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Cited by 348Ethics and Social Impacts of AIAI and HR TechnologiesLegal and Labor Studies

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

Open access
Leicht-Deobald, Ulrich, Busch, Thorsten, Schank, Christoph, Weibel, Antoinette, Schafheitle, Simon, Wildhaber, Isabelle, Kasper, Gabriel·2019·Journal of Business Ethics
DOI 10.1007/s10551-019-04204-wPMC PMC6868110
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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.

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Crossref · 10.1007/s10551-019-04204-w

Cites

148 of 148 references verified0 citation errors · 0 not checkable

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  • AI-driven algorithmic monitoring allows the collection and recording of a vast array of new information and metrics about workers.

    “AI-driven algorithmic monitoring thus allows to collect and record a vast array of new information and metrics, such as emotions (Chan, 2018;Pinheiro, Ramos, Donizete, Picanço, & De Oliveira, 2017), movements, sleep time, physical and health condition, social media activity, internet browser history (Angrave, Charlwood, Kirkpatrick, Lawrence, & Stuart, 2016;Leicht-Deobald et al., 2019)”

    From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Leicht-Deobald et al., 2019

    In “A Brief History of Algorithm-Based HR Decision-Making” (page unavailable), the paper describes monitoring location, health and fitness data, Facebook activity, and browser histories, supporting the claim that algorithmic tools can collect varied new information about workers. It does not identify sleep time as a monitored metric, so that item in the citing sentence is unsupported by our copy. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Predictive algorithms operate regression-based forecasting techniques that help managers predict which employees are likely to leave the organization, or to predict the future performance of a job candidate.

    “These so-called predictive algorithms operate regression-based forecasting techniques that, for example, help managers predict which employees are likely to leave the organization (and thus how to make decisions about retention), or to predict the future performance of a job candidate (and thus help hiring managers with selection decisions) (Cheng & Hackett, 2021;Leicht-Deobald et al., 2019).”

    From Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM (2021) · cites this paper as Leicht-Deobald et al., 2019

    In “A Brief History of Algorithm-Based HR Decision-Making” (page unavailable), the paper says predictive algorithms use advanced regression techniques and describes a hiring algorithm that scores how well applicants would fit a job. It does not describe using predictive algorithms to identify employees likely to leave or to guide retention decisions; its mention of turnover concerns a descriptive scorecard. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • IT developers and end-user employees, managers or independent contractors are not aware of the basis upon which computer-programmed steps are taken and which data are drawn on when HRM algorithms generate a selected type of output.

    “More generally, IT developers and therefore end-user employees, managers or independent contractors are not aware of the basis upon which computer-programmed steps are taken and which data are drawn on when HRM algorithms generate a selected type of output (Gal et al., 2020;Leicht-Deobald et al., 2019).”

    From Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM (2021) · cites this paper as Leicht-Deobald et al., 2019

    In “A Brief History of Algorithm-Based HR Decision-Making” (page unavailable), the paper says the complexity of prescriptive algorithms makes it virtually impossible for a human to understand exactly how they turn information into a decision. In “The Assumption of Algorithm’s Objectivity” (page unavailable), it says proprietary code makes it difficult for HR managers to examine the code or training data. These passages support limited transparency, but not the categorical claim that IT developers and all the listed end users are unaware of the steps and data used for HRM algorithm outputs. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Algorithms can assist Human Resources to filter job applicants, fire inadequately fast warehouse workers, and improve work morale through fine-grained people analytics.

    “For instance, algorithms can assist Human Resources (HR) to filter job applicants (Leicht-Deobald et al., 2019), fire inadequately fast warehouse workers, and improve work morale through fine-grained people analytics (Gal et al., 2020).”

    From Algorithmic management in a work context (2021) · cites this paper as Leicht-Deobald et al., 2019

  • Algorithmic HR systems can promote an efficiency-driven logic that changes the balance between employee personal integrity and organizational compliance.

    “they argue that algorithmic HR systems can promote an efficiencydriven logic that changes the balance between employee personal integrity and organizational compliance.”

    From Designing “Contestability” in Automated HR Decisions: Appeal Mechanisms, Evidence, and Outcomes (2025) · cites this paper as Leicht-Deobald et al. (2019)

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Cites

Flagged first, then by citations

3 of the 151 references couldn’t be matched to a known paper.

Research works printed but not linked: 14

  1. ACM. (2018). ACM code of ethics and professional conduct. https://www.acm.org/code-of-ethics. Accessed September 23, 2018.
  2. Angwin, J. (2016). Make algorithms accountable. The New York Times (Vol. 2018).
  3. Davenport TH Analytics 3.0 Harvard Business Review 2013 91 12 64 72
  4. IEEE Prioritizing human well-being in the age of artificial intelligence 2017 Piscataway, NJ IEEE-Standards Association
  5. Konrad, A. (2013). Meet orion, software that will save ups millions by improving drivers’ routes. Forbes (Vol. 2018).
  6. Morozov, E. (2013). The perils of perfection. The New York Times (Vol. 2018).
  7. Nijhuis, M. (2017). How to call B.S. on big data: A practical guide The New Yorker (Vol. 2018).
  8. O’Heigeartaigh, S. (2013). Would you hand over a moral decision to a machine? Why not? Moral outsourcing and artificial intelligence. In Practical ethics. Oxford: University of Oxford.
  9. Simonite, T. (2012). Microsoft’s workplace social network becomes emotionally aware. MIT Technology Review (Vol. 2018).
  10. Tarnoff, B. (2017). Silicon Valley siphons our data like oil. But the deepest drilling has just begun. The Guardian (Vol. 2018).
  11. Vara, V. (2015). Can Intel make silicon valley more diverse. The New Yorker (Vol. 2018).
  12. Watson, R. (2016). In silicon valley, young white males are stealing the future from everyone else. The Guardian (Vol. 2018).
  13. Why, M. (2018). 4 reasons why an automated hiring process will help your company. Select International, a psi business (Vol. 2018). Pittsburg, PA: Select International.
  14. Zax, D. (2013). Brown down: UPS drivers vs. The UPS algorithm. FastCompany (Vol. 2019).

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Authors
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G.
Affiliations
  • University of St.Gallen (CH)
  • University of Vechta (DE)
ORCID
Venue
Journal of Business Ethics
Publisher
Springer Science and Business Media LLC
Published
2019-06-07
Volume
160
Issue
2
Pages
377-392
ISSN
  • 0167-4544 (ISSN-L)
  • 1573-0697
DOI
10.1007/s10551-019-04204-w
PMC
PMC6868110
OpenAlex
W2948100573
Cited by
347
References
151
Citations by year
2026: 82 · 2025: 73 · 2024: 50 · 2023: 43 · 2022: 53 · 2021: 34 · 2020: 11 · 2019: 1
FWCI
43.30
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Business Ethics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
  • KOPS (University of Konstanz) (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
  • Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) (submittedVersion, cc-by-nc-nd)
Funders
Swiss National Science Foundation (407540_167208)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
Keywords
algorithmic decision-making, human resource management, personal integrity, workplace monitoring, employee compliance, algorithmic bias, critical data literacy, moral consciousness, participatory design, corporate social responsibility, critical data studies

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