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Responsible governance of generative AI: conceptualizing GenAI as complex adaptive systems

Marijn Janssen

Policy and Society2025https://doi.org/10.1093/polsoc/puae040article
ABDC B
Weight
0.70

What the paper says

Abstract Organizations increasingly use Generative Artificial Intelligence (AI) to create strategic documents, legislation, and recommendations to support decision-making. Many current AI initiatives are technology-deterministic, whereas technology co-evolves with the social environment, resulting in new applications and situations. This paper presents a novel view of AI governance by organizations from the perspective of complex adaptive systems (CASs). AI is conceptualized as a socio-technological and adaptive system in which people, policies, systems, data, AI, processes, and other elements co-evolve. The CAS lens draws attention to focusing AI governance on the entire organization, taking an outward perspective and considering public values and societal concerns. Although there is no shortage of AI governance instruments, they differ in their effectiveness, and combinations of appropriate mechanisms should be selected to deal with AI’s evolving nature and complexity. A major challenge is that no responsibility, and therefore accountability, is taken due to the lack of understanding of the full socio-technological CAS. As such, joint accountability is needed in which involved parties work together.

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https://doi.org/https://doi.org/10.1093/polsoc/puae040

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@article{marijn2025,
  title        = {{Responsible governance of generative AI: conceptualizing GenAI as complex adaptive systems}},
  author       = {Marijn Janssen},
  journal      = {Policy and Society},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/polsoc/puae040},
}

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Responsible governance of generative AI: conceptualizing GenAI as complex adaptive systems

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Evidence weight

0.70

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.80 × 0.4 = 0.32
M · momentum1.00 × 0.15 = 0.15
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.