Our weekly seminar series welcomes Schwartz Reisman Faculty Affiliate Nisarg Shah, assistant professor in the Department of Computer Science at the University of Toronto.
Shah’s research lies at the intersection of computer science and economics and focuses on issues of fairness, efficiency, elicitation, and incentives that arise when humans are affected by algorithmic decision-making.
Talk title
“Fairness in algorithmic decision-making: Theory & applications”
Abstract
Fairness in algorithmic decision-making has been studied for decades at the intersection of economics and computer science in the context of resource allocation. Its real-world applications include inheritance division, allocation of CPU and RAM in computing environments, and rent-sharing among roommates.
In this talk, I will first discuss our theoretical work on fair division and how it has helped tens of thousands of people fairly divide resources through our not-for-profit website Spliddit.org. Then, I will argue that these economic notions of fairness are also quite applicable to modern algorithmic paradigms such as machine learning.
Recommended reading
This will be a high-level talk that will cover seven papers under two areas:
Fair division:
“Group Fairness for the Allocation of Indivisible Goods” (PDF)
“Best of Both Worlds: Ex-Ante and Ex-Post Fairness in Resource Allocation” (PDF)
“Fair Division with Binary Valuations: One Rule to Rule Them All” (PDF)
Fairness in machine learning:
About Nisarg Shah
Nisarg Shah is an assistant professor in the Department of Computer Science at the University of Toronto. Shah received his PhD from Carnegie Mellon University and was a postdoctoral researcher at Harvard University. He is the winner of the 2013-2014 Hima and Jive Graduate Fellowship, the 2014-2015 Facebook Fellowship, and the 2016 Victor Lesser Distinguished Dissertation Award. Shah’s research lies at the intersection of computer science and economics and focuses on issues of fairness, efficiency, elicitation, and incentives that arise when humans are affected by algorithmic decision-making.
About the SRI Seminar Series
The SRI Seminar Series brings together the Schwartz Reisman community and beyond for a robust exchange of ideas that advance scholarship at the intersection of technology and society. Seminars are led by a leading or emerging scholar and feature extensive discussion.
Each week, a featured speaker will present for 45 minutes, followed by 45 minutes of discussion. Registered attendees will be emailed a Zoom link approximately one hour before the event begins. The event will be recorded and posted online.
Nisarg Shah