Gender and Racial Bias in Visual Question Answering Datasets
This work addresses societal bias in VQA datasets, which is crucial for preventing harmful stereotypes in AI models, though it is incremental as it builds on prior concerns about bias in machine learning.
The paper investigated gender and racial bias in five visual question answering (VQA) datasets, finding significant differences in answer distributions between genders and the presence of harmful stereotypes, such as detrimental gender-stereotypical samples and underrepresented race-related attributes.
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.