The artificial intelligence (AI) governance dilemma
DOI:
https://doi.org/10.55892/jrg.v9i20.3182Palabras clave:
Artificial Intelligence, Governance, Responsibility, Accountability, EU AI ActResumen
The rapid diffusion of Artificial Intelligence (AI) across organizational and societal domains has exposed a tension between innovation, technological capability, and institutional responsibility. While AI systems are often discussed in terms of efficiency, innovation, or risk, less attention has been devoted to the structural misalignment between those who design, control, deploy, and are affected by these systems. This paper develops a theoretically grounded interpretation of AI governance as a problem of distributed agency. Building on Weber, Luhmann, Foucault, and Arendt, it argues that contemporary AI systems cannot be adequately governed through traditional, actor-centric, or Public Administration bureaucracy frameworks. Instead, they require a shift toward system-level accountability. The analysis highlights how current regulatory approaches, like the European Union’s AI Act, only partially address this transformation. The paper concludes by proposing a conceptual model of the ownership–control–responsibility gap and outlining implications for future governance architectures.
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Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., … Horvitz, E.. Guidelines for human-AI interaction. Proceedings of the 2019 CHI Conference, 1–13. 2019.
Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., & Mané, D. Concrete problems in AI safety. 2016. arXiv preprint arXiv:1606.06565.
Axelrod, R.. The complexity of cooperation: Agent-based models of competition and collaboration. Princeton University Press. 1997.
Arendt, Hannah. The human condition. Chicago: University of Chicago Press, 1958.
Arendt, H. Eichmann in Jerusalem: A report on the banality of evil. New York, NY: Viking Press. 1963.
Aveni. A. Overview of AI International and Brazil regulations. Revista da Presidência v. 27 n. 142 2025b. https://revistajuridica.presidencia.gov.br/index.php/saj/article/view/3248 in https://revistajuridica.presidencia.gov.br/index.php/saj/issue/view/153
Aveni A.Free, open source, and paid programs offer. Increasing social surplus. Revista Processus de Estudos de Gestão, Jurídicos e Financeiros v. 16 n. 50 2025a. https://periodicos.processus.com.br/index.php/egjf/article/view/1370
Aveni A. Define artificial intelligence outcomes as intellectual property, a collective right. Revista Processus de Estudos de Gestão, Jurídicos e Financeiros. v.XV, p.1 - 18, 2024.
Aveni A, De Carvalho S.S.M. Avaliação de patentes e inovação DE métodos e problemas. Cad. Prospec., Salvador, v. 10, n. 4, p. 639-649, out./dez. 2017. D.O.I.:http://dx.doi.org/10.9771/cp.v10i4.23018
Bishop, C. M.. Pattern recognition and machine learning. Springer. 2006.
Bostrom, N.. Superintelligence: Paths, dangers, strategies. Oxford University Press. 2014.
Campbell, M., Hoane, A. J., & Hsu, F.. Deep Blue. Artificial Intelligence, 134(1–2), 57–83.. 2002. https://doi.org/10.1016/S0004-3702(01)00129-1
Collindrige, David. The social control of technology. London: Frances Pinter, 1980.
European Commission (EC). Proposal for a regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). 2021. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence
European Parliament and Council of the European Union (EP). Artificial Intelligence Act (final text, pending implementation). 2024
Campos Faria L. And Aveni A. Clarify Artificial Intelligence (AI) decision models' rights in the Intellectual Property (IP) system. Revista JRG de Estudos Acadêmicos. v.7, p.e141033, 2024a.
Campos Faria L. And Aveni A. Desafios e perspectivas da Inteligência artificial na análise da concorrência do Poder Público. Revista JRG de Estudos Acadêmicos. v.7, p.e141035, 2024b. https://revistajrg.com/ index.php/jrg][doi:10.55892/ jrg.v7i14.1035
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Vayena, E.AI4People—An ethical framework for a good AI society. Minds and Machines, 28(4), 689–707. 2018. https://doi.org/10.1007/s11023-018-9482-5
Floridi, L., & Cowls, J. A. Unified framework of five principles for AI in society. Harvard Data Science Review, 1(1). 2019. https://doi.org/10.1162/99608f92.8cd550d1
Foucault, Michel. Security, territory, population. New York: Palgrave, 2007.
Foucault, Michel. Governmentality. In G. Burchell, C. Gordon, & P. Miller (Eds.), The Foucault effect: Studies in governmentality (pp. 87–104). Chicago, IL: University of Chicago Press. 1991.
Garcez, A. d., Lamb, L. C., & Gabbay, D. (2009). Neural-symbolic cognitive reasoning. Springer.
Garcez, A. d., Besold, T. R., Raedt, L. D., Földiak, P., Hitzler, P., Icard, T., … Silver, D. Neural-symbolic learning and reasoning: Contributions and challenges. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 9482–9489. 2019.
Goertzel, B. Artificial general intelligence: Concept, state of the art, and future prospects. Journal of Artificial General Intelligence, 5(1), 1–48. 2014.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y.. Generative adversarial nets. Advances in Neural Information. 2014
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning (2nd ed.). Springer.
Ho, J., Jain, A., & Abbeel, P. Denoising diffusion probabilistic models. Advances in
Neural Information Processing Systems, 33, 6840–6851. 2020.
Jensen, Michael; Meckling, William. Theory of the firm. Journal of Financial Economics, v. 3, p. 305–360, 1976.
Kingma, D. P., & Welling, M. Auto-encoding variational Bayes. International Conference on Learning Representations (ICLR). 2014.
Latour, Bruno. Reassembling the social. Oxford: Oxford University Press, 2005.
lacuna, Y., Bengio, Y., & Hinton, G. Deep learning. Nature, 521(7553), 436–444. 2015. https://doi.org/10.1038/nature14539
Lin, P., Abney, K., & Bekey, G. A. Robot ethics: The ethical and social implications of robotics. MIT Press. 2011.
Luhmann, Niklas. Social systems. Stanford: Stanford University Press, 1995.
McCarthy, J. Programs with common sense. Stanford University AI Laboratory. 1969.
Maslej, Nestor et al. AI Index Report 2025. Stanford: HAI, 2025.
Murphy, K. P. Machine learning: A probabilistic perspective. MIT Press. 2012.
Newell, A., & Simon, H. A.. Computer science as empirical inquiry: Symbols and search. Communications of the ACM, 19(3), 113–126. 1976. https://doi.org/10.1145/360018.360022
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I.. Language models are unsupervised multitask learners. OpenAI Technical Report. 2019.
Rumelhart, D. E., McClelland, J. L., & PDP Research Group. Parallel distributed processing: Explorations in the microstructure of cognition. MIT Press. 1986.
Russel, Stuart; Norvig, Peter. Artificial intelligence: a modern approach. 4. ed. Pearson, 2020.
Russell, S. J.. Rationality and intelligence. Artificial Intelligence, 94(1–2), 57–77. 1997. https://doi.org/10.1016/S0004-3702(97)00026-8
Stanford University. The AI Index 2025 Annual Report. By Nestor Maslej, Loredana Fattorini, Raymond Perrault, Yolanda Gil, Vanessa Parli, Njenga Kariuki, Emily Capstick, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, Toby Walsh, Armin Hamrah, Lapo Santarlasci, Julia Betts Lotufo, Alexandra Rome, Andrew Shi, Sukrut Oak. “The AI Index 2025 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2025. https://doi.org/10.48550/arXiv.2504.07139
Weber, Max. Economy and society: An outline of interpretive sociology (G. Roth & C. Wittich, Eds.). Berkeley, CA: University of California Press, 1978
Sutton, R. S., & Barto, A. G. Reinforcement learning: An introduction (2nd ed.). MIT Press. 2018.
Wooldridge, M. An introduction to multiagent systems (2nd ed.). Wiley. 2009.
Wooldridge, M., & Jennings, N. R. Intelligent agents: Theory and practice. Knowledge Engineering Review, 10(2), 115–152. 1995.
