ArticleiScience2025
AI's assigned gender affects human-AI cooperation.
Article in iScience, 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.
- Collective predictive coding as model of science: formalizing scientific activities towards generative science.Royal Society open science · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
Cooperation between humans and machines is increasingly vital as artificial intelligence (AI) becomes integrated into daily life. Research shows that people are often less willing to cooperate with AI agents than with humans and are more likely to exploit AI for personal gain. While prior studies indicate that human-like features in AI influence cooperation, the impact of AI's assigned gender remains underexplored. This study investigates how cooperation varies with the gender labels assigned to AI partners. In a Prisoner's Dilemma game, 402 participants interacted with partners labeled as AI or human, and as male, female, non-binary, or gender-neutral. Participants exploited female-labeled and distrusted male-labeled AI agents more than human counterparts with the same gender labels, reflecting gender biases similar to those in human-human interactions. These findings underscore the importance of accounting for gender bias in AI design, policy, and regulation.
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Registered trials
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.