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TRUVACE RECORD VERSION
record: TRV-2026-2705
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T17:11:58.194530Z
status: published
lens: p_space
sector: business
headline: When AI says who made it: A meta-analysis of AI disclosure effects on consumer responses
dek: Artificial intelligence (AI) is increasingly embedded in digital communications, yet the behavioral consequences of disclosing AI authorship remain inconsistent across studies. This meta-analysis synthesizes 21 experimental effect sizes (N = 13,582) to estimate the effect of AI disclosure on human behavioral responses—trust, attitudes, and behavioral intentions—and to identify the conditions under which it is strongest. Using a DerSimonian–Laird random-effects model, the pooled effect is significant and negative…
gain_title: (none)
problem_title: A meta-analysis of 21 experiments with 13,582 participants found that disclosing AI authorship reduced trust, attitudes and behavioral intentions (Hedges' g = −0.440), with every experiment pointing the same way.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: A meta-analysis of 21 experiments with 13,582 participants found that disclosing AI authorship reduced trust, attitudes and behavioral intentions (Hedges' g = −0.440), with every experiment pointing the same way.
problem_evidence: the pooled effect is significant and negative (Hedges' g = −0.440, 95% CI [−0.532, −0.348]), with complete directional consistency across all 21 effect sizes
quick_read: A meta-analysis pooled 21 experimental effect sizes from studies involving 13,582 people to test what happens when content is labeled as AI-made. Disclosure lowered trust, attitudes and behavioral intentions, with a pooled effect of Hedges' g = −0.440 (95% CI −0.532 to −0.348), and all 21 effect sizes went in the negative direction.

The penalty depended on the kind of task. In subjective settings such as advertising, service and chatbot communication the effect was g = −0.522, compared with g = −0.216 in objective informational settings such as news labeling. That difference was statistically significant (p = .004).

Results varied a lot between studies (I² = 80.5%), though effects within objective contexts were highly consistent (I² = 0.0%). The authors say the findings point to a task-dependent model of AI disclosure, with implications for how platforms design transparency labels and govern AI content.
limitation: Results varied substantially across the pooled studies (I² = 80.5%), and the apparent link between sample size and effect size was confounded with task context, so the moderator findings rest on a small set of 21 effect sizes.
tag: Evidence-backed problem
key_points: The analysis combined 21 experimental effect sizes from studies with 13,582 participants in total. | The pooled effect of AI disclosure on trust, attitudes and behavioral intentions was g = −0.440, with a 95% confidence interval of −0.532 to −0.348. | All 21 effect sizes pointed in the same negative direction. | Effects were stronger in subjective contexts such as advertising, service and chatbots (g = −0.522) than in objective ones such as news labeling (g = −0.216). | Heterogeneity across studies was substantial overall (I² = 80.5%) but 0.0% among objective-context studies. | Larger samples were linked to bigger effects in a single-variable analysis, but this vanished when task context was modeled alongside it, because large studies clustered in objective domains. | The authors tie the results to the persuasion knowledge model, domain ownership threat theory and the machine heuristic.
rundown: The researchers used a DerSimonian–Laird random-effects model to combine 21 experimental effect sizes covering trust, attitudes and behavioral intentions. The pooled result was a significant negative effect of AI disclosure (g = −0.440), and no individual effect size went the other way.

Because results differed widely between studies (I² = 80.5%), the authors ran moderator analyses. Task subjectivity was the main moderator: subjective contexts such as advertising, service and chatbot communication had a stronger penalty (g = −0.522) than objective informational contexts such as news labeling (g = −0.216; Q_between = 8.27, p = .004).

Sample size appeared linked to effect size in a single-variable test, but large-sample studies were concentrated in objective domains. When both factors were modeled together the link was no longer significant, so the authors read it as a sign of contextual differences rather than a methodological artifact. They say the results support a task-dependent approach to transparency design and platform governance.
sources:
- peer_reviewed | Computers in Human Behavior Reports | https://doi.org/10.1016/j.chbr.2026.101345 | 2026-09-23
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