30.10.2025
©Terzo Algeri/Fotoatelier M/ TUM
Language Shapes Gender Bias in AI Images
TUM News
Alexander Fraser, MCML PI, and his team discovered that AI image generators reproduce gender stereotypes differently across languages. In their study of nine languages, they found that generic prompts like “accountant” mostly produced male images, while explicitly feminine or neutral prompts reduced bias but sometimes affected image quality.
The study highlights that AI is not language‑agnostic and careful wording can influence outcomes, underlining the need for fairness and multilingual awareness in AI systems.
Related
13.11.2025
Explaining AI Decisions: Shapley Values Enable Smart Exosuits
AI meets wearable robotics: MCML and Harvard researchers make exosuits smarter and safer with explainable optimization, presented at ECML-PKDD 2025.
©Gorodenkoff-stock.adobe.com
03.11.2025
Research on Human-Centred Exosuit Technology Highlighted in Börsen-Zeitung
Julian Rodemann worked with Harvard on interpretable algorithms for “Back Exosuits,” improving human–machine interaction.
02.11.2025
MCML at EMNLP 2025
MCML researchers are represented with 39 papers at EMNLP 2025 (18 Main, 13 Findings, and 8 Workshops).
