Societal Adaptation to Advanced AI
This work tackles the problem of managing AI risks for society by shifting focus from development control to resilience-building, though it is incremental as it builds on existing strategies.
The paper addresses the limitations of current AI risk management strategies by proposing a complementary approach focused on societal adaptation to reduce negative impacts from AI diffusion, illustrated with examples in election manipulation, cyberterrorism, and loss of control.
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.