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Link to original content: https://doi.org/10.1007/978-3-031-09316-6_12
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Keyword Recommendation for Fair Search

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Advances in Bias and Fairness in Information Retrieval (BIAS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1610))

Abstract

Online search engines are an extremely popular tool for seeking information. However, the results returned sometimes exhibit undesirable or even wrongful forms of bias, such as with respect to gender or race. In this paper, we consider the problem of fair keyword recommendation, in which the goal is to suggest keywords that are relevant to a user’s search query, but exhibit less (or opposite) bias. We present a multi-objective optimization method that uses word embeddings to suggest alternate keywords for biased keywords present in a search query. We perform a qualitative analysis on pairs of subReddits from Reddit.com (r/Republican vs. r/democrats). Our results demonstrate the efficacy of the proposed method and illustrate subtle linguistic differences between subReddits.

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Notes

  1. 1.

    https://github.com/harshdsdh/fairKR.

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Acknowledgements

S. Soundarajan is supported by NSF #2047224.

Author information

Authors and Affiliations

  1. Syracuse University, Syracuse, NY, 13244, USA

    Harshit Mishra & Sucheta Soundarajan

Authors
  1. Harshit Mishra
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  2. Sucheta Soundarajan
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Corresponding author

Correspondence to Harshit Mishra .

Editor information

Editors and Affiliations

  1. University of Cagliari, Cagliari, Italy

    Ludovico Boratto

  2. Sapienza University of Rome, Rome, Italy

    Stefano Faralli

  3. University of Cagliari, Cagliari, Italy

    Mirko Marras

  4. University of L’Aquila, L’Aquila, Italy

    Giovanni Stilo

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Mishra, H., Soundarajan, S. (2022). Keyword Recommendation for Fair Search. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Advances in Bias and Fairness in Information Retrieval. BIAS 2022. Communications in Computer and Information Science, vol 1610. Springer, Cham. https://doi.org/10.1007/978-3-031-09316-6_12

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  • DOI: https://doi.org/10.1007/978-3-031-09316-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-09315-9

  • Online ISBN: 978-3-031-09316-6

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