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Artificial intelligence, complexity, and systemic resilience in global governance.

Andrés Ilcic1,2, Miguel Fuentes2,3,4, Diego Lawler2

  • 1Facultad de Filosofía y Humanidades, Universidad Nacional de Córdoba, Córdoba, Argentina.

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Summary
This summary is machine-generated.

Artificial Intelligence (AI) integration in global governance offers resilience but poses risks. Complexity science provides a framework for adaptive, inclusive models to manage AI

Keywords:
adaptive governanceartificial intelligence (AI)complexity scienceethical AIinternational governancescience diplomacysocio-technical systemssystemic resilience

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Area of Science:

  • Global Governance
  • Complexity Science
  • Artificial Intelligence Ethics

Background:

  • Artificial Intelligence (AI) is transforming international governance, creating both opportunities for enhanced systemic resilience and significant ethical, social, and geopolitical challenges.
  • AI presents a dual capacity: a tool for improving decision-making and crisis management, and a disruptive force introducing risks like data bias and exacerbated inequalities.
  • Existing governance models struggle to address the complexities and uncertainties inherent in AI-driven socio-technical systems.

Purpose of the Study:

  • To propose complexity science as a framework for navigating AI's integration into global governance.
  • To advocate for adaptive, inclusive governance models that manage AI's inherent uncertainties.
  • To bridge the gap between governance literature and the philosophy of science and technology using complexity as a boundary concept.

Main Methods:

  • Analysis of AI's dual role in governance: transformative tool versus disruptive force.
  • Framing resilience as a boundary concept to integrate diverse disciplines and practices.
  • Conceptual approach integrating complexity insights with institutional modularity and stakeholder collaboration.

Main Results:

  • Complexity science offers a valuable framework for understanding and managing AI in global governance.
  • Adaptive and inclusive governance models are crucial for addressing AI's risks, including data bias and inequality.
  • Integrating complexity insights with principles like institutional modularity and stakeholder collaboration fosters equity, accountability, and sustainability.

Conclusions:

  • AI's integration into global governance necessitates adaptive, inclusive models informed by complexity science.
  • Resilience, framed as a boundary concept, is key to aligning technological innovation with societal values.
  • A conceptual approach bridging governance and philosophy of science and technology ensures AI deployment contributes to a more equitable and resilient global future.