Evidence map›Paper›PMID 42552796›Full record

ArticleRisk analysis : an official publication of the Society for Risk Analysis2026

Mapping Human-AI Teaming in Risk Analysis: Role Evolution, Thematic Landscape, and Governance Mechanisms From News Media.

Cong Cheng, Jian Dai, Lulu Yan

Abstract read
In one paragraph

Article in Risk analysis : an official publication of the Society for Risk Analysis, 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

3 authors.

Cong ChengSchool of Management, Zhejiang University of Technology, Hangzhou, China.ORCID https://orcid.org/0000-0001-9486-6750
Jian DaiSchool of Management, Zhejiang University of Technology, Hangzhou, China.ORCID https://orcid.org/0000-0002-5869-9446
Lulu YanSchool of Management, Zhejiang University of Technology, Hangzhou, China.ORCID https://orcid.org/0009-0003-2411-2429

Funding

China Postdoctoral Science Foundation 2025M780670China Postdoctoral Science Foundation 2026T190188Doctoral Student Program of the Young S&T Talents Cultivation Project, CASTNational Natural Science Foundation of China 72342029Postdoctoral Fellowship Program of CPSF GZC20260374
6 · The paper itself

Abstract

Human-artificial intelligence (AI) teams are increasingly embedded in risk analysis, yet news reports repeatedly portray meaningful oversight as fragile when review conditions are poorly designed. This study examines how public discourse has portrayed the roles, failure modes, and governance mechanisms of human-AI teaming in risk analysis. Drawing on 184,282 AI-related Factiva news articles (1956-2025), we use a funnel-shaped mixed-methods design to identify 3571 articles describing human-AI interaction in risk-analytic contexts. Structural topic modeling identifies 20 thematic clusters and their temporal and cross-domain dynamics, whereas LLM-assisted inductive thematic analysis produces five mirror-mapped pairs of failure modes and governance mechanisms: opacity and explainability, bias and auditing, agency erosion and cognitive friction, systemic fragility and oversight and liability, and accountability vacuums and institutional governance. The analysis shows that media discourse often follows an AI-predicts-human-decides paradigm and portrays formal human-in-the-loop requirements as insufficient without deliberate interaction design.

Indexed as

Artificial IntelligenceMass MediaHumansRisk Assessmentgovernance mechanismhuman–AI teamingmedia discourserisk analysisstructural topic modeling

Identifiers

PMID42552796
PMCPMC13438684

What OpenQuestion holds

Textmetadata
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Registered trials

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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.