Colloquium Presentation of Dr. Nisarg Shah:"Pushing the Limits of Fairness in Resource Allocation"

 

 

Date: Friday March 25, 2022 

Time: 11:00am-12:30pm  

Meeting URL:    https://us06web.zoom.us/j/86567632518?from=addon 

Passcode: If interested in attending this event, contact the Graduate Secretary at csgradinfo@uwindsor.ca with sufficient notice before the event to obtain the passcode

 

In this talk, I will discuss the millennia-old problem of how to divide resources fairly. This has numerous everyday applications such as estate division, divorce settlement, or the allocation of office spaces to faculty members at a university.  

 

I will introduce compelling fairness and efficiency guarantees proposed in early economics literature, and then describe algorithms which provably achieve such guarantees. The focus will be on pushing the limits of fairness by successively achieving stronger guarantees. The role of user preferences, user groups, and randomization will be explored.  

 

Some of these algorithms have been deployed to Spliddit.org, a not-for-profit website I co-developed that has helped hundreds of thousands of users. No prior background will be required. 

 

Keywords: algorithmic fairness, resource allocation, multi-agent systems 

Nisarg Shah is an assistant professor of computer science at the University of Toronto. He has been recognized as AI's 10 to Watch by IEEE Intelligent Systems in 2020. He is also the winner of the 2016 IFAAMAS Victor Lesser Distinguished Dissertation Award and the 2014-2015 Facebook PhD Fellowship. Shah conducts research at the intersection of computer science and economics, addressing issues of fairness, efficiency, elicitation, and incentives that arise when humans are affected by algorithmic decision-making. His recent work develops theoretical foundations for fairness in fields such as voting, resource allocation, and machine learning. He has co-developed two not-for-profit websites, Spliddit.org and RoboVote.org (temporarily unavailable), which have helped more than 200,000 users make provably fair and optimal decisions in their everyday lives. He earned his PhD in computer science at Carnegie Mellon University and was a postdoctoral fellow at Harvard University. 

 

 

Email: nisarg@cs.toronto.edu

Twitter: @nsrg_shah

 

 

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