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Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development

Author

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  • Alina Köchling

    (Heinrich-Heine-University Düsseldorf)

  • Marius Claus Wehner

    (Heinrich-Heine-University Düsseldorf)

Abstract

Algorithmic decision-making is becoming increasingly common as a new source of advice in HR recruitment and HR development. While firms implement algorithmic decision-making to save costs as well as increase efficiency and objectivity, algorithmic decision-making might also lead to the unfair treatment of certain groups of people, implicit discrimination, and perceived unfairness. Current knowledge about the threats of unfairness and (implicit) discrimination by algorithmic decision-making is mostly unexplored in the human resource management context. Our goal is to clarify the current state of research related to HR recruitment and HR development, identify research gaps, and provide crucial future research directions. Based on a systematic review of 36 journal articles from 2014 to 2020, we present some applications of algorithmic decision-making and evaluate the possible pitfalls in these two essential HR functions. In doing this, we inform researchers and practitioners, offer important theoretical and practical implications, and suggest fruitful avenues for future research.

Suggested Citation

  • Alina Köchling & Marius Claus Wehner, 2020. "Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 795-848, November.
    • Handle: RePEc:spr:busres:v:13:y:2020:i:3:d:10.1007_s40685-020-00134-w
    DOI: 10.1007/s40685-020-00134-w
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    3. Mallory Avery & Andreas Leibbrandt & Joseph Vecci, 2023. "Does Artificial Intelligence Help or Hurt Gender Diversity? Evidence from Two Field Experiments on Recruitment in Tech," Monash Economics Working Papers 2023-09, Monash University, Department of Economics.
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    5. Chenfeng Yan & Quan Chen & Xinyue Zhou & Xin Dai & Zhilin Yang, 2024. "When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company," Journal of Business Ethics, Springer, vol. 190(4), pages 841-859, April.
    6. Fumagalli, Elena & Rezaei, Sarah & Salomons, Anna, 2022. "OK computer: Worker perceptions of algorithmic recruitment," Research Policy, Elsevier, vol. 51(2).
    7. Weisman, Hannah & Wu, Chia-Huei & Yoshikawa, Katsuhiko & Lee, Hyun-Jung, 2022. "Antecedents of organizational identification: a review and agenda for future research," LSE Research Online Documents on Economics 117626, London School of Economics and Political Science, LSE Library.
    8. Zhang, Lixuan & Yencha, Christopher, 2022. "Examining perceptions towards hiring algorithms," Technology in Society, Elsevier, vol. 68(C).
    9. Suen, Hung-Yue & Hung, Kuo-En, 2024. "Revealing the influence of AI and its interfaces on job candidates' honest and deceptive impression management in asynchronous video interviews," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    10. Leyer, Michael & Schneider, Sabrina, 2021. "Decision augmentation and automation with artificial intelligence: Threat or opportunity for managers?," Business Horizons, Elsevier, vol. 64(5), pages 711-724.
    11. Jian Zhu & Bin Zhang & Hui Wang, 2024. "The double-edged sword effects of perceived algorithmic control on platform workers’ service performance," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-12, December.
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