Ethical Considerations in Algorithm Construction: Evidence from Two Human-Resources Algorithms

Clotilde Coron; Simon Porcher

doi:10.37725/mgmt.2025.9830

Clotilde Coron RITM Research Centre in Economics & Management, Université Paris Saclay, Sceaux, France https://orcid.org/0000-0001-8431-336X
Simon Porcher Dauphine recherches en management (DRM), Université Paris Dauphine-PSL, Paris, France https://orcid.org/0000-0001-6614-0338

DOI: https://doi.org/10.37725/mgmt.2025.9830

Keywords: Algorithm; Ethics; Human resource management; Artificial intelligence

Abstract

Organizations increasingly rely on algorithms for decision-making, which raises significant ethical issues. In this paper, we provide a detailed case study of the development and deployment of two human resources (HR) algorithms in a major French digital company. Our findings show that these ethical issues reflect the ethical considerations of the various stakeholders involved in the process, including data scientists, HR practitioners, and legal experts. We discuss how these considerations intervene during the decision-making process in algorithm design and usage, offering insights for both academics and practitioners into how ethical issues are approached by different actors.

Downloads

Download data is not yet available.

Author Biographies

Clotilde Coron, RITM Research Centre in Economics & Management, Université Paris Saclay, Sceaux, France

Clotilde Coron is a full professor of management at Université Paris-Saclay (RITM Research Center in Economics & Management). Her research and publications deal with gender equality on the one hand, and the use of numbers in human resource management in the other hand.

Simon Porcher, Dauphine recherches en management (DRM), Université Paris Dauphine-PSL, Paris, France

Simon Porcher is a full professor of management at Université Paris Dauphine-PSL (Dauphine recherches en management). His research focuses on how public-private partnerships can address major societal challenges, particularly in the water sector.

References

Ananny, M. (2016). Toward an ethics of algorithms: Convening, observation, probability, and timeliness. Science, Technology, & Human Values, 41(1), 93–117. https://doi.org/10.1177/0162243915606523

Ananny, M. & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645

Binns, R. (2018). Algorithmic accountability and public reason. Philosophy & Technology, 31(4), 543–556. https://doi.org/10.1007/s13347-017-0263-5

Bucher, E. L., Schou, P. K., & Waldkirch, M. (2021). Pacifying the algorithm–Anticipatory compliance in the face of algorithmic management in the gig economy. Organization, 28(1), 44–67. https://doi.org/10.1177/1350508420961531

Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12. https://doi.org/10.1177/2053951715622512

Cheng, M. M. & Hackett, R. D. (2021). A critical review of algorithms in HRM: Definition, theory, and practice. Human Resource Management Review, 31(1), 100698. https://doi.org/10.1016/j.hrmr.2019.100698

Christin, A. (2017). Algorithms in practice: Comparing web journalism and criminal justice. Big Data & Society, 4(2), 205395171771885. https://doi.org/10.1177/2053951717718855

Christin, A. (2020). The ethnographer and the algorithm: Beyond the black box. Theory and Society, 49, 897–918. https://doi.org/10.1007/s11186-020-09411-3

Coron, C. (2022). Quantifying human resource management: A literature review. Personnel Review, 51(4), 1386–1409. https://doi.org/10.1108/PR-05-2020-0322

Donaldson, T. J. & Neesham, C. (2020). The problem of value alignment in business decision making: Humans vs. artificial intelligence. Academy of Management Proceedings, 2020(1), 14706. https://doi.org/10.5465/AMBPP.2020.14706abstract

Faraj, S., Pachidi, S. & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005

Garreau, L. (2020). Petit précis méthodologique. Le Libellio d’AEGIS, 16(2), 51–64.

Glaser, V. L., Pollock, N. & D’Adderio, L. (2021). The biography of an algorithm: Performing algorithmic technologies in organizations. Organization Theory, 2(2), 1–27. https://doi.org/10.1177/26317877211004609

Gregory, R. W., Henfridsson, O., Kaganer, E. & Kyriakou, H. (2021). The role of artificial intelligence and data network effects for creating user value. Academy of Management Review, 46(3), 534–551. https://doi.org/10.5465/amr.2019.0178

Hodder, M. (2009, April 14). Why Amazon didn’t just have a glitch. TechCrunch. Retrieved from http://techcrunch.com/2009/04/14/guest-post-why-amazon-didnt-just-have-a-glitch/

Hursthouse, R. (1999). On virtue ethics. Oxford University Press. https://doi.org/10.1093/0199247994.001.0001

Introna, L. D. (2016). Algorithms, Governance, and Governmentality: On Governing Academic Writing. Science, Technology, & Human Values, 41(1), 17–49. https://doi.org/10.1177/0162243915587360

Introna, L. D. (2016). Algorithms, governance, and governmentality: On governing academic writing. Science, Technology, & Human Values, 41(1), 17–49. https://doi.org/10.1177/0162243915587360

Irani, L. (2015). Difference and dependence among digital workers: The case of Amazon Mechanical Turk. South Atlantic Quarterly, 114(1), 225–234. https://doi.org/10.1215/00382876-2831665

Jago, A. S. (2019). Algorithms and authenticity. Academy of Management Discoveries, 5(1), 38–56. https://doi.org/10.5465/amd.2017.0002

Kellogg, K. C., Valentine, M. A. & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174

Kim, M. P., Reingold, O. & Rothblum, G. N. (2018). Fairness through computationally-bounded awareness. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, et al. (Eds.), Advances in neural information processing systems (Vol. 31, pp. 4842–4852). Curran Associates. Retrieved from https://papers.nips.cc/paper_files/paper/2018/file/c8dfece5cc68249206e4690fc4737a8d-Paper.pdf

Kraemer, F., Van Overveld, K. & Peterson, M. (2011). Is there an ethics of algorithms? Ethics and Information Technology, 13(3), 251–260. https://doi.org/10.1007/s10676-010-9233-7

Lange, A.-C., Lenglet, M. & Seyfert, R. (2019). On studying algorithms ethnographically: Making sense of objects of ignorance. Organization, 26(4), 598–617. https://doi.org/10.1177/135050841880823

Langer, M. & König, C. J. (2023). Introducing a multi-stakeholder perspective on opacity, transparency and strategies to reduce opacity in algorithm-based human resource management. Human Resource Management Review, 33(1), 100881. https://doi.org/10.1016/j.hrmr.2021.100881

Langley, P. & Simon, H. A. (1995). Applications of machine learning and rule induction. In J. Cohen (Ed.), Communications of the ACM (Vol. 38, no. 11, pp. 54–64). Association for Computing Machinery. https://doi.org/10.1145/219717.219768

Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A. et al. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392. https://doi.org/10.1007/s10551-019-04204-w

Lowrie, I. (2017). Algorithmic rationality: Epistemology and efficiency in the data sciences. Big Data & Society, 4(1), 205395171770092. https://doi.org/10.1177/2053951717700925

Marcus, G. E. (1995). Ethnography in/of the world system: The emergence of multi-sited ethnography. Annual Review of Anthropology, 24, 95–117. https://doi.org/10.1146/annurev.an.24.100195.000523

Martin, K. (2019a). Designing ethical algorithms. MIS Quarterly Executive, 18(2), 129–142. https://doi.org/10.17705/2msqe.00012

Martin, K. (2019b). Ethical implications and accountability of algorithms. Journal of Business Ethics, 160(4), 835–850. https://doi.org/10.1007/s10551-018-3921-3

Meijerink, J. & Bondarouk, T. (2023). The duality of algorithmic management: Toward a research agenda on HRM algorithms, autonomy and value creation. Human Resource Management Review, 33(1), 100876. https://doi.org/10.1016/j.hrmr.2021.100876

Merrill III, D. G. (2011). Allocation-oriented algorithm design with application to gpu computing. University of Virginia Press.

Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S. et al. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 2053951716679679. https://doi.org/10.1177/2053951716679679

Murray, A., Rhymer, J. & Sirmon, D. G. (2021). Humans and technology: Forms of conjoined agency in organizations. Academy of Management Review, 46(3), 552–571. https://doi.org/10.5465/amr.2019.0186

Neff, G. & Stark, D. C. (2003). Permanently beta: Responsive organization in the Internet era. In P. N. Howard & S. Jones (Eds.), Society online: The internet in context (pp. 173–188). Sage.

Neyland, D. (2015). On organizing algorithms. Theory, Culture & Society, 32(1), 119–132. https://doi.org/10.1177/0263276414530477

Orlikowski, W. & Scott, S. V. (2015). The algorithm and the crowd: Considering the materiality of service innovation. MIS Quarterly, 39(1), 201–216. https://doi.org/10.25300/MISQ/2015/39.1.09

Orlikowski, W. & Scott, S. V. (2016). Digital work: A research agenda. In B. Czarniawska (Ed.), A research agenda for management and organization studies (pp. 88–95). Edward Elgar. https://doi.org/10.4337/9781784717025.00014

Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.

Raisch, S. & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072

Roulet, T. J., Gill, M. J., Stenger, S. & Gill, D. J. (2017). Reconsidering the value of covert research: The role of ambiguous consent in participant observation. Organizational Research Methods, 20(3), 487–517. https://doi.org/10.1177/1094428117698745

Sandberg, J. (2005). How do we justify knowledge produced within interpretive approaches? Organizational Research Methods, 8(1), 41–68. https://doi.org/10.1177/1094428104272000

Schouten, J. W. & McAlexander, J. H. (1995). Subcultures of consumption: An ethnography of the new bikers. Journal of Consumer Research, 22(1), 43–61. https://doi.org/10.1086/209434

Seaver, N. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big Data & Society, 4(2), 205395171773810. https://doi.org/10.1177/2053951717738104

Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S. & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59–68), January 29-31, 2019, Atlanta, GA: ACM.

Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S. et al. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59–68). https://doi.org/10.1145/3287560.3287598

Starke, C., Baleis, J., Keller, B. & Marcinkowski, F. (2022). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Big Data & Society, 9(2), 20539517221115189. https://doi.org/10.1177/20539517221115189

Turner, F. (2009). Burning man at Google: A cultural infrastructure for new media production. New Media & Society, 11(1–2), 73–94. https://doi.org/10.1177/1461444808099575

Von Krogh, G. (2018). Artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries, 4(4), 404–409. https://doi.org/10.5465/amd.2018.0084

Wilson, H. J. & Daugherty, P. R. (2018, July–August). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review. Retrieved from https://hbr.org/2018/07/collaborative-intelligence-humans-and-ai-are-joining-forces

Yin, R. K. (1981a). The case study as a serious research strategy. Science Communication, 3(1), 97–114. https://doi.org/10.1177/107554708100300106

Yin, R. K. (1981b). The case study crisis: Some answers. Administrative Science Quarterly, 26(1), 58–65. https://doi.org/10.2307/2392599