Equity Promotion in Online Resource Allocation

Pan Xu, Yifan Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


We consider online resource allocation under a typical nonprofit setting, where limited or even scarce resources are administered by a not-for-profit organization like a government. We focus on the internal-equity by assuming that arriving requesters are homogeneous in terms of their external factors like demands but heterogeneous for their internal attributes like demographics. Specifically, we associate each arriving requester with one or several groups based on their demographics (i.e., race, gender, and age), and we aim to design an equitable distributing strategy such that every group of requesters can receive a fair share of resources proportional to a preset target ratio. We present two LP-based sampling algorithms and investigate them both theoretically (in terms of competitive-ratio analysis) and experimentally based on real COVID-19 vaccination data maintained by the Minnesota Department of Health. Both theoretical and numerical results show that our LP-based sampling strategies can effectively promote equity, especially when the arrival population is disproportionately represented, as observed in the early stage of the COVID-19 vaccine rollout.

Original languageEnglish (US)
Title of host publicationAAAI-22 Technical Tracks 9
PublisherAssociation for the Advancement of Artificial Intelligence
Number of pages9
ISBN (Electronic)1577358767, 9781577358763
StatePublished - Jun 30 2022
Externally publishedYes
Event36th AAAI Conference on Artificial Intelligence, AAAI 2022 - Virtual, Online
Duration: Feb 22 2022Mar 1 2022

Publication series

NameProceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022


Conference36th AAAI Conference on Artificial Intelligence, AAAI 2022
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


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