Satisfying Complex Top-kFairness Constraints by Preference Substitutions

Md Mouinul Islam, Dong Wei, Baruch Schieber, Senjuti Basu Roy

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Given m users (voters), where each user casts her preference for a single item (candidate) over n items (candidates) as a ballot, the preference aggregation problem returns k items (candidates) that have the k highest number of preferences (votes). Our work studies this problem considering complex fairness constraints that have to be satisfied via proportionate representations of different values of the group protected attribute(s) in the top-k results. Precisely, we study the margin finding problem under single ballot substitutions, where a single substitution amounts to removing a vote from candidate i and assigning it to candidate j and the goal is to minimize the number of single ballot substitutions needed to guarantee that the top-k results satisfy the fairness constraints. We study several variants of this problem considering how top-k fairness constraints are defined, (i) MFBinaryS and MFMultiS are defined when the fairness (proportionate representation) is defined over a single, binary or multivalued, protected attribute, respectively; (ii) MFMulti2 is studied when top-k fairness is defined over two different protected attributes; (iii) MFMulti3+ investigates the margin finding problem, considering 3 or more protected attributes. We study these problems theoretically, and present a suite of algorithms with provable guarantees. We conduct rigorous large scale experiments involving multiple real world datasets by appropriately adapting multiple state-of-the-art solutions to demonstrate the effectiveness and scalability of our proposed methods.

Original languageEnglish (US)
Pages (from-to)317-329
Number of pages13
JournalProceedings of the VLDB Endowment
Volume16
Issue number2
DOIs
StatePublished - 2022
Event49th International Conference on Very Large Data Bases, VLDB 2023 - Vancouver, Canada
Duration: Aug 28 2023Sep 1 2023

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • General Computer Science

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