6 papers
VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech
Yi-Cheng Lin, Yusuke Hirota, Sung-Feng Huang +1
Large Audio-Language Models (LALMs) are increasingly integrated into daily applications, yet their generative biases remain underexplored. Existing speech fairness benchmarks rely…
Interpretable Debiasing of Vision-Language Models for Social Fairness
Na Min An, Yoonna Jang, Yusuke Hirota +3
The rapid advancement of Vision-Language models (VLMs) has raised growing concerns that their black-box reasoning processes could lead to unintended forms of social bias. Current d…
Bias in Gender Bias Benchmarks: How Spurious Features Distort Evaluation
Yusuke Hirota, Ryo Hachiuma, Boyi Li +9
Gender bias in vision-language foundation models (VLMs) raises concerns about their safe deployment and is typically evaluated using benchmarks with gender annotations on real-worl…
From Global to Local: Social Bias Transfer in CLIP
Ryan Ramos, Yusuke Hirota, Yuta Nakashima +1
The recycling of contrastive language-image pre-trained (CLIP) models as backbones for a large number of downstream tasks calls for a thorough analysis of their transferability imp…
LOTUS: A Leaderboard for Detailed Image Captioning from Quality to Societal Bias and User Preferences
Yusuke Hirota, Boyi Li, Ryo Hachiuma +7
Large Vision-Language Models (LVLMs) have transformed image captioning, shifting from concise captions to detailed descriptions. We introduce LOTUS, a leaderboard for evaluating de…
SANER: Annotation-free Societal Attribute Neutralizer for Debiasing CLIP
Yusuke Hirota, Min-Hung Chen, Chien-Yi Wang +3
Large-scale vision-language models, such as CLIP, are known to contain societal bias regarding protected attributes (e.g., gender, age). This paper aims to address the problems of…