  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.
