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The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity

Author

Listed:
  • Ulrich Leicht-Deobald

    (INSEAD
    University of St. Gallen)

  • Thorsten Busch

    (University of St. Gallen)

  • Christoph Schank

    (University of St. Gallen
    University of Vechta)

  • Antoinette Weibel

    (University of St. Gallen)

  • Simon Schafheitle

    (University of St. Gallen)

  • Isabelle Wildhaber

    (University of St. Gallen)

  • Gabriel Kasper

    (University of St. Gallen)

Abstract

Organizations increasingly rely on algorithm-based HR decision-making to monitor their employees. This trend is reinforced by the technology industry claiming that its decision-making tools are efficient and objective, downplaying their potential biases. In our manuscript, we identify an important challenge arising from the efficiency-driven logic of algorithm-based HR decision-making, namely that it may shift the delicate balance between employees’ personal integrity and compliance more in the direction of compliance. We suggest that critical data literacy, ethical awareness, the use of participatory design methods, and private regulatory regimes within civil society can help overcome these challenges. Our paper contributes to literature on workplace monitoring, critical data studies, personal integrity, and literature at the intersection between HR management and corporate responsibility.

Suggested Citation

  • Ulrich Leicht-Deobald & Thorsten Busch & Christoph Schank & Antoinette Weibel & Simon Schafheitle & Isabelle Wildhaber & Gabriel Kasper, 2019. "The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity," Journal of Business Ethics, Springer, vol. 160(2), pages 377-392, December.
    • Handle: RePEc:kap:jbuset:v:160:y:2019:i:2:d:10.1007_s10551-019-04204-w
    DOI: 10.1007/s10551-019-04204-w
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    Cited by:

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    2. 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.
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    7. Florian Pethig & Julia Kroenung, 2023. "Biased Humans, (Un)Biased Algorithms?," Journal of Business Ethics, Springer, vol. 183(3), pages 637-652, March.
    8. Evgenia I. Lysova & Jennifer Tosti-Kharas & Christopher Michaelson & Luke Fletcher & Catherine Bailey & Peter McGhee, 2023. "Ethics and the Future of Meaningful Work: Introduction to the Special Issue," Journal of Business Ethics, Springer, vol. 185(4), pages 713-723, July.
    9. Mahmoud Abdulhadi Alabdali & Sami A. Khan & Muhammad Zafar Yaqub & Mohammed Awad Alshahrani, 2024. "Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis," Sustainability, MDPI, vol. 16(11), pages 1-30, June.
    10. Prikshat, Verma & Islam, Mohammad & Patel, Parth & Malik, Ashish & Budhwar, Pawan & Gupta, Suraksha, 2023. "AI-Augmented HRM: Literature review and a proposed multilevel framework for future research," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    11. 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.
    12. Kirsten Martin & Ari Waldman, 2023. "Are Algorithmic Decisions Legitimate? The Effect of Process and Outcomes on Perceptions of Legitimacy of AI Decisions," Journal of Business Ethics, Springer, vol. 183(3), pages 653-670, March.
    13. Alina Köchling & Shirin Riazy & Marius Claus Wehner & Katharina Simbeck, 2021. "Highly Accurate, But Still Discriminatory," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 39-54, February.
    14. Jake B. Telkamp & Marc H. Anderson, 2022. "The Implications of Diverse Human Moral Foundations for Assessing the Ethicality of Artificial Intelligence," Journal of Business Ethics, Springer, vol. 178(4), pages 961-976, July.
    15. Maude Lavanchy & Patrick Reichert & Jayanth Narayanan & Krishna Savani, 2023. "Applicants’ Fairness Perceptions of Algorithm-Driven Hiring Procedures," Journal of Business Ethics, Springer, vol. 188(1), pages 125-150, November.
    16. Sarah Bankins & Paul Formosa, 2023. "The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work," Journal of Business Ethics, Springer, vol. 185(4), pages 725-740, July.
    17. Trocin, Cristina & Hovland, Ingrid Våge & Mikalef, Patrick & Dremel, Christian, 2021. "How Artificial Intelligence affords digital innovation: A cross-case analysis of Scandinavian companies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    18. Lukas Lanz & Roman Briker & Fabiola H. Gerpott, 2024. "Employees Adhere More to Unethical Instructions from Human Than AI Supervisors: Complementing Experimental Evidence with Machine Learning," Journal of Business Ethics, Springer, vol. 189(3), pages 625-646, January.
    19. Jean-Philippe Deranty & Thomas Corbin, 2022. "Artificial Intelligence and work: a critical review of recent research from the social sciences," Papers 2204.00419, arXiv.org.
    20. Deriu, Valerio & Pozharliev, Rumen & De Angelis, Matteo, 2024. "How trust and attachment styles jointly shape job candidates’ AI receptivity," Journal of Business Research, Elsevier, vol. 179(C).
    21. Lechardoy, Lucie & López Forés, Laura & Codagnone, Cristiano, 2023. "Artificial intelligence at the workplace and the impacts on work organisation, working conditions and ethics," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done? 277997, International Telecommunications Society (ITS).

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