arXiv AI June 18, 2026 By Dasol Choi, Jihwan Lee, Minjae Lee, Minsuk Kahng

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Computer Vision

Sep 22

IMPLICIT-Bench: Measuring Implicit Bias in Text-to-Image Models under Neutral Prompts

arXiv:2609.24228v1 Announce Type: new Abstract: Text-to-image (T2I) models are typically evaluated for bias using slot-based templates such as ``a photo of a [profession]''. Such templates probe only...

By Yue Dai, Ziyang Liu, Marc Cheong, Caren Han

diffusion benchmarks 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 Machine Learning

Jun 9

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

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.

By Manos Plitsis, Giorgos Bouritsas, Vassilis Katsouros, Yannis Panagakis

llms diffusion benchmarks safety

arXiv AI

Sep 1

Vision-Language Models Suppress Female Representations Under Ambiguous Input

arXiv:2605.31556v2 Announce Type: replace-cross Abstract: Alignment teaches vision-language models (VLMs) to avoid expressing demographic biases, and when gender is clearly visible they largely succe...

By Arnau Marin-Llobet, Simon Henniger, Mahzarin R. Banaji

llms rag multimodal benchmarks safety

arXiv AI

Jun 2

Prototypicality Bias Reveals Blindspots in Multimodal Evaluation Metrics

arXiv:2601. 04946v3 Announce Type: replace-cross Abstract: Automatic metrics are widely used to evaluate text-to-image models, often replacing human judgment in benchmarking, model selection, and large-scale data filtering.

By Subhadeep Roy, Gagan Bhatia, Steffen Eger

rag diffusion multimodal benchmarks safety

arXiv AI

Aug 25

Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding

arXiv:2608.21415v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases fro...

By Yisong Xiao, Aishan Liu, Yongxin Huang, Zonghao Ying, Shiji Zhao, Tianlin Li, Yong Han, Jian Yang, Xianglong Liu

llms multimodal benchmarks safety

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Related stories

IMPLICIT-Bench: Measuring Implicit Bias in Text-to-Image Models under Neutral Prompts

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

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

Vision-Language Models Suppress Female Representations Under Ambiguous Input

Prototypicality Bias Reveals Blindspots in Multimodal Evaluation Metrics

Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding