Rawlsian Fairness in Online Bipartite Matching: Two-Sided, Group, and Individual


  • Seyed Esmaeili University of Maryland, College Park
  • Sharmila Duppala University of Maryland, College Park
  • Davidson Cheng Colorado College
  • Vedant Nanda University of Maryland, College Park
  • Aravind Srinivasan University of Maryland College Park
  • John P. Dickerson University of Maryland




GTEP: Auctions and Market-Based Systems, ML: Bias and Fairness, MAS: Applications


Online bipartite-matching platforms are ubiquitous and find applications in important areas such as crowdsourcing and ridesharing. In the most general form, the platform consists of three entities: two sides to be matched and a platform operator that decides the matching. The design of algorithms for such platforms has traditionally focused on the operator’s (expected) profit. Since fairness has become an important consideration that was ignored in the existing algorithms a collection of online matching algorithms have been developed that give a fair treatment guarantee for one side of the market at the expense of a drop in the operator’s profit. In this paper, we generalize the existing work to offer fair treatment guarantees to both sides of the market simultaneously, at a calculated worst case drop to operator profit. We consider group and individual Rawlsian fairness criteria. Moreover, our algorithms have theoretical guarantees and have adjustable parameters that can be tuned as desired to balance the trade-off between the utilities of the three sides. We also derive hardness results that give clear upper bounds over the performance of any algorithm.




How to Cite

Esmaeili, S., Duppala, S., Cheng, D., Nanda, V., Srinivasan, A., & Dickerson, J. P. (2023). Rawlsian Fairness in Online Bipartite Matching: Two-Sided, Group, and Individual. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 5624-5632. https://doi.org/10.1609/aaai.v37i5.25698



AAAI Technical Track on Game Theory and Economic Paradigms