Undermining Mental Proof: How AI Can Make Cooperation Harder by Making Thinking Easier

TL;DR

This paper introduces the concept of 'mental proof' to explain how AI's ease of use can paradoxically hinder social cooperation by undermining the credibility of private mental states in low-trust situations.

Contribution

It presents a new theoretical framework linking AI's impact on social signaling and cooperation through the concept of mental proof, integrating insights from multiple disciplines.

Findings

AI can weaken mental proofs in low-trust environments.

Understanding mental proof clarifies when AI hinders cooperation.

Theoretical mechanisms explain AI's paradoxical effects on social trust.

Abstract

Large language models and other highly capable AI systems ease the burdens of deciding what to say or do, but this very ease can undermine the effectiveness of our actions in social contexts. We explain this apparent tension by introducing the integrative theoretical concept of "mental proof," which occurs when observable actions are used to certify unobservable mental facts. From hiring to dating, mental proofs enable people to credibly communicate values, intentions, states of knowledge, and other private features of their minds to one another in low-trust environments where honesty cannot be easily enforced. Drawing on results from economics, theoretical biology, and computer science, we describe the core theoretical mechanisms that enable people to effect mental proofs. An analysis of these mechanisms clarifies when and how artificial intelligence can make low-trust cooperation harder despite making thinking easier.