Echoes of Doubt: Exposure to Information About Generative AI Decreases Believability of News
Bibliographic record
Abstract
The emergence of generative artificial intelligence (GenAI) has sparked a debate about its potential misuse for creating political disinformation. However, the effects of providing information about generative AI on disinformation perceptions remain unclear. We fill this gap by testing the impact of GenAI literacy interventions on truth discrimination (i.e., the ability to accurately distinguish between genuine and false online news) and deception bias (i.e., the tendency to believe that online news is false) in an online experiment among 897 Canadian adults. Respondents were randomly assigned to a GenAI literacy intervention (explainer videos), showing how ChatGPT and Midjourney can be used to create political disinformation vs. art. The GenAI interventions increased participants’ propensity to classify online news as false, yet signal detection analyses showed no improvement in truth discrimination. In addition, we find evidence for a deception bias where participants have a slight tendency to judge online news as false rather than true. We conclude that GenAI literacy interventions need to be carefully crafted to avoid further undermining the believability of genuine news.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".