Training humans to detect AI-generated faces
Source record
dawel-2026-training-ai-faces· 2026 · Proceedings of the National Academy of Sciences (PNAS)
Evidence tier: B (peer-reviewed-journal) · Verification: verified-abstract (compiler confidence: medium)
Bottom line: Training people to notice the global impressions AI faces systematically evoke (rather than artefacts) improved every participant, roughly doubled mean accuracy, and made confidence well calibrated; a test-retest control ruled out practice effects.
Citation
Amy Dawel, Tanya George, Eric Y. Mah, James D. Dunn, Clare A. M. Sutherland, Nick Argument, James W. Tanaka (2026). Training humans to detect AI-generated faces. Proceedings of the National Academy of Sciences (PNAS), 123(27), e2602122123. https://doi.org/10.1073/pnas.2602122123
Verification entry (identifiers, resolvers, claims with source locations): /data/citations.json#dawel-2026-training-ai-faces
Study design
| Field | Value |
|---|---|
| Design | Pre-post training study on untrained test faces; test-retest control study; online replication |
| Modality | image-face |
| Stimuli | AI-generated and real faces (details not in abstract) |
| Participants (N) | 45 |
| N detail | Main pre-post study N=45; control and online replication Ns not in abstract |
| Topics | human-detection, training, confidence, hyperrealism |
Results
| Metric | Value | Note |
|---|---|---|
| Pre-post accuracy | all participants improved; mean accuracy nearly doubled | exact values not in abstract |
| High performers | near-perfect detection after training | |
| Confidence | well calibrated only after training | |
| Controls | test-retest control ruled out practice effects; online replication supported scalability |
Limitations
- Small main sample
- Retention not reported in abstract
- Values not checked against full text
Cited in
- MR-0001: Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
Verification
| Field | Value |
|---|---|
| Status | verified-abstract |
| Compiler confidence | medium |
| Checked on | 2026-10-12 |
| Via | OpenAlex abstract record for doi:10.1073/pnas.2602122123 (publisher page not reachable) |
Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.