Min Kyung Lee
Research Scientist, Center for Machine Learning and Health, Carnegie Mellon University
- Friday, October 30, 2015 - 1:30pm to 2:30pm
- NSH 1305
Even beyond industrial domains such as production and operation management, algorithms increasingly act as mediators between people and the world around them, from curating Facebook walls to matching riders with Uber drivers to selecting top job candidates on LinkedIn. Yet despite their often-touted objectivity and efficiency, algorithms can make biased, unfair, incomprehensible, or untrustworthy decisions behind the scenes. I argue that we need to understand the social and decision-making implications of these algorithms, and take design approaches to make them more useful, fair and enjoyable. In this talk, I will present the findings of a study I conducted with Uber and Lyft drivers, which revealed the systematic biases that people bring to algorithmic decisions, the limitations of algorithmic assumptions about users, and the importance of empowering users to make their own decisions using data. I will then discuss how I further explore these problems through my ongoing research in on-demand work, smart cities, and healthcare.
Sorry, no video of this lecture is available for copyright reasons.
- Speaker's Bio
Min Kyung Lee is a research scientist in human-computer interaction at the Center for Machine Learning and Health at Carnegie Mellon University. Her research examines the social and decision-making implications of intelligent systems and supports the development of more human-centered machine learning applications. Dr. Lee is a Siebel Scholar and has received several best paper awards, as well as an Allen Newell Award for Research Excellence. Her work has been featured in media outlets such as the New York Times, New Scientist, and CBS. She received a PhD in HCI in 2013 and an MDes in Interaction Design from Carnegie Mellon, and a BS summa cum laude in Industrial Design from KAIST.
- Speaker's Website
- Jodi Forlizzi