Evidence map›Paper›PMID 42857525›Full record

ArticlePNAS nexus2026

Perceived political bias in large language models reduces persuasive abilities.

Matthew DiGiuseppe, Joshua Robison

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PNAS nexus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Matthew DiGiuseppeInstitute of Political Science, Leiden University, Wassenaarseweg 52, Leiden 2333 AK, South Holland, The Netherlands.ORCID https://orcid.org/0000-0003-0823-0436
Joshua RobisonInstitute of Political Science, Leiden University, Wassenaarseweg 52, Leiden 2333 AK, South Holland, The Netherlands.ORCID https://orcid.org/0000-0002-7472-2247

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conversational AI has been proposed as a scalable way to correct public misconceptions, yet its effectiveness may depend on perceptions of political neutrality. As large language models become objects of partisan conflict, political elites increasingly portray them as ideologically aligned. We test whether such credibility attacks reduce persuasion by conversational AI. In a preregistered US survey experiment (

Indexed as

AIlarge language modelspartisan biaspolitical persuasionpublic opinion

Identifiers

PMID42857525
PMCPMC13650708

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.