5 papers
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…
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…
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…