Elections & Speech
2 gains2 Traces18 problems
Every measured gain and problem on the record for this topic, newest first.Too few to weigh yet. Updated .
One square per record, darker for stronger evidence:Evidence: 1 high10 moderate11 emerging
22 records so far, too few to weigh yet. Is your study missing? Add it
AI in elections and public speech
A Trace is one record with both a gain and a problem, so it counts on both sides: 4 records show a gain and 20 show a problem. 8 context records are in the record list but not counted, because they report no measured gain or problem.
| Evidence | Gain side | Problem side |
|---|---|---|
| HighStrong study design, backed by at least two independent sources | 1 | 0 |
| ModerateA measured study or an official source | 1 | 9 |
| EmergingEarly, small, unreviewed or reported second-hand | 2 | 11 |
By type of evidence: 8 news reports; 4 investigations by reporters; 4 surveys; 2 interview studies; 1 controlled comparison; 1 benchmark or lab test; 1 commentary or narrative review; 1 observational study. How evidence is scored
22 records so far, too few to weigh yet. A written summary appears once a topic has at least 20 counted records, with at least 5 on each side. Is your study missing? Add it
In a survey of 1,795 Kazakh social media users, those who perceived algorithmic personalization sometimes reported a greater sense of control over their information choices.
In two experiments (280 and 232 people), those who checked a false claim about blood donation or hospice care with generative AI lost more trust in it than those who used a search engine, though effects were short-term.
A comparison of Indonesia, Thailand, the Philippines and Myanmar found AI voter identification, registration and result monitoring streamlined election administration and improved voter-list accuracy.
A study of the 2025 German election found AI-generated posts drew more engagement than non-AI posts, but the boost was equal for major and minor parties, so minor parties gained no competitive advantage.
The same survey found people over-relied on labels: they believed false claims with human-made images more, and believed true claims with labeled AI images less.
Disclosure rules miss spending by consultants, media firms and independent committees, so the bulk of campaign AI use is likely hidden from public view.
18 records · page 2 of 2
Researchers studied whether legally mandated disclosure labels help users avoid deception from AI-generated images. After five focus groups, they surveyed 1,354 participants on how labels changed their judgments of true and false claims illustrated with different image types.
Journal of Applied Learning and Teaching
After Richard Tice posted a photo of an apparent Reform campaign event, critics pointed to five types of artifacts suggesting AI manipulation, including extra fingers, garbled placard text, smeared faces, pixel-perfect…
During the four weeks before the 2025 German federal election, 37 parties' Facebook and Instagram accounts published nearly 1,000 VGenAI images and videos.
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