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To Impress an Algorithm: Minoritized Applicants’ Perceptions of Fairness in AI Hiring Systems

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Wisdom, Well-Being, Win-Win (iConference 2024)

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Abstract

Technology firms increasingly leverage artificial intelligence (AI) to enhance human decision-making processes in the rapidly evolving talent acquisition landscape. However, the ramifications of these advancements on workforce diversity remain a topic of intense debate. Drawing upon Gilliland’s procedural justice framework, we explore how IT job candidates interpret the fairness of AI-driven recruitment systems. Gilliland’s model posits that an organization’s adherence to specific fairness principles, such as honesty and the opportunity to perform, profoundly shapes candidates’ self-perceptions, their judgments of the recruitment system’s equity, and the overall attractiveness of the organization. Using focus groups and interviews, we interacted with 47 women, Black and Latinx or Hispanic undergraduates specializing in computer and information science to discern how gender, race, and ethnicity influence attitudes toward AI in hiring. Three procedural justice rules, consistency of administration, job-relatedness, and selection information, emerged as critical in shaping participants’ fairness perceptions. Although discussed less frequently, the propriety of questions held significant resonance for Black and Latinx or Hispanic participants. Our study underscores the critical role of fairness evaluations for organizations, especially those striving to diversify the tech workforce.

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Acknowledgment

This material is based upon work supported by the National Science Foundation under Grant Number 1841368. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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  1. Pennsylvania State University, University Park, PA, 16802, USA

    Antonio E. Girona & Lynette Yarger

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  1. Antonio E. Girona
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  1. iSchool organization, Berlin, Germany

    Isaac Sserwanga

  2. University of Tsukuba, Tsukuba, Japan

    Hideo Joho

  3. Jilin University, Changchun, China

    Jie Ma

  4. Stockholm University, Kista, Sweden

    Preben Hansen

  5. Wuhan University, Wuhan, China

    Dan Wu

  6. University of Tsukuba, Tsukuba, Japan

    Masanori Koizumi

  7. University of California, Los Angeles, CA, USA

    Anne J. Gilliland

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Girona, A.E., Yarger, L. (2024). To Impress an Algorithm: Minoritized Applicants’ Perceptions of Fairness in AI Hiring Systems. In: Sserwanga, I., et al. Wisdom, Well-Being, Win-Win. iConference 2024. Lecture Notes in Computer Science, vol 14597. Springer, Cham. https://doi.org/10.1007/978-3-031-57860-1_4

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