Previously, she has interned with Microsoft Research and Arthur AI, and received a B.S. in Electrical Engineering and Computer Science from UC Berkeley.

This talk is organized by Emily Dacquisto