Gender and Racial Bias in Visual Question Answering Datasets

Yusuke Hirota, Yuta Nakashima, Noa Garcia

Abstract:

Vision-and-language tasks have increasingly drawn more attention
as a means to evaluate human-like reasoning in machine learning
models. A popular task in the field is visual question answering
(VQA), which aims to answer questions about images. However,
VQA models have been shown to exploit language bias by learning
the statistical correlations between questions and answers without looking into the image content: e.g., questions about the color
of a banana are answered with yellow, even if the banana in the
image is green. If societal bias (e.g., sexism, racism, ableism, etc.)
is present in the training data, this problem may be causing VQA
models to learn harmful stereotypes. For this reason, we investigate
gender and racial bias in five VQA datasets. In our analysis, we
find that the distribution of answers is highly different between
questions about women and men, as well as the existence of detrimental gender-stereotypical samples. Likewise, we identify that
specific race-related attributes are underrepresented, whereas potentially discriminatory samples appear in the analyzed datasets.
Our findings suggest that there are dangers associated to using
VQA datasets without considering and dealing with the potentially
harmful stereotypes. We conclude the paper by proposing solutions to alleviate the problem before, during, and after the dataset
collection process.