Responsible AI Governance in the Firm: A Conceptual Framework Linking Board Oversight, Accountability Structures and Organisational Legitimacy
DOI:
https://doi.org/10.68854/amsbr651Keywords:
AI governance, corporate governance, board oversight, accountability, responsible AI, organisational legitimacy, risk management, ESG, stakeholder theoryAbstract
Artificial intelligence (AI) is now used in pricing, hiring, lending, customer service and strategy, and the governance of these systems has become a board-level responsibility. Yet most governance scholarship treats AI as a technology risk to be delegated to management rather than as an accountability question for directors, and the growing literature on responsible AI pays little attention to the board. At the same time, firms increasingly publish AI principles whose credibility is contested, raising the question of when AI governance is substantive and when it is merely symbolic. This conceptual paper develops a framework for responsible AI governance within the firm. Methodologically, it uses a theory-synthesis design based on an integrative review of 43 peer-reviewed journal articles published between 2023 and 2026. Each article was verified against its bibliographic record and screened for retractions and corrections, and the articles were coded thematically by governance problem, board function and governance mode. The synthesis integrates corporate governance theory, stakeholder and legitimacy theory, and research on AI governance, accountability and organisational AI ethics. It identifies four distinctive governance problems created by AI: opacity, scale, drift and diffuse responsibility. It maps each problem onto the board functions of strategy oversight, risk oversight and accountability. It distinguishes symbolic from substantive AI governance and derives six propositions on the roles of board AI expertise, the verifiability of institutional pressure, integration into existing risk and audit structures, and institutional voids. The paper argues that substantive AI governance is a source of durable organisational legitimacy with investors, regulators and customers, rather than a compliance cost, whereas symbolic governance offers fragile legitimacy that can collapse after an AI incident. Situating the framework in emerging-economy governance conditions, it recommends that Nigerian and African boards treat AI as a standing strategic and risk matter and build or access AI expertise. Boards should also assign AI oversight to existing committees, designate accountable executives and report against measurable indicators. Regulators and professional bodies should pair AI disclosure with verification and director education.
References
Agnese, P., Arduino, F. R., & Di Prisco, D. (2025). The era of artificial intelligence: What implications for the board of directors? Corporate Governance: The International Journal of Business in Society, 25(2), 272–287. https://doi.org/10.1108/CG-06-2023-0259
Ahdadou, M., Aajly, A., & Tahrouch, M. (2024). Unlocking the potential of augmented intelligence: A discussion on its role in boardroom decision-making. International Journal of Disclosure and Governance, 21(3), 433–446. https://doi.org/10.1057/s41310-023-00207-2
Ahdadou, M., Ahdadou, A., Aajly, A., & Tahrouch, M. (2026). Artificial intelligence in corporate boards: A dual-dimensional framework for integration across autonomy and structural levels. Data Science and Management, 9(2), Article 100174. https://doi.org/10.1016/j.dsm.2025.12.001
Ashiru, F., Adegbite, E., & Nakpodia, F. (2023). Nigerian professional investors' sense-making of the impact of shareholder activism on corporate accountability. Journal of Accounting and Public Policy, 42(4), Article 107114. https://doi.org/10.1016/j.jaccpubpol.2023.107114
Ayana, G., Dese, K., Daba Nemomssa, H., Habtamu, B., Mellado, B., Badu, K., Yamba, E., Faye, S. L., Ondua, M., Nsagha, D., Nkweteyim, D., & Kong, J. D. (2024). Decolonizing global AI governance: Assessment of the state of decolonized AI governance in Sub-Saharan Africa. Royal Society Open Science, 11(8), Article 231994. https://doi.org/10.1098/rsos.231994
Batool, A., Zowghi, D., & Bano, M. (2025). AI governance: A systematic literature review. AI and Ethics, 5, 3265–3279. https://doi.org/10.1007/s43681-024-00653-w
Ben-Shahar, D., Carmeli, A., Sulganik, E., & Weiss, D. (2026). Artificial intelligence transforms board decision-making processes: An extension of the information-processing model of board decision synergy. AI & Society, 41(5), 4349–4364. https://doi.org/10.1007/s00146-025-02773-1
Biddle, J. B., Nelson, J. P., & Olugbade, O. E. (2024). How can we know if you are serious? Ethics washing, symbolic ethics offices, and the responsible design of AI systems. Canadian Journal of Philosophy, 54(4), 329–345. https://doi.org/10.1017/can.2025.9
Birkstedt, T., Minkkinen, M., Tandon, A., & Mäntymäki, M. (2023). AI governance: Themes, knowledge gaps and future agendas. Internet Research, 33(7), 133–167. https://doi.org/10.1108/INTR-01-2022-0042 Bonsón, E., Bednárová, M., & Perea, D. (2023). Disclosures about algorithmic decision making in the corporate reports of Western European companies. International Journal of Accounting Information Systems, 48, Article 100596. https://doi.org/10.1016/j.accinf.2022.100596
Brand, V. (2024). Directors' duties and AI regulation. Griffith Law Review, 33(4), 324–354. https://doi.org/10.1080/10383441.2024.2422221
Camilleri, M. A. (2024). Artificial intelligence governance: Ethical considerations and implications for social responsibility. Expert Systems, 41(7), Article e13406. https://doi.org/10.1111/exsy.13406
Da Silva, M. (2024). Responsibility gaps. Philosophy Compass, 19(9–10), Article e70002. https://doi.org/10.1111/phc3.70002 Díaz-Rodríguez, N., Del Ser, J., Coeckelbergh, M., López de Prado, M., Herrera-Viedma, E., & Herrera, F. (2023). Connecting the dots in trustworthy artificial intelligence: From AI principles, ethics, and key requirements to responsible AI systems and regulation. Information Fusion, 99, Article 101896. https://doi.org/10.1016/j.inffus.2023.101896
Effoduh, J. O., Akpudo, U. E., & Kong, J. D. (2024). Toward a trustworthy and inclusive data governance policy for the use of artificial intelligence in Africa. Data & Policy, 6, Article e34. https://doi.org/10.1017/dap.2024.26
Elsayed, D. H., & Ahmed, E. A. (2025). Board of directors' attributes and artificial intelligence disclosure: Evidence from Egyptian banks. Journal of Financial Reporting and Accounting. Advance online publication. https://doi.org/10.1108/JFRA-01-2025-0044
Galani, S.-V., Rotaru, G.-C., & Dumitru, A.-M. (2026). AI disclosure dynamics in large global corporations. Poslovna izvrsnost – Business Excellence, 20(1), 43–64. https://doi.org/10.22598/pi-be/2026.1.42656
Grimmelikhuijsen, S. (2023). Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making. Public Administration Review, 83(2), 241–262. https://doi.org/10.1111/puar.13483
Huhta, M., Sivunen, A., & van Zoonen, W. (2026). Constituting AI-enabling organizations: Legitimation through AI principles. International Journal of Business Communication. Advance online publication. https://doi.org/10.1177/23294884261465791
Laine, J., Minkkinen, M., & Mäntymäki, M. (2024). Ethics-based AI auditing: A systematic literature review on conceptualizations of ethical principles and knowledge contributions to stakeholders. Information & Management, 61(5), Article 103969. https://doi.org/10.1016/j.im.2024.103969
Laux, J., Wachter, S., & Mittelstadt, B. (2024). Trustworthy artificial intelligence and the European Union AI act: On the conflation of trustworthiness and acceptability of risk. Regulation & Governance, 18(1), 3–32. https://doi.org/10.1111/rego.12512
Llorca Albareda, J. (2025). Uncovering the gap: Challenging the agential nature of AI responsibility problems. AI and Ethics. Advance online publication. https://doi.org/10.1007/s43681-025-00685-w
Lu, Q., Zhu, L., Xu, X., Whittle, J., Zowghi, D., & Jacquet, A. (2024). Responsible AI pattern catalogue: A collection of best practices for AI governance and engineering. ACM Computing Surveys, 56(7), Article 173. https://doi.org/10.1145/3626234
Minkkinen, M., Niukkanen, A., & Mäntymäki, M. (2024). What about investors? ESG analyses as tools for ethics-based AI auditing. AI & Society, 39(1), 329–343. https://doi.org/10.1007/s00146-022-01415-0 Mökander, J., & Floridi, L. (2023). Operationalising AI governance through ethics-based auditing: An industry case study. AI and Ethics, 3(2), 451–468. https://doi.org/10.1007/s43681-022-00171-7 Mökander, J., Schuett, J., Kirk, H. R., & Floridi, L. (2024). Auditing large language models: A three-layered approach. AI and Ethics, 4(4), 1085–1115. https://doi.org/10.1007/s43681-023-00289-2
Munn, L. (2023). The uselessness of AI ethics. AI and Ethics, 3(3), 869–877. https://doi.org/10.1007/s43681-022-00209-w
Nakpodia, F., Adegbite, E., Olan, F., & Ashiru, F. (2025a). Emergent ethnicity logic and corporate governance in Nigerian boardrooms. Journal of Accounting and Public Policy, 54, Article 107380. https://doi.org/10.1016/j.jaccpubpol.2025.107380
Nakpodia, F., Ashiru, F., Adegbite, E., & Koporcic, N. (2025b). Insidious institutional challenges of mature MNE subsidiaries operating in weak institutional markets: Corporate governance to the rescue. Journal of International Management, 31(4), Article 101258. https://doi.org/10.1016/j.intman.2025.101258
Novelli, C., Taddeo, M., & Floridi, L. (2024). Accountability in artificial intelligence: What it is and how it works. AI & Society, 39(4), 1871–1882. https://doi.org/10.1007/s00146-023-01635-y
Oimann, A.-K., & Tollon, F. (2025). Responsibility gaps and technology: Old wine in new bottles? Journal of Applied Philosophy, 42(1), 337–356. https://doi.org/10.1111/japp.12763
Papagiannidis, E., Enholm, I. M., Dremel, C., Mikalef, P., & Krogstie, J. (2023). Toward AI governance: Identifying best practices and potential barriers and outcomes. Information Systems Frontiers, 25(1), 123–141. https://doi.org/10.1007/s10796-022-10251-y
Papagiannidis, E., Mikalef, P., & Conboy, K. (2025). Responsible artificial intelligence governance: A review and research framework. The Journal of Strategic Information Systems, 34(2), Article 101885. https://doi.org/10.1016/j.jsis.2024.101885
Plantinga, P., Shilongo, K., Mudongo, O., Umubyeyi, A., Gastrow, M., & Razzano, G. (2024). Responsible artificial intelligence in Africa: Towards policy learning. Data & Policy, 6, Article e72. https://doi.org/10.1017/dap.2024.60 Sætra, H. S. (2023). The AI ESG protocol: Evaluating and disclosing the environment, social, and governance implications of artificial intelligence capabilities, assets, and activities. Sustainable Development, 31(2), 1027– 1037. https://doi.org/10.1002/sd.2438
Sahiner, B., Chen, W., Samala, R. K., & Petrick, N. (2023). Data drift in medical machine learning: Implications and potential remedies. British Journal of Radiology, 96(1150), Article 20220878. https://doi.org/10.1259/bjr.20220878
Sanderson, C., Douglas, D., Lu, Q., Schleiger, E., Whittle, J., Lacey, J., Newnham, G., Hajkowicz, S., Robinson, C., & Hansen, D. (2023). AI ethics principles in practice: Perspectives of designers and developers. IEEE Transactions on Technology and Society, 4(2), 171–187. https://doi.org/10.1109/TTS.2023.3257303
Schuett, J. (2024). Risk management in the Artificial Intelligence Act. European Journal of Risk Regulation, 15(2), 367–385. https://doi.org/10.1017/err.2023.1
Schuett, J. (2025). Three lines of defense against risks from AI. AI & Society, 40(2), 493–507. https://doi.org/10.1007/s00146-023-01811-0
Schultz, M. D., Conti, L. G., & Seele, P. (2025). Digital ethicswashing: A systematic review and a process-perception-outcome framework. AI and Ethics, 5, 805– 818. https://doi.org/10.1007/s43681-024-00430-9
Schultz, M. D., & Seele, P. (2023). Towards AI ethics' institutionalization: Knowledge bridges from business ethics to advance organizational AI ethics. AI and Ethics, 3, 99–111. https://doi.org/10.1007/s43681-022-00150-y
Taeihagh, A. (2025). Governance of generative AI. Policy and Society, 44(1), 1–22. https://doi.org/10.1093/polsoc/puaf001
Zhang, J., Ki, E.-J., & Cai, Q. (2026). Legitimizing AI: Ethical AI in corporate ethics statements. Corporate Communications: An International Journal, 31(1), 21–36. https://doi.org/10.1108/CCIJ-07-2025-0173
Downloads
Published
Issue
Section
License
Copyright (c) 2026 IIAR Journal of Business, Management, and Entrepreneurship
This work is licensed under a Creative Commons Attribution 4.0 International License.
