Algorithmic Leadership Without Dehumanization: A Human-Centered Model for Digital Work
DOI:
10.47353/jmd.v1i1.376Published:
2026-04-11Downloads
Abstract
The rapid integration of algorithmic systems and artificial intelligence into organizational management has given rise to a new paradigm of leadership often described as algorithmic leadership. While such systems enhance efficiency, scalability, and data-driven decision-making, they also raise critical concerns regarding dehumanization, loss of autonomy, and erosion of employee well-being. This study aims to develop a human-centered model of algorithmic leadership that balances technological capabilities with fundamental human values in digital work environments. Using a descriptive qualitative approach based on an integrative literature review, this research synthesizes insights from leadership theory, human–computer interaction, organizational behavior, and AI ethics. The analysis identifies three core dimensions essential for human-centered algorithmic leadership: augmented decision-making, human dignity preservation, and relational transparency. These dimensions emphasize the need to design algorithmic systems that support rather than replace human judgment, maintain employee agency, and foster trust through explainability and accountability. The study proposes a multi-layered leadership model that integrates strategic intent, operational practices, and technological design. It highlights key tensions between efficiency and empathy, automation and autonomy, and control and empowerment. The findings suggest that effective algorithmic leadership requires not only technical sophistication but also ethical awareness and organizational redesign. This research contributes to the emerging discourse on digital leadership by introducing a framework that mitigates the risks of dehumanization while leveraging the benefits of algorithmic systems. It offers practical implications for leaders and organizations seeking to implement AI-driven management systems responsibly. Ultimately, human-centered algorithmic leadership is essential for ensuring sustainable, ethical, and inclusive digital work environments.
Keywords:
Algorithmic leadership human-centered AI digital work organizational behaviorReferences
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922
Deci, E. L., & Ryan, R. M. (2000). The “what” and “why” of goal pursuits: Human needs and self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Floridi, L., Cowls, J., Beltrametti, M., et al. (2018). AI4People—An ethical framework for a good AI society. Minds and Machines, 28, 689–707. https://doi.org/10.1007/s11023-018-9482-5
Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research. MIS Quarterly, 37(2), 337–355. https://doi.org/10.25300/MISQ/2013/37.2.01
Haslam, N. (2006). Dehumanization: An integrative review. Personality and Social Psychology Review, 10(3), 252–264. https://doi.org/10.1207/s15327957pspr1003_4
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10, 18–26. https://doi.org/10.1007/s13162-020-00161-0
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
Lee, M. K., Kusbit, D., Metsky, E., & Dabbish, L. (2015). Working with machines: The impact of algorithmic management. Proceedings of CHI, 1603–1612. https://doi.org/10.1145/2702123.2702548
Liao, Q. V., Gruen, D., & Miller, S. (2020). Questioning the AI: Informing design practices for explainable AI. Proceedings of CHI, 1–15. https://doi.org/10.1145/3313831.3376590
Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence, 267, 1–38. https://doi.org/10.1016/j.artint.2018.07.007
Möhlmann, M., & Zalmanson, L. (2017). Hands on the wheel: Navigating algorithmic management. Proceedings of ICIS.
Newell, S., & Marabelli, M. (2015). Strategic opportunities (and challenges) of algorithmic decision-making. MIS Quarterly Executive, 14(4), 155–169.
OECD. (2021). OECD principles on artificial intelligence. https://doi.org/10.1787/ead7c7d9-en
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
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118
Simon, H. A. (1997). Administrative behavior (4th ed.). Free Press.
Susskind, R., & Susskind, D. (2015). The future of the professions. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780198713395.001.0001
Venkatesh, V., Bala, H., & Sykes, T. A. (2022). Impacts of artificial intelligence on organizations. Journal of Information Technology, 37(3), 1–16. https://doi.org/10.1177/02683962211063359
Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114–123.
Zuboff, S. (2019). The age of surveillance capitalism. PublicAffairs.
License
Copyright (c) 2026 Ika Fitriyani, Muhammad Nur Fietroh

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
