Algorithmic Governance, Trust, and the Transformation of Social Structures in the Digital Age
DOI:
https://doi.org/10.47451/soc2025-10-01Keywords:
algorithmic governance, social structures, digital trust, SHAP analysis, delegation, artificial intelligence, structural power, machine learningAbstract
This article presents a sociological analysis of the transformation of trust and managerial authority in the context of algorithmic governance. The object of the study is the algorithmized decision-making environment, and the subject is the social mechanisms of responsibility delegation and changes in legitimation in the digital age. The study aims to identify the structural consequences of implementing algorithms in areas that previously relied on personalized evaluation, as well as to empirically model such consequences using psychometric data. The study employs an interdisciplinary methodology, incorporating Niklas Luhmann’s systems theory, Pierre Bourdieu’s concept of symbolic power, Anthony Giddens’ structuration theory, and machine learning tools, particularly Random Forest and SHAP analysis. The empirical illustration is based on an original synthetic dataset constructed using real psychometric scales. The findings reveal that algorithmic models not only organize data but also generate new structures of social influence through a logic of opaque decisions perceived as objective. SHAP analysis demonstrates that the importance of individual features in system predictions varies by context, offering prospects for critically interpreting the social functions of algorithms. The results can be applied in the analysis of trust in digital services and automated decision-making in healthcare, education, and security.
Downloads
References
Bourdieu, P. (2021). Forms of capital: General sociology (Vol. 3). Lectures at the Collège de France 1983–84. Polity.
Cao, G., Duan, Y., Edwards, J., & Dwivedi, Y. (2021). Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making. Technovation, 106(5), 102312.
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
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.
Kukhta, M. P., & Yenin, M. N. (2025). Digital strategies of psychoprophylaxis in the Ministry of Internal Affairs system [Цифрові стратегії психопрофілактики в системі МВС]. Habitus, 72, 203–208. (In Ukr.)
Latour, B. (2005). Reassembling the social: An introduction to the actor-network theory. Oxford University Press.
Luhmann, N. (2011). The concept of purpose and systemic rationality: On the function of goals in social systems (M. Boychenko & V. Kebuladze, Trans.). Dukh i Litera.
Perga, I. (2025). Ethical challenges of using AI in managerial decision-making: Lessons from the EU for Ukraine. Naukovi Perspektivy, 1(55), 21–36. (In Ukr.)
Raschka, S., & Mirjalili, V. (2019). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2 (3rd ed.). Packt Publishing.
Sayes, E. (2014). Actor-network theory and methodology: Just what does it mean to say that nonhumans have agency? Social Studies of Science, 44(1), 134–149.
Soldner, F., Rosenbusch, H., Evans, A. M., & Zeelenberg, M. (2019). Supervised machine learning methods in psychology: A practical introduction with annotated R code. Social and Personality Psychology Compass. https://doi.org/10.31234/osf.io/s72vu
Weber, M. (1998). Sociology: General historical analyses. Politics (O. Pohorylyi, Trans.). Osnovy.
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector — Applications and challenges. International Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103
Published
Issue
Section
License
Copyright (c) 2025 European Scientific e-Journal
This work is licensed under a Creative Commons Attribution 4.0 International License.
The European Scientific e-Journal (ESEJ) is an open access journal. Articles are available free of charge as PDF files on the website of the European Institute for Innovation Development. PDF files can be previewed with Acrobat Reader from www.adobe.com.
All articles of the “Tuculart Student Scientific” are published under a Creative Commons Attribution 4.0 Generic (CC BY 4.0) International license.
According to the Creative Commons Attribution 4.0 Generic (CC BY 4.0) International license, the users are free to Share — copy and redistribute the material in any medium or format for any purpose, even commercially (the licensor cannot revoke these freedoms as long as you follow the license terms).
Under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
