# Imagining the Far East: Exploring Perceived Biases in AI-Generated Images of East Asian Women

arXiv: https://arxiv.org/abs/2504.04865 · HTML: https://mlsanity.com/paper/2504.04865/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2025.

## Built on by (most cited first, 3 of 3)

- [Aesthetics as Structural Harm: Algorithmic Lookism Across Text-to-Image Generation and Classification](https://mlsanity.com/paper/2601.11651/) (2026)
- [An Exploration of Default Images in Text-to-Image Generation](https://mlsanity.com/paper/2505.09166/) (2025)
- [SafeGen: Embedding Ethical Safeguards in Text-to-Image Generation](https://mlsanity.com/paper/2512.12501/) (2025)

## Abstract (arXiv)

Image-generating AI, which allows users to create images from text, is increasingly used to produce visual content. Despite its advancements, cultural biases in AI-generated images have raised significant concerns. While much research has focused on issues within Western contexts, our study examines the perceived biases regarding the portrayal of East Asian women. In this exploratory study, we invited East Asian users to audit three popular models (DALL-E, Midjourney, Stable Diffusion) and identified 18 specific perceived biases, categorized into four patterns: Westernization, overuse or misuse of cultural symbols, sexualization & feminization, and racial stereotypes. This work highlights the potential challenges posed by AI models in portraying Eastern individuals.