Defining Deception in Decision Making

Marwa Abdulhai, Micah Carroll, Justin Svegliato, Aryansh Shrivastava, Anca Dragan, Sergey Levine

Keywords: deception, AI safety

TL;DR: a definition for deception under the formalism of decision making

Abstract: With the growing capabilities of machine learning systems, particularly those that interact with humans, there is an increased risk of systems that can easily deceive and manipulate people. Preventing unintended behaviors therefore represents an important challenge for creating aligned AI systems. To approach this challenge in a principled way, we first need to define deception formally. In this work, we present a concrete definition of deception under the formalism of rational decision making in partially observed Markov decision processes. Specifically, we propose a general regret theory of deception under which the degree of deception can be quantified in terms of the actor's beliefs, actions, and utility. To evaluate our definition, we study the degree to which our definition aligns with human judgments about deception. We hope that our work will constitute a step toward both systems that aim to avoid deception, and detection mechanisms to identify deceptive agents.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 14282