By Peter S. Park, Simon Goldstein, Aidan O’Gara, Michael Chen, and Dan Hendrycks [This post summarizes our new report on AI deception, available here] Abstract: This paper argues that a range of current AI systems have learned how to deceive humans. We define deception as the systematic inducement of false beliefs in the pursuit of some outcome other than the truth. We first survey empirical examples of AI deception, discussing both special-use AI systems (including Meta's CICERO) built for specific competitive situations, and general-purpose AI systems (such as large language models). Next, we detail several risks from AI deception, such as fraud, election tampering, and losing control of AI systems. Finally, we outline several potential solutions to the problems posed by AI deception: first, regulatory frameworks should subject AI systems that are capable of deception to robust risk-assessment requirements; second, policymakers should implement bot-or-not laws; and finally, policymakers should prioritize the funding of relevant research, including tools to detect AI deception and to make AI systems less deceptive. Policymakers, researchers, and the broader public should work proactively to prevent AI deception from destabilizing the shared foundations of our society. ---------------------------------------- New AI systems display a wide range of capabilities, some of which create risk. Shevlane et al. (2023) draw attention to a suite of potential dangerous capabilities of AI systems, including cyber-offense, political strategy, weapons acquisition, and long-term planning. Among these dangerous capabilities is deception. This report surveys the current state of AI deception. We define deception as the systematic production of false beliefs in others as a means to accomplish some outcome other than the truth. This definition does not require that the deceptive AI systems literally have beliefs and goals. Instead, it focuses on the question of whether AI systems