Evidence map›Paper›PMID 41162527›Full record

ArticleScientific reports2025

Inequity aversion toward AI counterparts.

Debanjan Borthakur, Peter Diep, Jason E Plaks

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Debanjan BorthakurDepartment of Psychology, University of Toronto, Toronto, Canada. debanjan.borthakur@utoronto.ca.
Peter DiepDepartment of Psychology, University of Waterloo, Waterloo, Canada.
Jason E PlaksDepartment of Psychology, University of Toronto, Toronto, Canada.

Funding

This work is partially funded by grants from the Natural Sciences and Engineering Research Council of Canada (fund # 508977) and the Schwartz-Reisman Institute for Technology and Society (SRI- VPRI). 508977
6 · The paper itself

Abstract

Human moral interactions often assume that resources should be allocated equitably, i.e., one should not take more than one's fair share. To what extent do people apply this assumption to social AI entities? Using a 21-round Ultimatum Game, we investigated participants' behavioral, physiological, and affective responses to fair, disadvantageous, and advantageous offers from an AI (vs. human) counterpart. We report three principal findings: (a) Participants were more likely to reject disadvantageous offers from an AI counterpart than from a human counterpart, but were more likely to reject advantageous offers from a human counterpart than from an AI counterpart; (b) Participants reported more negative affect following disadvantageous offers from an AI counterpart than from a human counterpart; (c) Participants exhibited a stronger association between heart rate variability and rejection rate for disadvantageous offers from an AI counterpart than from a human counterpart. Based on these findings, we propose a model emphasizing an important, previously under-examined role of self-regulatory processes in humans' responses toward AI moral behavior.

Indexed as

Artificial IntelligenceMoralsAdultFemaleGames, ExperimentalHeart RateHumansMaleYoung Adult

Identifiers

PMID41162527
PMCPMC12572205

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.