ArticleScientific reports2026
Users prefer generative AI to reflect real over idealized gender proportions.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Generative artificial intelligence (GAI) systems must make decisions about how to represent gender in images of social categories. Different standards exist for evaluating whether such representations are appropriate: systems can reflect current statistical distributions (proportional equality), depict equal proportions (uniform equality), or actively promote diversity (substantive equality). While different authors apply different standards, what constitutes an appropriate representation from users' perspective is rarely investigated systematically. Understanding user expectations is crucial. Our study therefore investigates users' evaluations of gender representation in various social categories. In an experiment with 719 US participants, we find that uniform equality and substantive equality play little role in user evaluations. Users prefer a representation that reflects their perception of current reality. Our results show that users prefer statistical alignment.
Indexed as
Identifiers
What OpenQuestion holds
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.