AI deception: A survey of examples, risks, and potential solutions

Peter Seho Park · Simon Goldstein · Aidan O’Gara · Dan Hendrycks

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Ethics and Social Impacts of AI Digitalization, Law, and Regulation Misinformation and Its Impacts

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) and general-purpose AI systems (including large language models). Next, we detail several risks from AI deception, such as fraud, election tampering, and losing control of AI. Finally, we outline several potential solutions: 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.

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2012Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds 1990Influence tactics, affect, and exchange quality in supervisor-subordinate interactions: A laboratory experiment and field study.2019Human Compatible: Artificial Intelligence and the Problem of Control 2022AI-to-AI Code Reviews of GitHub Pull Requests 2021The Alignment Problem: Machine Learning and Human Values

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