ArticleAI and ethics2025
Auditing and instructing text-to-image generation models on fairness.
Article in AI and ethics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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Who cites it
6 citing papers in PubMed.
- Users prefer generative AI to reflect real over idealized gender proportions.Scientific reports · 2026Article
- How Good Is the Machine at the Imitation Game? On Stylistic Characteristics of AI-Generated Images.Journal of imaging · 2025Article
- Assessing the quality and educational applicability of AI-generated anterior segment images in ophthalmology.Scientific reports · 2025Article
- AI-generated faces influence gender stereotypes and racial homogenization.Scientific reports · 2025Article
- Ethical-Lens: Curbing malicious usages of open-source text-to-image models.Patterns (New York, N.Y.) · 2025Article
- Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models.Transactions on machine learning research · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative AI models have recently achieved astonishing results in quality and are consequently employed in a fast-growing number of applications. However, since they are highly data-driven, relying on billion-sized datasets randomly scraped from the internet, they also suffer from degenerated and biased human behavior, as we demonstrate. In fact, they may even reinforce such biases. To not only uncover but also combat these undesired effects, we present a novel strategy, called Fair Diffusion, to attenuate biases during the deployment of generative text-to-image models. Specifically, we demonstrate shifting a bias in any direction based on human instructions yielding arbitrary proportions for, e.g., identity groups. As our empirical evaluation demonstrates, this introduced control enables instructing generative image models on fairness, requiring no data filtering nor additional training.
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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.