aceEvidence of a social evaluation penalty for using AI
Despite the rapid proliferation of AI tools, we know little about how people who use them are perceived by others. Drawing on theories of attribution and impression management, we propose that people believe they will be evaluated negatively by others for using AI tools and that this belief is justified. We examine these predictions in four preregistered experiments (N = 4,439) and find that people who use AI at work anticipate and receive negative evaluations regarding their competence and motivation. Further,…
People who use AI at work receive negative social evaluations about their competence and motivation, which can harm job candidate assessments.
Evidence
- Peer-reviewedProceedings of the National Academy of Sciences2025-05-08
How should this claim be treated?
Truvace Impact Record TRV-2026-0525, v1: “Evidence of a social evaluation penalty for using AI.” Truvace, 2026-07-24. /record/TRV-2026-0525 (accessed at citation time). sha256 ce05843da9f71b27…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0525 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.