Antisocial behavior towards large language model users: experimental evidence

TL;DR

This study provides experimental evidence that users relying on large language models face social sanctions, with negative reactions increasing with actual use and disclosure, highlighting social costs of AI reliance.

Contribution

First experimental demonstration of social sanctions against LLM users, revealing how disclosure and actual use influence peer punishment behaviors.

Findings

Participants punished LLM reliance by destroying 36% of earnings.

Punishment increased with actual LLM use, indicating social disapproval.

Disclosure of no LLM use was often met with suspicion and harsher punishment.

Abstract

The rapid spread of large language models (LLMs) has raised concerns about the social reactions they provoke. Prior research documents negative attitudes toward AI users, but it remains unclear whether such disapproval translates into costly action. We address this question in a two-phase online experiment (N = 491 Phase II participants; Phase I provided targets) where participants could spend part of their own endowment to reduce the earnings of peers who had previously completed a real-effort task with or without LLM support. On average, participants destroyed 36% of the earnings of those who relied exclusively on the model, with punishment increasing monotonically with actual LLM use. Disclosure about LLM use created a credibility gap: self-reported null use was punished more harshly than actual null use, suggesting that declarations of "no use" are treated with suspicion. Conversely, at high levels of use, actual reliance on the model was punished more strongly than self-reported reliance. Taken together, these findings provide the first behavioral evidence that the efficiency gains of LLMs come at the cost of social sanctions.