1 citations · 1 across the 3 of their papers we have counts for
6 papers
Mind the Gaps: Auditing and Reducing Group Inequity in Large-Scale Mobility Prediction
Ashwin Kumar, Hanyu Zhang, David A. Schweidel +1
Next location prediction underpins a growing number of mobility, retail, and public-health applications, yet its societal impacts remain largely unexplored. In this paper, we audit…
A General Incentives-Based Framework for Fairness in Multi-agent Resource Allocation
Ashwin Kumar, William Yeoh
We introduce the General Incentives-based Framework for Fairness (GIFF), a novel approach for fair multi-agent resource allocation that infers fair decision-making from standard va…
FairVizARD: A Visualization System for Assessing Multi-Party Fairness of Ride-Sharing Matching Algorithms
Ashwin Kumar, Sanket Shah, Meghna Lowalekar +3
There is growing interest in algorithms that match passengers with drivers in ride-sharing problems and their fairness for the different parties involved (passengers, drivers, and…
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini, Omri Abend, Dina Acklin +105
This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2…
Past-Discounting is Key for Learning Markovian Fairness with Long Horizons
Ashwin Kumar, William Yeoh
Fairness is an important consideration for dynamic resource allocation in multi-agent systems. Many existing methods treat fairness as a one-shot problem without considering tempor…
DECAF: Learning to be Fair in Multi-agent Resource Allocation
Ashwin Kumar, William Yeoh
A wide variety of resource allocation problems operate under resource constraints that are managed by a central arbitrator, with agents who evaluate and communicate preferences ove…