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A Study of Pre-processing Fairness Intervention Methods for Ranking People

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Advances in Information Retrieval (ECIR 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14611))

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Abstract

Fairness interventions are hard to use in practice when ranking people due to legal constraints that limit access to sensitive information. Pre-processing fairness interventions, however, can be used in practice to create more fair training data that encourage the model to generate fair predictions without having access to sensitive information during inference. Little is known about the performance of pre-processing fairness interventions in a recruitment setting. To simulate a real scenario, we train a ranking model on pre-processed representations, while access to sensitive information is limited during inference. We evaluate pre-processing fairness intervention methods in terms of individual fairness and group fairness. On two real-world datasets, the pre-processing methods are found to improve the diversity of rankings with respect to gender, while individual fairness is not affected. Moreover, we discuss advantages and disadvantages of using pre-processing fairness interventions in practice for ranking people.

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Notes

  1. 1.

    https://www.bls.gov/soc/.

  2. 2.

    https://www.xing.com/.

  3. 3.

    https://github.com/MilkaLichtblau/xing_dataset/.

  4. 4.

    https://github.com/ClaraRus/A-Study-of-Pre-processing-Fairness-Intervention-Methods-for-Ranking-People.

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Acknowledgments

We thank our reviewers for valuable feedback. This research was supported by the FINDHR (Fairness and Intersectional Non-Discrimination in Human Recommendation) project that received funding from the European Union’s Horizon Europe research and innovation program under grant agreement No 101070212, the Hybrid Intelligence Center, a 10-year program funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research, https://hybrid-intelligence-centre.nl, and project LESSEN with project number NWA.1389.20.183 of the research program NWA ORC 2020/21, which is (partly) financed by the Dutch Research Council (NWO).

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Authors and Affiliations

  1. University of Amsterdam, Amsterdam, The Netherlands

    Clara Rus, Andrew Yates & Maarten de Rijke

Authors
  1. Clara Rus
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  2. Andrew Yates
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  3. Maarten de Rijke
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Corresponding author

Correspondence to Clara Rus .

Editor information

Editors and Affiliations

  1. Georgetown University, Washington, WA, USA

    Nazli Goharian

  2. University of Pisa, PISA, Pisa, Italy

    Nicola Tonellotto

  3. King's College London, London, UK

    Yulan He

  4. University College London, London, UK

    Aldo Lipani

  5. University of Glasgow, Glasgow, UK

    Graham McDonald

  6. University of Glasgow, Glasgow, UK

    Craig Macdonald

  7. University of Glasgow, Glasgow, UK

    Iadh Ounis

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Rus, C., Yates, A., de Rijke, M. (2024). A Study of Pre-processing Fairness Intervention Methods for Ranking People. In: Goharian, N., et al. Advances in Information Retrieval. ECIR 2024. Lecture Notes in Computer Science, vol 14611. Springer, Cham. https://doi.org/10.1007/978-3-031-56066-8_26

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  • DOI: https://doi.org/10.1007/978-3-031-56066-8_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56065-1

  • Online ISBN: 978-3-031-56066-8

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