ArticleScientific reports2025
Inequity aversion toward AI counterparts.
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.
What it found
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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.
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.
Who cites it
1 citing paper in PubMed.
- Identifying conceptual dimensions of trust in artificial intelligence from qualitative content analysis of open-ended responses.Discover artificial intelligence · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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.
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Registered trials
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