The phenomenon of knowledge fragmentation, rooted in Cartesianism, has led to partial observations about knowledge itself. In this sense, theories of knowledge capable of addressing significant human problems cannot be limited tnike wiki miroir terzo sport jumpsuit nike comprar fatos de treino adidas baratos puma suede classic velvet sneakers in cordovan leather calfskin velvet tongue and toe cap játék hajszárító árukeresö meilleur lampe uv converse lugged beige detske lyžiarske nohavice 134 140 kilpi predam brandon aiyuk youth jersey cheap jordan 4 corsair ddr3 1600 converse lugged beige jayden daniels lsu jersey yeezy sneakerso treating them separately or relying solely on the accumulation of knowledge. Instead, they must involve the transformation of their principles and the multidimensional observation of reality (MORIN, 2003).
Morin (2003) argues that modern society is comprised of a series of problems resulting from simple, partial, and fragmented thinking, such that academic disciplines prove incapable of creating connections and correlations between elements. In this context, the author highlights the emergence of a scientific approach prioritizing the interconnection of concepts and elements within systems, recognizing that reality is not simply structured but is multidimensional and complex.
In a perspective similar to Morin (2003), Kuhn (1987) emphasizes that scientific development occurs through the overcoming of paradigms, which are understood as a ‘constellation of beliefs, values, and techniques shared by members of a scientific community’ (KUHN, 1987, p. 218). When these paradigms enter into crisis, they are transformed by others in a historical series marked by certain cycles. Scientific progress, therefore, is not a product of linear advancement but results from different phases that involve irregularities and conflicts.
The complexity of the governance issue in the field of Artificial Intelligence (AI) is evident and materializes through specific arguments. The impacts caused by the increased use of AI extend beyond areas of technological innovation or engineering, reaching into political, administrative, and sociological fields. In this sense, governance in AI is a crucial matter for the future of Public Administration (DENHARDT, 2001; UZUN, et al., 2022). This phenomenon emerges as a multidisciplinary debate covering public policies, computer engineering, philosophy, law, sociology, and international relations (BOSTROM, et al., 2019; UZUN, et al., 2022). In fact, not only are the challenges of AI multidisciplinary, but so are its benefits, which are vast and impact areas such as medicine and health, transportation, education, science, sustainability, and economic development (DAFOE, 2018).
There is a widespread consensus that AI systems need to be well-governed to operate in alignment with human and social values to harness the benefits and control their risks. However, the literature on AI governance is still disorganized. Moreover, this phenomenon in the field of AI is embedded in a broader landscape that involves corporate governance, data governance, and Information Technology (IT) governance, making the study even more complex (MÄNTYMÄKI, et al., 2022).
“Regardless of the concepts studied, it is certain that the topic of governance in AI has led researchers into complex discussions, making it impossible to develop a single universally accepted conceptualization. Regardless of the conceptual multiplicity, the centrality of the topic on government agendas is emphasized, as it directly engages with the quality of life for future generations (UZUN, et al., 2022). Governments, as well as civil society and private sectors, are responsible for debating the use of AI mechanisms to ensure transparency and accountability for these systems, mitigating the risks and potential disadvantages of using these systems, while simultaneously harnessing the potential of this technology (GASSER & ALMEIDA, 2017).
It is worth noting that not only governance but also ethics is the subject of a remarkable complexity of approaches, constituting a multidisciplinary, complex field subject to different interpretations. For example, Bartneck (2021) defines ethics as principles, general judgments, and norms, currently the subject of different schools of thought. Resnik (2015) argues that ethics is a set of standards that differentiate acceptable from unacceptable behaviors. This means that for an ethical theory to be relevant to current problems, it needs to be dynamic and multidisciplinary, capable of efficiently addressing specific issues in the field of Administration.
In seeking to connect the phenomenon of AI governance with Morin’s theory (2003), certain elements can be recognized: (i) the complexity of the AI governance phenomenon; (ii) the relations of this topic with other scientific fields; (iii) the various connections with stakeholders who have diverse interests; and (iv) the demand for an integrated scientific study that involves different disciplines and perspectives.
Finally, a connection between Kuhn’s theory of the scientific revolution (1987) and the complexity of governance in AI lies in the governance paradigms that, over the decades, have been replaced by new ways of understanding this phenomenon. New governance concepts replace old ones and encompass a greater number of elements, complexifying the concept through interconnection with other elements. This complexification of governance is what qualifies it to provide answers to the issues involving AI.
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BARTNECK, Christopher et al. An introduction to ethics in robotics and AI. Springer Nature, 2021.
BOSTROM, Nick; DAFOE, Allan; FLYNN, Carrick. Public policy and superintelligent AI: a vector field approach. Governance of AI Program, Future of Humanity Institute, University of Oxford. Oxford, UK, 2018.
DAFOE, Allan. AI governance: a research agenda. Governance of AI Program, Future of Humanity Institute, University of Oxford: Oxford, UK, v. 1442, p. 1443, 2018.
DENHARDT, Robert B. The big questions of public administration education. Public Administration Review, v. 61, n. 5, p. 526-534, 2001.
GASSER, Urs; ALMEIDA, Virgilio AF. A layered model for AI governance. IEEE Internet Computing, v. 21, n. 6, p. 58-62, 2017.
KHUN, T. Posfácio. In: KHUN, T. A estrutura das revoluções científicas. São Paulo: Perspectiva, 1987, p. 217-257.
MÄNTYMÄKI, Matti et al. Defining organizational AI governance. AI and Ethics, p. 1-7, 2022.
MORIN, E. Introdução ao pensamento complexo. Lisboa, Instituto Piaget, 2003 (Trechos escolhidos – p. 57 a 76 e p. 85-93).
RESNIK, David B. et al. What is ethics in research & why is it important. December, 2015.
UZUN, Mehmet Metin; YILDIZ, Mete; ÖNDER, Murat. Big Questions of AI in Public Administration and Policy. Siyasal: Journal of Political Sciences, v. 31, n. 2, p. 423-442. 2022.
