How latent and prompting biases in AI-generated historical narratives influence opinions.

Matthew Shu, Daniel Karell, Keitaro Okura, Thomas R Davidson

Abstract read

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

4 authors.

Matthew ShuDepartment of Statistics and Data Science, Yale University, New Haven, CT 06511, USA.ORCID https://orcid.org/0009-0007-7832-2121

Daniel KarellDepartment of Sociology, Yale University, New Haven, CT 06511, USA.ORCID https://orcid.org/0000-0001-6709-6535

Keitaro OkuraDepartment of Sociology, Yale University, New Haven, CT 06511, USA.ORCID https://orcid.org/0000-0001-9061-7490

Thomas R DavidsonDepartment of Sociology, Rutgers University, New Brunswick, NJ 08901, USA.ORCID https://orcid.org/0000-0002-5947-7490

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large language models (LLMs) can be used to persuade people on a range of issues, particularly through user-driven strategies such as personalizing messages and dialogues intended to change minds. However, their capacity to influence opinions through subtle, latent ideological framing remains relatively understudied. We investigate whether AI-generated historical summaries affect social and political opinions through a preregistered experiment (

Indexed as

AI framingAI historyAI persuasiongenerative artificial intelligencelarge language models

Identifiers

PMID41783460

DOI10.1093/pnasnexus/pgag022

PMCPMC12954675

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC

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.