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Bootless Application of Greedy Re-ranking Algorithms in Fair Neural Team Formation

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Advances in Bias and Fairness in Information Retrieval (BIAS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1840))

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Abstract

Team formation aims to automate forming teams of experts who can successfully solve difficult tasks, which have firsthand effects on creating organizational performance. While existing neural team formation methods are able to efficiently analyze massive collections of experts to form effective collaborative teams, they largely ignore the fairness in recommended teams of experts. Fairness breeds innovation and increases teams’ success by enabling a stronger sense of community, reducing conflict, and stimulating more creative thinking. In this paper, we study the application of state-of-the-art deterministic greedy re-ranking algorithms to mitigate the potential popularity bias in the neural team formation models based on demographic parity. Our experiments show that, first, neural team formation models are biased toward popular experts. Second, although deterministic re-ranking algorithms mitigate popularity bias substantially, they severely hurt the efficacy of teams. The code to reproduce the experiments reported in this paper is available at https://github.com/fani-lab/Adila/tree/bias23 (

, a feminine Arabic given name meaning just and fair.)

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Notes

  1. 1.

    business.linkedin.com/talent-solutions.

  2. 2.

    imdb.com/interfaces/.

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Authors and Affiliations

  1. University of Windsor, Windsor, Canada

    Hamed Loghmani & Hossein Fani

Authors
  1. Hamed Loghmani
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  2. Hossein Fani
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Correspondence to Hossein Fani .

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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, L'Aquila, Italy

    Giovanni Stilo

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Loghmani, H., Fani, H. (2023). Bootless Application of Greedy Re-ranking Algorithms in Fair Neural Team Formation. In: Boratto, L., Faralli, S., Marras, M., Stilo, G. (eds) Advances in Bias and Fairness in Information Retrieval. BIAS 2023. Communications in Computer and Information Science, vol 1840. Springer, Cham. https://doi.org/10.1007/978-3-031-37249-0_9

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  • DOI: https://doi.org/10.1007/978-3-031-37249-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37248-3

  • Online ISBN: 978-3-031-37249-0

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