From Technical Capability to Institutional Authority: Human Responsibility Before AI Execution

PDF
Published: Σεπ 18, 2026
DOI: https://doi.org/10.12681/dia.46982
Keywords:
execution governance Institutional authority Artificial intelligence Authorization Legitimacy Human responsibility Execution admissibility Decision ownership
Maria Kollia
STANAIC, California, USA Independent Researcher; Co-Chair, STANAIC
Abstract

Artificial intelligence governance often treats permission as though it completes the problem of authority. Yet a system may be technically capable of acting, connected to operational resources, and supported by an earlier approval while the proposed action is no longer institutionally legitimate when execution occurs. This article distinguishes technical capability, authorization, legitimacy, and execution admissibility. It argues that consequential AI-enabled action requires more than access or prior approval: it requires valid institutional authority, identifiable decision ownership, sufficient and current evidence, applicable policy conditions, and a genuine capacity for human intervention. The article names the interval between prior authorization and present execution the execution gap. It then develops the execution gate as an institutional mechanism through which an action may be allowed, held, denied, or escalated before technical capacity becomes real-world consequence. Human responsibility, on this account, must be constituted before action, not reconstructed only after harm.

Article Details
  • Section
  • Articles
Downloads
Download data is not yet available.
References
Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human-AI interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Paper 3, pp. 1-13). Association for Computing Machinery.
Arnold, M., Bellamy, R. K. E., Hind, M., Houde, S., Mehta, S., Mojsilovic, A., Nair, R., Ramamurthy, K. N., Olteanu, A., Piorkowski, D., Reimer, D., Richards, J., Tsay, J., & Varshney, K. R. (2019). FactSheets: Increasing trust in AI services through supplier declarations of conformity. IBM Journal of Research and Development, 63(4/5), 6:1-6:13.
Ashmore, R., Calinescu, R., & Paterson, C. (2021). Assuring the machine learning lifecycle: Desiderata, methods, and challenges. ACM Computing Surveys, 54(5), Article 111.
Bainbridge, L. (1983). Ironies of automation. Automatica, 19(6), 775-779.
Bovens, M. (2007). Analysing and assessing accountability: A conceptual framework. European Law Journal, 13(4), 447-468.
Citron, D. K. (2008). Technological due process. Washington University Law Review, 85(6), 1249-1313.
Council of Europe. (2024). Framework Convention on Artificial Intelligence and Human Rights. Democracy and the Rule of Law (CETS No. 225).
Dafov, B. (2026). The Logic of Prediction and the Non-Totalization of Possibility: Uncertainty, Incompleteness, and the Limits of Algorithmic Rationality. Dia-noesis: A Journal of Philosophy, 19(1), 301-320.
European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence. Official Journal of the European Union, L 2024/1689.
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daume III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86-92.
Hrechukha, L., Pavlenko, O., & Butko, N. (2026). The Erosion of Subjectivity: Cognitive Offloading and the Responsibility Gap in the Age of Algorithmic Governance. Dia-noesis: A Journal of Philosophy, 19(1), 137-158.
Konstantinou, K., & Chorianopoulou, M. K. (2026). Agency, responsibility, and the AGI mirror: A metaethical and anthropological inquiry. Dia-noesis: A Journal of Philosophy, 19(1), 179-202.
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633-705.
Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50-80.
Matthias, A. (2004). The responsibility gap: Ascribing responsibility for the actions of learning automata. Ethics and Information Technology, 6(3), 175-183.
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220-229). Association for Computing Machinery.
National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). U.S. Department of Commerce.
Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse, disuse, abuse. Human Factors, 39(2), 230-253.
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33-44). Association for Computing Machinery.
Raz, J. (1986). The morality of freedom. Clarendon Press.
Sandhu, R. S., Coyne, E. J., Feinstein, H. L., & Youman, C. E. (1996). Role-based access control models. Computer, 29(2), 38-47.
Santoni de Sio, F., & van den Hoven, J. (2018). Meaningful human control over autonomous systems: A philosophical account. Frontiers in Robotics and AI, 5, Article 15.
Searle, J. R. (1995). The construction of social reality. Free Press.
Selbst, A. D., boyd, d., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59-68). Association for Computing Machinery.
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe and trustworthy. International Journal of Human-Computer Interaction, 36(6), 495-504.
Skitka, L. J., Mosier, K. L., & Burdick, M. (1999). Does automation bias decision-making? International Journal of Human-Computer Studies, 51(5), 991-1006.
Weber, M. (1978). Economy and society: An outline of interpretive sociology (G. Roth & C. Wittich, Eds.). University of California Press.
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.