How fair can we go: Detecting the boundaries of fairness optimization in information retrieval

Ruoyuan Gao, Chirag Shah

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

10 Scopus citations

Abstract

The presence of bias in today's IR systems has raised concerns on the social responsibilities of IR. Fairness has become an increasingly important factor when building systems for information searching and content recommendations. Fairness in IR is often considered as an optimization problem where the system aims to optimize the utility, subject to a set of fairness constraints, or optimize fairness while guaranteeing a lower bound on the utility, or jointly optimize for both utility and fairness to achieve an overall satisfaction. While various optimization algorithms have been proposed along with theoretical analysis, in real world applications, the performance of diferent optimization algorithms often heavily depend on the data. Therefore, it is consequential to ask what is the solution space characterized by the data, what effect does introducing fairness bring to the system, and can we identify this solution space to help us trade-off different optimization policies and guide us to pick suitable algorithms and/or make adjustments on data? In this work, we propose a framework that offers a novel perspective into the optimization with fairness constraints problems. Our framework can efectively and efficiently estimate the solution space and answer such questions. It also has the advantage of simplicity, explainability, and reliability. Specifically, we derive theoretical expressions to identify the fairness and relevance bounds for data of different distributions, and apply them to both synthetic and real world datasets. We present a series of use cases to demonstrate how our framework is applied to facilitate various analyses and decision making.

Original languageEnglish (US)
Title of host publicationICTIR 2019 - Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages229-236
Number of pages8
ISBN (Electronic)9781450368810
DOIs
StatePublished - Sep 23 2019
Event9th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2019 - Santa Clara, United States
Duration: Oct 2 2019Oct 5 2019

Publication series

NameICTIR 2019 - Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval

Conference

Conference9th ACM SIGIR International Conference on the Theory of Information Retrieval, ICTIR 2019
Country/TerritoryUnited States
CitySanta Clara
Period10/2/1910/5/19

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Information Systems

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