Evidence map›Paper›PMID 40730578›Full record

ArticleNature communications2025

Heterogeneous preferences and asymmetric insights for AI use among welfare claimants and non-claimants.

Mengchen Dong, Jean-François Bonnefon, Iyad Rahwan

Abstract read
In one paragraph

Article in Nature communications, 2025. 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.

Mengchen DongCenter for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. dong@mpib-berlin.mpg.de.ORCID http://orcid.org/0000-0001-8547-3808
Jean-François BonnefonDepartment of Social and Behavioral Sciences, Toulouse School of Economics, Centre National de la Recherche Scientifique (Toulouse School of Management Research), University of Toulouse Capitole, Toulouse, France.ORCID http://orcid.org/0000-0002-4959-188X
Iyad RahwanCenter for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany.ORCID http://orcid.org/0000-0002-1796-4303

Funding

Agence Nationale de la Recherche (French National Research Agency) ANR-17-EURE-0010Agence Nationale de la Recherche (French National Research Agency) ANR-19-P13A-0004
6 · The paper itself

Abstract

The deployment of AI in welfare benefit allocation accelerates decision-making but has led to unfair denials and false fraud accusations. In the US and UK (N = 3249), we examine public acceptability of speed-accuracy trade-offs among claimants and non-claimants. While the public generally tolerates modest accuracy losses for faster decisions, claimants are less willing to accept AI in welfare systems, raising concerns that using aggregate data for calibration could misalign policies with the preferences of those most affected. Our study further uncovers asymmetric insights between claimants and non-claimants. Non-claimants overestimate claimants' willingness to accept speed-accuracy trade-offs, even when financially incentivized for accurate perspective-taking. This suggests that policy decisions aimed at supporting vulnerable groups may need to incorporate minority voices beyond popular opinion, as non-claimants may not easily understand claimants' perspectives.

Indexed as

Social WelfareAdultDecision MakingFemaleHumansMaleMiddle AgedPublic OpinionUnited KingdomUnited States

Identifiers

PMID40730578
PMCPMC12307907

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.