Evidence map›Paper›PMID 40821951›Full record

ArticleFrontiers in artificial intelligence2025

AI-driven disinformation: policy recommendations for democratic resilience.

Alexander Romanishyn, Olena Malytska, Vitaliy Goncharuk

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

3 authors.

Alexander RomanishynThink Tank ISE Group, Kyiv, Ukraine.
Olena MalytskaThink Tank ISE Group, Kyiv, Ukraine.
Vitaliy GoncharukThink Tank ISE Group, Kyiv, Ukraine.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The increasing integration of artificial intelligence (AI) into digital communication platforms has significantly transformed the landscape of information dissemination. Recent evidence indicates that AI-enabled tools, particularly generative models and engagement-optimization algorithms, play a central role in the production and amplification of disinformation. This phenomenon poses a direct challenge to democratic processes, as algorithmically amplified falsehoods systematically distort political information environments, erode public trust in institutions, and foster polarization - conditions that degrade democratic decision-making. The regulatory asymmetry between traditional media - historically subject to public oversight - and digital platforms exacerbates these vulnerabilities. This policy and practice review has three primary aims: (1) to document and analyze the role of AI in recent disinformation campaigns, (2) to assess the effectiveness and limitations of existing AI governance frameworks in mitigating disinformation risks, and (3) to formulate evidence-informed policy recommendations to strengthen institutional resilience. Drawing on qualitative analysis of case studies and regulatory trends, we argue for the urgent need to embed AI-specific oversight mechanisms within democratic governance systems. We recommend a multi-stakeholder approach involving platform accountability, enforceable regulatory harmonization across jurisdictions, and sustained civic education to foster digital literacy and cognitive resilience as defenses against malign information. Without such interventions, democratic processes risk becoming increasingly susceptible to manipulation, delegitimization, and systemic erosion.

Indexed as

AIAI regulationdeepfakedisinformationpolicy recommendation

Identifiers

PMID40821951
PMCPMC12351547

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.