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Societal Adaptation to Advanced AI

2024-05-16 17:52:12
Jamie Bernardi, Gabriel Mukobi, Hilary Greaves, Lennart Heim, Markus Anderljung

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.

Abstract (translated)

目前,管理先进人工智能系统风险的策略通常集中于影响AI系统的发展和扩散。然而,这种方法在AI开发者数量不断增加的情况下变得越来越不现实,也会阻碍有益的和有害的使用案例。因此,我们呼吁一种互补的方法:增加社会对先进AI的适应性,即减少从给定扩散水平开始,给定AI能力的潜在有害影响。我们引入了一个概念框架,以帮助识别避免、防御和修复可能对AI系统产生有害影响的适应干预措施,并通过选举操纵、网络恐怖主义和失去对AI决策者的控制等例子进行了说明。我们讨论了社会可以采用的三步循环来适应AI。不断提高社会实施这一循环能力增强了其对先进AI的韧性。最后,我们给出了政府、行业和第三方的具体建议。

URL

https://arxiv.org/abs/2405.10295

PDF

https://arxiv.org/pdf/2405.10295.pdf


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