arXiv AI

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

arXiv AI

Jul 31

BioPro: Towards Difference-Aware Gender Fairness for Vision-Language Models

arXiv:2512. 00807v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) inherit significant social biases from their training data, notably in gender representation.

By Yujie Lin, Jiayao Ma, Qingguo Hu, Wenbo Li, Genji Li, Derek Wong, Jinsong Su

llms rag diffusion multimodal safety

arXiv AI

Jul 24

Unlearning Under Imbalance: Benchmarking Fairness in Multimodal LLM Unlearning

arXiv:2607. 21300v1 Announce Type: cross Abstract: Machine unlearning has emerged as a tool for removing personal data from trained models to comply with recent AI regulations.

By Lorenzo Orsingher, Thomas De Min, Massimiliano Mancini, Davide Talon, Elisa Ricci

llms nlp fine-tuning multimodal benchmarks safety

arXiv AI

1d ago

BiasGym: A Simple and Generalizable Framework for Analyzing and Removing Biases through Injection

arXiv:2508.08855v5 Announce Type: replace-cross Abstract: Understanding biases and stereotypes encoded in the weights of Large Language Models (LLMs) is crucial for developing effective mitigation st...

By Sekh Mainul Islam, Nadav Borenstein, Siddhesh Milind Pawar, Haeun Yu, Arnav Arora, Isabelle Augenstein

llms fine-tuning safety

arXiv Computer Vision

2d ago

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 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 AI

Jun 24

When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models

arXiv:2605. 08245v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate, confidently describing content not present in the input.

By Harshvardhan Saini, Samyak Jha, Yiming Tang, Dianbo Liu

llms rag agents fine-tuning multimodal benchmarks safety

arXiv Machine Learning

Aug 10

Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs

arXiv:2509. 16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities.

By Mina Arzaghi, Alireza Dehghanpour Farashah, Florian Carichon, Jean-Fran\c{c}ois Plante, Golnoosh Farnadi

llms rag fine-tuning benchmarks safety

arXiv AI

Jul 3

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.

By Phillip Howard, Xin Su, Kathleen C. Fraser

llms multimodal 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

Aug 10

Calibrating WEAT Against Anisotropy: ZCA Whitening as a Geometric Pre-Processing Step for Embedding Association Tests

arXiv:2608. 06908v1 Announce Type: cross Abstract: We propose Zero-phase Component Analysis (ZCA) whitening as a geometric pre-processing step for the Word Embedding Association Test (WEAT).

By Seitaro Ono, Senna Ross, Jun Saiki

llms rag nlp benchmarks safety

arXiv Computation and Language

2d ago

ContextBias: Controlled Evaluation of Bias Persistence Under Context Shift in Text-to-Image Models

arXiv:2608.29847v1 Announce Type: cross Abstract: Text-to-image models learn associations between concepts - in the case of this paper, people's professions, which we refer to as roles - and visual a...

By Shaghayegh Kolli, Sina Emami, Moreno D'Inc\`a, Pouyan Nejadi, Nicu Sebe, Massimiliano Mancini, Jana Diesner

diffusion benchmarks 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

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