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Co-Designing Algorithmic Systems in the Public Interest

Michael Madaio: Ph.D Candidate, Human-Computer Interaction Institute at Carnegie Mellon University.

Event Details

Date
Monday, February 24, 2020
Time
10-11 a.m.
Location
4207 HC White - Bunge Room, Helen C. White Hall
Description

Abstract: Algorithmic decision systems are increasingly used in the public sector to provide more equitable provision of public services, particularly in low-resource contexts. Too often, however, algorithmic systems amplify societal biases via their design and use. In this talk, I will share results from my work on co-designing algorithmic systems with public stakeholders in two main research strands: 1) informing public sector decision-making and 2) supplementing public education in low-resource contexts. Finally, I will share findings from emerging research on co-designing AI fairness guidelines with industry practitioners, and discuss what these findings suggest for the design of more equitable algorithmic systems in the public interest.

Bio: Michael is a Ph.D. candidate in the Human-Computer Interaction Institute at Carnegie Mellon University. His research focuses on co-designing human-centered algorithmic systems in the public sector. He is a Siebel Scholar and a fellow with the U.S. Institute for Education Sciences’ Program for Interdisciplinary Education Research. He has received multiple best paper awards for his work from conferences including ACM’s Knowledge Discovery and Data Mining (KDD’16) and Computing and Sustainable Societies (COMPASS’19), and his research has been deployed in multiple cities and countries. He has been a research intern with Microsoft Research and the United Nations Institute on Computing and Society, and he has an M.S. in Digital Media from Georgia Tech and an M.Ed. and a B.A. in English Literature from the University of Maryland, College Park.

Cost
Free

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