arXiv (All)
open access
Newer Is Not Fairer: Gender Stereotyping in Text-to-Image AI Across Model Generations
Text-to-image generative models are widely used in professional and creative settings, yet how they represent gender across occupations -- and whether newer models are fairer -- remains poorly understood across multiple generations. We evaluate gender representation across 20 occupations, 5 prompt t
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Mitigate One, Skew Another? Tackling Intersectional Biases in Text-to-Image Models#75240
- Sex and gender bias in large language models: an old problem at a new scale.#1167028
- Human-Centric Image Captioning with Subject-Centered Spatial Understanding#985668
- Gender Bias in Vision-Language In-Context Learning#1064226
- How Far Do Simple Transformations Translate Across Text Embedding Models?#924634
Record · ID 1011717
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive