ANSWERABLE FOR WHAT ONE CANNOT KNOW: ACCOUNTABILITY WITHOUT COMPREHENSION IN THE ALGORITHMIC UNIVERSITY
DOI:
https://doi.org/10.35631/IJIREV.826054Keywords:
Accountability, Algorithmic Opacity, Artificial Intelligence Governance, Higher Education, Industry 4.0, Industry 5.0, Innovation LeadershipAbstract
Universities increasingly rely on intelligent systems to screen admissions, detect academic misconduct, flag students at risk and support grading. The leader who signs such a decision remains answerable for it, yet cannot reconstruct the reasoning that produced it. Existing scholarship does not close this gap: work on algorithmic opacity explains why the reasoning is inaccessible, work on artificial intelligence in higher education asks that accountability be traced but not what it consists of, and accountability theory assumes that whoever answers can give reasons. The objective of this paper is to develop a conceptual framework of accountability under algorithmic opacity. It names the condition accountability without comprehension and specifies a form of leadership fitted to it, answerability under opacity, through five dimensions: retained answerability, refusing to displace responsibility onto the system or its supplier; procured comprehension, obtaining through testing, audit and contract the understanding a leader cannot hold personally; scope discipline, holding a system to the boundary within which it was validated; explanation owed to those affected, giving the student a usable account rather than a technical one; and reversion authority, the capacity to return a decision to human hands. Theoretically it extends accountability theory, which treats reason-giving as constitutive of the relationship, by showing the relationship survives when that element is removed. Accountability does not disappear under opacity but shifts, from justifying the decision to justifying the arrangements that produced it, so legitimacy rests on those arrangements. It separates substantive answerability from oversight that legitimates without constraining, and advances six propositions, boundary conditions and a research agenda. For innovation leadership it argues that the capacity to account for intelligent systems is part of the innovation: institutions adopting Industry 4.0 and Industry 5.0 without building it acquire the tools while losing the ability to explain what the tools decide.
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References
Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973-989. https://doi.org/10.1177/1461444816676645
Bearman, M., Ryan, J., & Ajjawi, R. (2023). Discourses of artificial intelligence in higher education: A critical literature review. Higher Education, 86(2), 369-385. https://doi.org/10.1007/s10734-022-00937-2
Bovens, M. (2007). Analysing and assessing accountability: A conceptual framework. European Law Journal, 13(4), 447-468. https://doi.org/10.1111/j.1468-0386.2007.00378.x
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1-12. https://doi.org/10.1177/2053951715622512
Davis, J. H., Schoorman, F. D., & Donaldson, L. (1997). Toward a stewardship theory of management. Academy of Management Review, 22(1), 20-47. https://doi.org/10.5465/amr.1997.9707180258
European Commission, Directorate-General for Research and Innovation. (2021). Industry 5.0: Towards a sustainable, human-centric and resilient European industry. Publications Office of the European Union. https://data.europa.eu/doi/10.2777/308407
European Parliament & Council of the European Union. (2024). Regulation (EU) 2024/1689 of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
Goddard, K., Roudsari, A., & Wyatt, J. C. (2012). Automation bias: A systematic review of frequency, effect mediators, and mitigators. Journal of the American Medical Informatics Association, 19(1), 121-127. https://doi.org/10.1136/amiajnl-2011-000089
Green, B. (2022). The flaws of policies requiring human oversight of government algorithms. Computer Law & Security Review, 45, 105681. https://doi.org/10.1016/j.clsr.2022.105681
Hernandez, M. (2012). Toward an understanding of the psychology of stewardship. Academy of Management Review, 37(2), 172-193. https://doi.org/10.5465/amr.2010.0363
Kelly, A. (2021). A tale of two algorithms: The appeal and repeal of calculated grades systems in England and Ireland in 2020. British Educational Research Journal, 47(3), 725-741. https://doi.org/10.1002/berj.3705
Kemper, J., & Kolkman, D. (2019). Transparent to whom? No algorithmic accountability without a critical audience. Information, Communication & Society, 22(14), 2081-2096. https://doi.org/10.1080/1369118X.2018.1477967
Khairullah, S. A., Harris, S., Hadi, H. J., Sandhu, R. A., Ahmad, N., & Alshara, M. A. (2025). Implementing artificial intelligence in academic and administrative processes through responsible strategic leadership in the higher education institutions. Frontiers in Education, 10, 1548104. https://doi.org/10.3389/feduc.2025.1548104
Maak, T., & Pless, N. M. (2006). Responsible leadership in a stakeholder society: A relational perspective. Journal of Business Ethics, 66(1), 99-115. https://doi.org/10.1007/s10551-006-9047-z
Oncioiu, I., & Bularca, A. R. (2025). Artificial intelligence governance in higher education: The role of knowledge-based strategies in fostering legal awareness and ethical artificial intelligence literacy. Societies, 15(6), 144. https://doi.org/10.3390/soc15060144
Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(6), 49-66. https://doi.org/10.53761/q3azde36
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206-215. https://doi.org/10.1038/s42256-019-0048-x
Santoni de Sio, F., & Mecacci, G. (2021). Four responsibility gaps with artificial intelligence: Why they matter and how to address them. Philosophy & Technology, 34(4), 1057-1084. https://doi.org/10.1007/s13347-021-00450-x
Selwyn, N. (2024). On the limits of artificial intelligence (AI) in education. Nordisk Tidsskrift for Pedagogikk og Kritikk, 10(1), 3-14. https://doi.org/10.23865/ntpk.v10.6062
Wieringa, M. (2020). What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 1-18). Association for Computing Machinery. https://doi.org/10.1145/3351095.3372833
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223-235. https://doi.org/10.1080/17439884.2020.1798995
Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0: Inception, conception and perception. Journal of Manufacturing Systems, 61, 530-535. https://doi.org/10.1016/j.jmsy.2021.10.006
