Survey of Social Bias in Vision-Language Models
Nayeon Lee Yejin Bang Holy Lovenia Samuel Cahyawijaya Wenliang Dai Pascale Fung
In recent years, the rapid advancement of machine learning (ML) models,
particularly transformer-based pre-trained models, has revolutionized Natural
Language Processing (NLP) and Computer Vision (CV) fields. However, researchers
have discovered that these models can inadvertently capture and reinforce
social biases present in their training datasets, leading to potential social
harms, such as uneven resource allocation and unfair representation of specific
social groups. Addressing these biases and ensuring fairness in artificial
intelligence (AI) systems has become a critical concern in the ML community.
  The recent introduction of pre-trained vision-and-language (VL) models in the
emerging multimodal field demands attention to the potential social biases
present in these models as well. Although VL models are susceptible to social
bias, there is a limited understanding compared to the extensive discussions on
bias in NLP and CV. This survey aims to provide researchers with a high-level
insight into the similarities and differences of social bias studies in
pre-trained models across NLP, CV, and VL. By examining these perspectives, the
survey aims to offer valuable guidelines on how to approach and mitigate social
bias in both unimodal and multimodal settings. The findings and recommendations
presented here can benefit the ML community, fostering the development of
fairer and non-biased AI models in various applications and research endeavors.