SCS Faculty Candidate
- Gates Hillman Centers
- ASA Conference Room 6115
- MIN KYUNG LEE
- Research Scientist
- Machine Learning Department, Center for Machine Learning and Health
- Carnegie Mellon University
Toward Fair and Trustworthy Algorithmic Decisions: A Human-Centered Perspective
Advances in artificial intelligence (AI) are transforming how people work and govern citizens and organizations. Now more than ever, computational algorithms increasingly make decisions that people used to make, changing the practices of managers, policy makers, physicians, teachers, police, judges, on-demand labor platforms, online communities, smart cities, and more. Algorithms match patients to doctors, passengers to ride-sharing drivers, job candidates to recruiters, and donations to recipients. However, the impact of algorithmic decisions on human perception, decision-making, and society is largely unexamined.
My goal is to build generalizable knowledge of the social and decision-making implications of algorithmic decisions and to use that knowledge to develop AI design principles. I envision AI for all, drawing from human-computer interaction, behavioral science, and design. I am engaged in field projects and qualitative and experimental research. I have done a study on how Uber and Lyft drivers worked with algorithm in their apps. A current field project is helping to design smart community services for food-insecure and homeless populations. Today, in this talk, I will describe experimental studies that investigate how people perceive managerial algorithmic decisions, such as hiring or task division, and when and how people judge them to be erroneous or unfair. A follow-up study elucidates how adding transparency and control to provably fair division algorithms can enable people to make fair decisions for themselves.
Min Kyung Lee is a research scientist in Human-Computer Interaction in the Machine Learning Department and the Center for Machine Learning and Health at Carnegie Mellon University. Dr. Lee has conducted some of the first studies that empirically examine the social implications of algorithms’ emerging roles in management and governance in society, looking at how people perceive algorithms and how we can design fairer and more trustworthy algorithmic services that work in the real world. Her current research is inspired by and complements her previous work on social robots for long-term interaction, seamless human-robot handovers, and telepresence robots. Dr. Lee is a Siebel Scholar and has received several best paper and honorable mention awards in venues such as CHI, CSCW, DIS and HRI, as well as an Allen Newell Award for Research Excellence. She is an associate editor of the ACM Transactions on Human-Robot Interaction. Her work has been featured in media outlets such as the New York Times, New Scientist, and CBS. She received a PhD in Human-Computer Interaction and an MDes in Interaction Design from Carnegie Mellon.