Societal Adaptation to Advanced AI

2024-05-16

arXiv.org

AI Summary

In response to emergent risks posed by advanced AI—including election interference, cyberterrorism, and systemic failure—this paper proposes a novel risk governance paradigm centered on “societal adaptability,” moving beyond conventional technical containment toward systemic resilience. Methodologically, it establishes a dynamic, three-phase “avoid–defend–recover” cycle, integrating conceptual modeling, multi-scenario risk analysis, policy intervention design, and a cross-stakeholder governance framework involving governments, industry, and independent third parties. Key contributions include: (1) the first formal theoretical framework for societal adaptability in AI governance; (2) an actionable, stage-wise adaptation pathway; and (3) a globally applicable, operational AI governance system that reconciles stringent safety safeguards with sustained innovation capacity. This work delivers an original, implementation-oriented solution for balancing AI risk mitigation and technological advancement. (149 words)

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Game Theory and Economic Paradigms: Adversarial Learning
• Humans and AI: Learning Human Values and Preferences

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Existing strategies for managing risks from advanced AI systems often focus on affecting what AI systems are developed and how they diffuse. However, this approach becomes less feasible as the number of developers of advanced AI grows, and impedes beneficial use-cases as well as harmful ones. In response, we urge a complementary approach: increasing societal adaptation to advanced AI, that is, reducing the expected negative impacts from a given level of diffusion of a given AI capability. We introduce a conceptual framework which helps identify adaptive interventions that avoid, defend against and remedy potentially harmful uses of AI systems, illustrated with examples in election manipulation, cyberterrorism, and loss of control to AI decision-makers. We discuss a three-step cycle that society can implement to adapt to AI. Increasing society's ability to implement this cycle builds its resilience to advanced AI. We conclude with concrete recommendations for governments, industry, and third-parties.

Problem

• Artificial Intelligence
• Societal Adaptation
• Risk Mitigation

Innovation

• Adaptive Strategy
• Resilience Enhancement
• AI Governance