🤖 AI Summary
This study investigates whether negative social attitudes toward users of large language models (LLMs) translate into tangible punitive behavior. Employing a two-stage online experiment, participants were given the opportunity to sacrifice their own earnings to reduce the rewards of others based on whether they used or refrained from using an LLM to complete a task, thereby quantifying the intensity of social sanctions. The research provides the first behavioral evidence that, despite enhancing task efficiency, LLM use elicits significant social punishment; moreover, disclosing LLM usage exacerbates credibility biases, leading to misjudgments and excessive penalties. Findings reveal that pure LLM users had, on average, 36% of their earnings actively destroyed, with punishment intensity increasing monotonically with actual usage levels. Individuals who misrepresented their LLM usage faced even harsher sanctions.
📝 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.