arXiv Machine Learning June 9, 2026 By Manos Plitsis, Giorgos Bouritsas, Vassilis Katsouros, Yannis Panagakis

Exposing Hidden Biases in Text-to-Image Models via Automated Prompt Search

Read the original on arXiv Machine Learning →

arXiv:2512. 08724v3 Announce Type: replace Abstract: Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across sensitive attributes such as gender, race and age.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 15

Aligned but Stereotypical? How System Prompts Shape Demographic Bias in LLM-Based Text-to-Image Models

arXiv:2512. 04981v2 Announce Type: replace-cross Abstract: Text-to-image (T2I) systems increasingly rely on Large Language Model (LLM)-based text conditioning to interpret and expand user prompts.

By NaHyeon Park, Na Min An, Kunhee Kim, Soyeon Yoon, Jiahao Huo, Hyunjung Shim

llms rag diffusion benchmarks safety

arXiv AI

Jun 2

Visual Persuasion: What Influences Decisions of Vision-Language Models?

arXiv:2602. 15278v2 Announce Type: replace-cross Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs).

By Manuel Cherep, Pranav M R, Pattie Maes, Nikhil Singh

llms agents diffusion multimodal safety

arXiv AI

Jun 18

When Cars Have Stereotypes: Auditing Demographic Bias in Objects from Text-to-Image Models

arXiv:2508. 03483v3 Announce Type: replace-cross Abstract: While prior research on text-to-image generation has predominantly focused on biases in human depictions, demographic bias in generated objects remains relatively underexplored.

By Dasol Choi, Jihwan Lee, Minjae Lee, Minsuk Kahng

diffusion benchmarks safety

arXiv Machine Learning

Jun 2

KG-FairDiff: Knowledge Graph-Guided Prompt Refinement for Demographically Fair Text-to-Image Generation

arXiv:2606. 01282v1 Announce Type: cross Abstract: Text-to-Image (TTI) systems are now everyday infrastructure for journalism, education, advertising, and public communication, and the demographic and cultural stereotypes they inherit from training data (rendering women, people of colour, older adults, and non-Western cultures as under-represented or caricatured) become a population-level harm at deployment scale.

By Farbod Davoodi, Seyed Reza Tavakoli Shiyadeh, Pooria Safaei, Sana Harighi, Parsa Gholami, Amirali Amini, Kimia Vanaei, Emad Firoozi, Parham Abed Azad, Babak Khalaj, Siavash Ahmadi, Amir Hossein Payberah, Mohammad Hossein Rohban, Soheil Kolouri, Ali Diba

llms diffusion safety

arXiv Machine Learning

Jun 2

RAIGen: Rare Attribute Identification in Text-to-Image Generative Models

arXiv:2602. 06806v2 Announce Type: replace-cross Abstract: Text-to-image diffusion models achieve impressive generation quality but inherit and amplify training-data biases, skewing coverage of semantic attributes.

By Silpa Vadakkeeveetil Sreelatha, Dan Wang, Serge Belongie, Muhammad Awais, Anjan Dutta

diffusion safety

arXiv AI

Jul 20

Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling

arXiv:2607. 15740v1 Announce Type: cross Abstract: As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI.

By Bo-An Chang, Yu-Chih Chen

llms diffusion nlp reinforcement-learning multimodal benchmarks safety

Related stories

Aligned but Stereotypical? How System Prompts Shape Demographic Bias in LLM-Based Text-to-Image Models

Visual Persuasion: What Influences Decisions of Vision-Language Models?

When Cars Have Stereotypes: Auditing Demographic Bias in Objects from Text-to-Image Models

KG-FairDiff: Knowledge Graph-Guided Prompt Refinement for Demographically Fair Text-to-Image Generation

RAIGen: Rare Attribute Identification in Text-to-Image Generative Models

Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling