News from Generative Artificial Intelligence is Believed Less
Bibliographic record
Abstract
Artificial Intelligence (AI) algorithms are now able to produce text virtually indistinguishable from text written by humans across a variety of domains. A key question, then, is whether people believe content from AI as much as content from humans. Trust in the (human generated) news media has been decreasing over time and AI is viewed as lacking human desires, and emotions, suggesting that AI news may be viewed as more accurate. Contrary to this, two preregistered experiments conducted on representative U.S. samples (combined N = 4,034) showed that people rated news produced by AI as being less accurate than news produced by humans. When news items were tagged as produced by AI (compared to a human), people were more likely to incorrectly rate them as inaccurate when they were actually true, and more likely to correctly rate them as inaccurate when they were indeed false. These results were robust to experimental paradigm (separate and joint evaluations), news item (actual veracity, age), and several respondent characteristics (e.g., political orientation). This effect is particularly important given the increasing use of AI algorithms in news production, and the associated ethical and governance pressures to disclose their use.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".