arXiv AI July 3, 2026 By Phillip Howard, Xin Su, Kathleen C. Fraser

Cross-Cultural Value Attribution in Large Vision-Language Models

arXiv:2604. 09945v2 Announce Type: replace-cross Abstract: The rapid adoption of large vision-language models (LVLMs) in recent years has been accompanied by growing fairness concerns due to their propensity to reinforce harmful societal stereotypes.

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 AI

Jun 4

Culturally Grounded Personas in Large Language Models: Characterization and Alignment with Socio-Psychological Value Frameworks

arXiv:2601. 22396v2 Announce Type: replace-cross Abstract: Despite the growing utility of Large Language Models (LLMs) for simulating human behavior, the extent to which these synthetic personas accurately reflect world and moral value systems across different cultural conditionings remains uncertain.

By Candida M. Greco, Lucio La Cava, Andrea Tagarelli

llms 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

arXiv Machine Learning

Jul 31

STEREODISCO: Discovering Stereotypicality in LLMs

arXiv:2607. 27824v1 Announce Type: cross Abstract: LLMs encode, convey, and perpetuate stereotypes.

By Farane Jalali Farahani, Corina Dima, Mojtaba Nayyeri, Raphael H. Heiberger, Steffen Staab

llms rag

arXiv Computer Vision

Sep 1

Guardrail-Agnostic Societal Bias Evaluation in Large Vision-Language Models

arXiv:2608.29590v1 Announce Type: new Abstract: We propose a societal bias evaluation method for large vision-language models (LVLMs) in the era of strong safety guardrails. Existing benchmarks rely...

By Yusuke Hirota, Michael Ross Boone, Arun George Zachariah, Jibin Rajan Varghese, Yu-Chiang Frank Wang, Boyi Li, Ryo Hachiuma

llms multimodal benchmarks safety

arXiv Machine Learning

Sep 3

FairLens: Benchmarking Fairness in Vision-Language Models for High-Stakes Decision-Making

FairLens is a benchmark and evaluation framework that measures fairness and validity of vision‑language models (VLMs) in high‑stakes domains such as hiring, legal, and healthcare. It uses over 100,000 face‑image and question pairs covering gender, race, and age, and assesses responses through demographic parity, soundness, demographic association, and bias in free‑text generation. The study finds that VLMs often make unwarranted inferences from faces rather than abstaining, especially in legal and healthcare contexts, and that small parity gaps can still hide unsafe treatment across groups.

By Vahid Reza Khazaie, Ahmed Y. Radwan, Shaina Raza

llms multimodal benchmarks safety

arXiv AI

Aug 13

Large Language Models Reproduce Racial Stereotypes When Used for Text Annotation

arXiv:2603. 13891v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used for automated text annotation in tasks ranging from academic research to content moderation and hiring.

By Petter T\"ornberg

llms fine-tuning safety

One email a morning, machine-written

Related stories

Culturally Grounded Personas in Large Language Models: Characterization and Alignment with Socio-Psychological Value Frameworks

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

STEREODISCO: Discovering Stereotypicality in LLMs

Guardrail-Agnostic Societal Bias Evaluation in Large Vision-Language Models

FairLens: Benchmarking Fairness in Vision-Language Models for High-Stakes Decision-Making

Large Language Models Reproduce Racial Stereotypes When Used for Text Annotation