Events Calendar

Machine Learning Center Seminar: Yang Liu (UC Santa Cruz) - Agency Bias in Machine Learning

Speaker: Yang Liu, UC Santa Cruz

Talk Title: Agency Bias in Machine Learning

Series: Machine Learning Seminar Series

Abstract: A trained machine learning model (e.g., a classifier) will ultimately observe data generated according to agents' responses. For instance, the rising literature on strategic classification concerns the setting where agents are fully rational and can best respond to a classifier in their own interests. The above interaction will lead to a distribution shift between training and deployment and will challenge the existing performance and fairness guarantees of the trained model. In this talk, I'll discuss three types of agency bias that arise due to the above interactional effects between agents and machine learning models. I'll then go over possible mitigation efforts, including our very recent works on certifying the fairness guarantees on an unknown and possibly different deployment distribution.

References:
[1] Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents. Andrew Estornell, Sanmay Das, Yang Liu and Yevgeniy Vorobeychik. Preprint, 2022.
[2] Actionable Recourse in Linear Classification. Berk Ustun, Alexander Spangher and Yang Liu
ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2019.
[3] Unintended Selection: Persistent Qualification Rate Disparities and Interventions. Reilly Raab and Yang Liu. Neural Information Processing Systems (NeurIPS), 2021.
[4] Fairness Transferability Subject to Bounded Distribution Shift. Yatong Chen, Reilly Raab, Jialu Wang and Yang Liu. Neural Information Processing Systems (NeurIPS), 2022.

Prof. Liu will give his talk in person at EEB 248 and we will also host the talk over Zoom.

Register in advance for this webinar at:

https://usc.zoom.us/webinar/register/WN_WtHgpFUFSbCI214E2i9q3Q

After registering, attendees will receive a confirmation email containing information about joining the webinar.

This lecture satisfies requirements for CSCI 591: Research Colloquium.


Biography: Yang Liu is currently an Assistant Professor of Computer Science and Engineering at UC Santa Cruz (2018 - present). He was previously a postdoctoral fellow at Harvard University (2016 - 2018). He obtained his Ph.D. degree from the Department of EECS, University of Michigan, Ann Arbor in 2015. He is interested in weakly supervised learning and algorithmic fairness. He is a recipient of the NSF CAREER Award and the NSF Fairness in AI award (lead PI). He has been selected to participate in several high-profile projects, including DARPA SCORE and IARPA HFC. His recent works have won four best paper awards at relevant workshops.


Host: Yan Liu

Webcast: https://usc.zoom.us/webinar/register/WN_WtHgpFUFSbCI214E2i9q3Q

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

WebCast Link: https://usc.zoom.us/webinar/register/WN_WtHgpFUFSbCI214E2i9q3Q

Audiences: Everyone Is Invited

Contact: Department of Computer Science