Uncurated image-text datasets: Shedding light on demographic bias

Abstract

The increasing tendency to collect large and uncurated datasets to train vision-and-language models has raised concerns about fair representations. It is known that even small but manually annotated datasets, such as MSCOCO, are affected by societal bias. This problem, far from being solved, may be getting worse with data crawled from the Internet without much control. In addition, the lack of tools to analyze societal bias in big collections of images makes addressing the problem extremely challenging. Our first contribution is to annotate part of the Google Conceptual Captions dataset, widely used for training vision-and-language models, with four demographic and two contextual attributes. Our second contribution is to conduct a comprehensive analysis of the annotations, focusing on how different demographic groups are represented. Our last contribution lies in evaluating three prevailing vision-and-language tasks: image captioning, text-image CLIP embeddings, and text-to-image generation, showing that societal bias is a persistent problem in all of them.

Publication
Proc.~IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Noa Garcia
Noa Garcia
Specially-Appointed Assistant Professor

Her research interests lie in computer vision and machine learning applied to visual retrieval and joint models of vision and language for high-level understanding tasks.

Yusuke Hirota
Yusuke Hirota
PhD Student
Yankun Wu
Yankun Wu
PhD Student
Yuta Nakashima
Yuta Nakashima
Professor

Yuta Nakashima is a professor with Institute for Datability Science, Osaka University. His research interests include computer vision, pattern recognition, natural langauge processing, and their applications.