The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Defining organizational AI governance
Open accessAbstract
Artificial intelligence (AI) governance is required to reap the benefits and manage the risks brought by AI systems. This means that ethical principles, such as fairness, need to be translated into practicable AI governance processes. A concise AI governance definition would allow researchers and practitioners to identify the constituent parts of the complex problem of translating AI ethics into practice. However, there have been few efforts to define AI governance thus far. To bridge this gap, this paper defines AI governance at the organizational level. Moreover, we delineate how AI governance enters into a governance landscape with numerous governance areas, such as corporate governance, information technology (IT) governance, and data governance. Therefore, we position AI governance as part of an organization’s governance structure in relation to these existing governance areas. Our definition and positioning of organizational AI governance paves the way for crafting AI governance frameworks and offers a stepping stone on the pathway toward governed AI.
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Citations
Cites
Design Science in Information Systems Research1
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004) · MIS Quarterly · doi:10.2307/25148625 · Cited by 10,707 · cited 1×
“Third, organizations need practical AI governance tools and frameworks, which opens avenues for design science research [53].”
BackgroundBarredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774 · cited 1×
“The AI governance system can also include structural arrangements such as ethical review boards [35].”
BackgroundThe Black Box Society: The Secret Algorithms That Control Money and Information
Pasquale, F. (2015) · doi:10.4159/harvard.9780674736061 · Cited by 3,796
Kaplan, A., & Haenlein, M. (2019) · Business Horizons · doi:10.1016/j.bushor.2018.08.004 · Cited by 3,140
Organization design: an information processing view
Galbraith, J. R. (1974) · Interfaces · doi:10.1287/inte.4.3.28 · Cited by 2,451
Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilovic, A., et al. (2018) · arXiv · doi:10.48550/arxiv.1810.01943 · Cited by 1,581
Fjeld, J., Achten, N., Hilligoss, H., Nagy, A., & Srikumar, M. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3518482 · Cited by 998 · cited 2×
“In particular, the definition draws on that of AI governance for business [12] while incorporating the regulatory constraints [19, 31] and ethical AI principles [6, 32].”
BackgroundMorley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-019-00165-5 · Cited by 882 · cited 2×
“However, current AI governance work faces the challenge of translating abstract ethical principles, such as fairness, into practicable AI governance processes [7, 8].”
Background
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Fui-Hoon Nah, F., Zheng, R., Cai, J., Siau, K., & Chen, L. (2023) · Journal of Information Technology Case and Application Research · doi:10.1080/15228053.2023.2233814 · Cited by 1,363
Responsible Artificial Intelligence Governance: A Review and Research Framework
Papagiannidis, E., Mikalef, P., & Conboy, K. (2025) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2024.101885 · Cited by 565
Camilleri, M. A. (2024) · Expert Systems · doi:10.1111/exsy.13406 · Cited by 331
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Batool, A., Zowghi, D., & Bano, M. (2025) · AI and Ethics · doi:10.1007/s43681-024-00653-w · Cited by 314
How to explain AI systems to end users: a systematic literature review and research agenda
Laato, S., Tiainen, M., Najmul Islam, A. K. M., & Mäntymäki, M. (2022) · Internet Research · doi:10.1108/intr-08-2021-0600 · Cited by 155
Self-citationAuditing of AI: Legal, Ethical and Technical Approaches
Mökander, J. (2023) · Digital Society · doi:10.1007/s44206-023-00074-y · Cited by 152
“…challenges associated with the use of AI systems in the railway sector from a legal point of view; Light and Panai (2022) conduct an ethical analysis of the principles against which AI systems are being audited; and Minkkinen et al. (2022) focus on the technical aspects of how audits are conducted.”
BackgroundInfluentialThe EU's AI act: A framework for collaborative governance
Cancela-Outeda, C. (2024) · Internet of Things · doi:10.1016/j.iot.2024.101291 · Cited by 129
Wells, B. J., Nguyen, H. M., McWilliams, A., Pallini, M., Bovi, A., Kuzma, A., Kramer, J., Chou, S.-H., Hetherington, T., Corn, P., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01900-y · Cited by 90
Ortega-Bolaños, R., Bernal-Salcedo, J., Germán Ortiz, M., Galeano Sarmiento, J., Ruz, G. A., & Tabares-Soto, R. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10740-3 · Cited by 76
Establishing organizational AI governance in healthcare: a case study in Canada
Kim, J. Y., Hasan, A., Kueper, J., Tang, T., Hayes, C., Fine, B., Balu, S., & Sendak, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01909-3 · Cited by 42
Putting AI Ethics into Practice: The Hourglass Model of Organizational AI Governance
Mäntymäki, M., Minkkinen, M., Birkstedt, T., & Viljanen, M. (2022) · arXiv · doi:10.48550/arxiv.2206.00335 · Cited by 38
Self-citationLedro, C., Nosella, A., Vinelli, A., Dalla Pozza, I., & Souverain, T. (2025) · Journal of Business Research · doi:10.1016/j.jbusres.2025.115531 · Cited by 33
Alami, H., Pozelli Sabio, R., Pérez, E. J., Gagnon, M.-P., Langlois, L., Denis, J.-L., Malas, K., Rivard, L., Savoldelli, M., Ag Ahmed, M. A., et al. (2026) · Journal of Medical Internet Research · doi:10.2196/87448 · Cited by 9
Who Owns AI When It Breaks? The Convergence of AI Governance and Cybersecurity Accountability
Montgomery, J., & Copeland, J. (2026) · The Pinnacle: A Journal by Scholar-Practitioners · doi:10.61643/c79060
Metadata
- Authors
- Mäntymäki, M., Minkkinen, M., Birkstedt, T., & Viljanen, M.
- Venue
- AI and Ethics
- Published
- 2022-02-24
- Volume
- 2
- Issue
- 4
- Pages
- 603-609
- ISSN
- 2730-5953 (ISSN-L)
- 2730-5961
- OpenAlex
- W4214629857
- Cited by
- 285
- References
- 22
- FWCI
- 50.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
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