At a glance
- Source
- Reif, Larrick and Soll (2025). Evidence of a social evaluation penalty for using AI. Proceedings of the National Academy of Sciences, 122(19), e2426766122. doi:10.1073/pnas.2426766122
- How it was done
- Four online experiments, registered in advance. Participants were assigned by chance to conditions in which a worker did or did not use AI, and either predicted how they would be judged or judged others. Randomised experiment. People were assigned by chance to work with or without the tool, and the two groups were compared.
- Who took part
- 4,439 participants across four experiments.
- Where
- Online experiments run by researchers at Duke University's Fuqua School of Business, United States.
“Although AI can enhance productivity, its use carries social costs.”
What it found
- People expect the penalty. Workers expected colleagues to rate them as less competent and less motivated if they used AI.
- The penalty is real. Evaluators did rate people who used AI as less competent and less motivated.
- It reaches hiring. The same judgements affected how evaluators assessed job candidates.
When the result holds
A worker who expects to be judged for using AI has a reason to use it quietly. A reviewer who does not know AI was used cannot check for the mistakes AI tends to make.
Limits
- The studies are online experiments, not observation of real workplaces.
- They do not measure how often people hide their AI use.
Practices that use this study
This study supports:
- Protect psychological safety during an AI rollout AI projects can make staff afraid to speak up and unwilling to share what they know. Say early what AI means for jobs, and consult the people affected. Do first · Effort: medium (weeks, a few people)
- Write a one-page AI-use policy staff will actually read Put the rules for AI on one page: which tools are allowed, what must never be typed in, who checks the output, and who to ask. Then build the most important rule into the work itself, because a page alone gets ignored. Do first · Effort: low (days, one person)