How Humans and AI Detect Deepfakes Differently: Complementary Strategies for Human-AI Teaming
Source record
chen-2026-humans-ai-deepfake-teaming· 2026 · Proceedings of the Human Factors and Ergonomics Society Annual Meeting
Evidence tier: B (peer-reviewed-conference) · Verification: verified-abstract (compiler confidence: medium)
Bottom line: Humans and AI detectors relied on different cues; eye-tracking and EEG suggested holistic, flexible human viewing that catches some inconsistencies AI misses, and people with more AI exposure matched or exceeded AI detectors in some contexts (abstract; no numbers reported).
Citation
Xinyi Chen, et al. (2026). How Humans and AI Detect Deepfakes Differently: Complementary Strategies for Human-AI Teaming. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. https://doi.org/10.1177/10711813261487968
Verification entry (identifiers, resolvers, claims with source locations): /data/citations.json#chen-2026-humans-ai-deepfake-teaming
Study design
| Field | Value |
|---|---|
| Design | Eye-tracking and EEG comparison of human and AI attention allocation in deepfake detection, across levels of AI literacy |
| Modality | video |
| Stimuli | Deepfake media manipulating lip movements, facial expressions and voices (details not in abstract) |
| Participants (N) | not recorded |
| N detail | N not reported in the abstract; OpenAlex lists Jing Yang (University at Buffalo) as last author; full author list not confirmed |
| Topics | human-detection, human-ai-teaming, neural |
Results
| Metric | Value | Note |
|---|---|---|
| Cue use | humans and AI rely on different cues and reasoning strategies | qualitative |
| Human vs AI detector | people with more AI exposure may match or exceed AI detectors in some contexts | no figures in abstract |
| Processing style | holistic, flexible visual processing | eye-tracking and EEG evidence |
Moderators
- AI literacy / AI exposure
Limitations
- Abstract reports no N or effect sizes
- Short proceedings paper
- Full text not read (closed access)
Cited in
- MR-0001: Human Detection of Deepfakes: Accuracy, Bias, Confidence, and Training
Verification
| Field | Value |
|---|---|
| Status | verified-abstract |
| Compiler confidence | medium |
| Checked on | 2026-10-19 |
| Via | OpenAlex abstract record W7221715535 for doi:10.1177/10711813261487968 |
Values on this page have not all been checked against the full text. Confirm them against the primary source before high-stakes use.