FAIR Lab
Pennsylvania State University
The Foundations of Artificial Intelligence Research Lab (FAIR) @ Penn State conducts foundational research at the intersection of artificial intelligence, economics, and computation. Our goal is to design novel principled solutions for robust and fair decision-making in the presence of multiple intelligent entities (e.g., humans, robots, or autonomous agents). These AI solutions have profound impact on several real-world applications ranging from healthcare resource allocation and refugee placement to recommendation systems and gig economies.
Members
Hadi Hosseini
Principle Investigator
Current & Past Members
Sanjukta Roy
Tomasz Wąs
Aghaheybat Mammadov
Medha Kumar
Ronak Singh
Irfan Tekdir
Levent Toksoz
Shivika Narang
Liwei Che
Kanika Sharma
Fatima Umar
Andrew Searns
Angelina Brilliantova
Yeting Bao
Sanjay Varma
James Ferris
Ritaban Bhattacharya
Laurel Perweiler
Theodora Bendlin
Summer Interns/REU Students
Alexander Adams, CMU: Summer 2022
Spencer Renenger, Cornell University: Summer 2022
Ronak Singh, Penn State: Summer 2022
Austin Hayes, University of Maryland, College Park: Summer 2021
Fatima Umar, Rochester Institute of Technology: Summer 2021
Nathan Ganger, University of Mount Union: Summer 2021
Sunand Raghupathi, Columbia University: Summer 2019
Gabriella Alexis, Hunter College: Summer 2019
Projects
Strategic Manipulation in Matching Markets
Synopsis: This project focuses on strategic behavior of agents (or a coalition of agents) in matching markets and its impact on stability and fairness.
Sample articles
Accomplice Manipulation of the Deferred Acceptance Algorithm
[arXiv / conference paper]Two for One & One for All: Two-Sided Manipulation in Matching Markets
[arXiv]
Ordinal Approximations of Fair Share
Synopsis: This project proposes new approximate algorithms to ensure involved agents receive their fair share when distributing goods or assigning tasks.
Sample articles
Guaranteeing Maximin Shares: Some Agents Left Behind
[arXiv / conference paper]Ordinal Maximin Share Approximation for Goods
[arXiv ]Ordinal Maximin Share Approximation for Chores[arXiv / conference paper]
Transparent & Fair Allocation of Resources/Tasks
Synopsis: How should the resources and tasks be distributed among interested parties fairly while maximizing transparency?
Sample articles
Fair Division through Information Withholding
[arXiv / conference paper]Fair and Efficient Allocations under Lexicographic Preferences
[arXiv / conference paper]
Surprisingly Popular Voting
Synopsis: How can we use the wisdom of the crowd to recover the ground truth when the majority is wrong?
Sample articles
Surprisingly Popular Voting Recovers Rankings, Surprisingly!
[arXiv / conference paper]
Sponsors
