Designing a Responsible Algorithmic Governance Framework for Iranian Public Sector Organizations

Document Type : Original Article

Authors

1 Management Faculty, Payame Noor University

2 Management Faculty, Payame Noor University, Tehran, Iran.

Abstract

Context and Purpose:

The expansion of the use of artificial intelligence and algorithmic systems in government organizations, in addition to increasing the efficiency and speed of public services, has brought consequences such as reduced transparency, data bias, weakening accountability, and impairing citizen rights. The lack of a native model for responsible algorithmic governance in the public sector in Iran has necessitated the present study. The main objective is to design a responsible algorithmic governance model in Iranian government organizations.

Methodology: This research is applied in terms of purpose and qualitative in terms of method and is based on data-driven theory with the systematic approach of Strauss and Corbin. Data were collected through semi-structured interviews with 17 experts in public administration, digital governance, and artificial intelligence. Sampling was purposeful and theoretical, and data analysis was carried out in three stages: open, axial, and selective coding.

Findings: The paradigmatic model of the research shows that the realization of responsible algorithmic governance in Iranian government organizations includes eight main categories: institutional and governance requirements, data and technology infrastructure, management competencies, legal and ethical dimensions, transparency and accountability, organizational culture and resilience, public trust, and consequences of algorithmic governance. The central category was identified as “institutionalization of responsible algorithmic governance in the government administrative system.”

Conclusion: Establishing responsible algorithmic governance requires a combination of institutional capacity, data governance, AI regulation, and algorithmic accountability. The innovation of the article is to present a native and integrated model for creating transparent, fair, and accountable smart systems in Iranian government organizations.

Keywords

References

منابع
Akbari, P. (2026). Designing Conceptual Bricolage Paradigm of Entrepreneurship in Human Resource. Journal of Comparative Public Administration, 3 (4), 252-284. Doi:10.22098/cpa.2025.18438.1118 . [in Persian]
Administrative Supreme Council. (2025). Smart Government Roadmap, Prepared by the Deputy for E-Governance and Smart Government, Administrative and Recruitment Affairs Organization of Iran, with the cooperation of the Plan and Budget Organization and the Ministry of Information and Communications Technology) [In Persian]. Tehran: Administrative and Recruitment Affairs Organization of Iran.
Amighi Roudsari, M. & Maslahatjoo, A. D. (2025). Lincoln and Guba scientific quality assessment criteria usage in determining of qualitative research validity and reliability, Applied Scientometric Studies, 2(3), 25 - 45. https://doi.org/10.22091/apss.2026.14854.1079. [in Persian]
Ansari, B. & Mahdanian, S. (2025). Artificial intelligence and the transformation of political subjectivity: In search of a solution. Theoretical Politics Research. 37: 223-252. https://political.ihss.ac.ir/Article/50397. [in Persian]
Bahrami, M., & Sabet Sayyar, H. (2024). Artificial intelligence in public security. Quarterly Journal of Internal Security Studies and Research, 2(5), 31–53. [In Persian].
Chen, Z., (2026). Algorithmic human resource management as a mode of algorithmic governance: transparency, fairness, and human agency in the digital workplace. Humanities and social science communications. https://doi.org/10.1057/s41599-026-06989-4
 
Danaeifard, H., and Islami, A., (2011), Making the Theory of Organizational Indifference: Applying the Research Strategy of Data Foundation Theory in Practice, Tehran, Imam Sadegh University, First Edition. [in Persian]
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., ... Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Article 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Floridi, L., Cowls, J., Beltrametti, M., et al. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. DOI: https://doi.org/10.1007/s11023-018-9482-5
Ghazizadeh Fard, S. Z., Naseri Bavaghar, D., Sanjaghi, M. E., & Amirkabiri, A. (2025). Designing a strategic digital transformation model in public sector organizations: A systematic review. Intelligent Learning and Management Transformation, 3(1), 1–20. [In Persian].
Gritsenko, D., & Wood, M. (2020). Algorithmic governance: A modes of governance approach. Regulation & Governance, 16(1), 45–62. https://doi.org/10.1111/rego.12367
Hillo, J., Vento, I., Erkkia, T. (2025). Algorithmic Governance: Experimental Evidence on Citizens' and Public Administrators' Legitimacy Perceptions of Automated Decision-Making. Public Administration.  https://doi.org/10.1111/padm.70028.
Karimi, M.; Danaeefard, H., & Kazemi, S. H. (2025). Exploring the Challenges of Digital Transformation in Iran's Public Sector: The Structural Interpretive Modeling Approach. Transformation Management Journal, 16(2): 1-31. [in Persian].
https://doi.org/10.22067/tmj.2024.81350.1405
Katzenbach, C. & Ulbricht, L. (2019). Algorithmic governance. Internet Policy Review, 8(4). DOI: 10.14763/2019.4.1424
Kitchin, R. (2017). Thinking critically about and researching algorithms. Information, Communication & Society, 20(1), 14–29. DOI: https://doi.org/10.1080/1369118X.2016.1154087
Langer, M., et al. (2021). What do we want from explainable Artificial Intelligence (XAI)? A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research. Artificial Intelligence 296(3). DOI: https://doi.org/10.1016/j.artint.2021.103473
Madan, R & Ashok, M (2023). AI adoption and diffusion in public administration: A systematic literature review and future research agenda, Government Information Quarterly, 40. https://doi.org/10.1016/j.giq.2022.101774
Margetts, H., & Dorobantu, C. (2019). Rethink government with AI. Nature, 568, 163–165. DOI: https://doi.org/10.1038/d41586-019-01099-5
Mehrabi, N., Morstatter, F., Saxena N., Lerman K, Galstyan, A (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys. 54(6), 1-35. DOI: https://doi.org/10.1145/3457607
Mergel, I., Edelmann, N., & Haug, N. (2022). Defining digital transformation in the public sector. Government Information Quarterly. DOI: https://doi.org/10.4337/j9781802202595
Ministry of Information and Communications Technology. (2026). Summary of the Performance and Activities of the Ministry of Information and Communications Technology in 1404. [In Persian]. Deputy for ICT Development Policy and Planning, Office of Monitoring and Evaluation of Plans and Projects.
Möhlmann, M., Gregory R., & Henfridsson. O., (2025) Algorithmic Stakeholder Governance on Content Platforms: A Lead Role Perspective, Journal of Management Information Systems, 42:3, 799-831, DOI: 10.1080/07421222.2025.2520177
Moradi-Berlian, M. & Tangestani, G.M. (2024). Algorithm and Administrative Law; Challenges and Solutions. The Journal in Modern Research in administrative law, Volume 6, Issue 19, 163-192 [in persian]
Moradi-Berlian, M & Tangestani, G.M. (2025) Transformation Platform of Era the in Order Public Governance Algorithmic in making-Policy of Study Comparative. Studies Strategic Quarterly, 28 (3). 43-79. Doi: ssq.2025.539441.42990 [in persian]
Nikiforova, A., et al. (2025). Responsible AI adoption in the public sector. arXiv / preprint. DOI: https://doi.org/10.48550/arXiv.2510.09634
OECD (2020-10-07), The OECD Digital Government Policy Framework: Six dimensions of a Digital Government, OECD Public Governance Policy Papers, No. 02, OECD Publishing, Paris. http://dx.doi.org/10.1787/f64fed2a-en
OECD. (2025). Governing with Artificial Intelligence The State of Play and Way Forward in Core Government Functions. (institutional report)
Peeters, R., & Schuilenburg, M. (2023). Algorithmic governance: Technology, knowledge and power. In W. Housley, A. Edwards, R. Beneito-Montagut, & R. Fitzgerald (Eds.), The SAGE handbook of digital society (pp. 439–457). SAGE Publications. https://doi.org/10.4135/9781529783193.n25
Robles, P. (2025). Advancing AI governance frameworks: A systematic review. Perspectives on Public Management and Governance. DOI: https://doi.org/10.1093/ppmgov/gvaf013
Strauss, Anselm; Corbin, Juliet (2006); Principles of Qualitative Research Methodology: Basic Theory, Procedures and Methods; Mohammadi, B.; Tehran: Institute of Humanities and Cultural Studies. [in Persian]
UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. Paris: UNESCO. DOI: N/A (UN policy framework)
Wang, C, Yin, Y., Hu, H. (2026).  The rise of algorithmic governance and the dual revolution: Applications, challenges, and governance of artificial intelligence in public administration. Technology in Society. https://doi.org/10.1016/j.techsoc.2026.103264
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. DOI: https://doi.org/10.1080/01900692.2018.1498103
Wirtz, B. W., Weyerer, J. C., & Sturm, B. J. (2020). The dark sides of artificial intelligence: An integrated AI governance framework for public administration. International Journal of Public Administration, 43(9), 818–829. https://doi.org/10.1080/01900692.2020.1749851
Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523. DOI: https://doi.org/10.1111/rego.12158
Yuan, Q., & Chen, T. (2025). Holding AI-based systems accountable in the public sector. Public Performance & Management Review. DOI: https://doi.org/10.1080/15309576.2025.2469784
Zuiderwijk, A., Chen, Y.-C., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3), Article 101577. https://doi.org/10.1016/j.giq.2021.101577.
Journal of Comparative Public Administration
Volume 4, Issue 2
September 2026
Pages 37-61

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History

  • Receive Date: 29 May 2026
  • Revise Date: 17 July 2026
  • Accept Date: 05 August 2026

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