Defining Strategic AI Deception Through Generalized Propositional Attitudes

Human deception involves intentionally causing false beliefs. Since it is contested whether large language models (LLMs) and artificial intelligence (AI) in general have beliefs and intentions, deception by such systems is typically defined in terms of observable behavior. However, this paper argues that such behavioral definitions fall short of the strategic nature of deception, among other things, because they do not consistently distinguish deception from mere error. Moreover, behavioral definitions of AI deception are not optimal as methodological guidance for mechanistic approaches to AI deception because they do not specify candidates for internal markers of deception. It is important to take the internal mechanisms of LLMs and AI systems into account when addressing deception, since skilled forms of deception may be behaviorally indistinguishable from honesty. This paper formulates these challenges to behavioral definitions and addresses them by proposing a novel definition of strategic deception in terms of generalized propositional attitudes. Specifically, it provides generalizations of the attitudes of belief and desire that can be ascribed to current and future AI systems less controversially than their human counterparts, yet are expressive enough to figure in a definition of strategic deception. This definition has several advantages over its behavioral alternatives as it 1) better satisfies pre-theoretic intuitions such as the error condition 2) fits better with applied researchers’ usage of the term “strategic deception”, 3) is relevant to large-scale risks of AI deception, and 4) is methodologically instructive for mechanistic approaches to deception.