Gender and Racial Bias in Visual Question Answering Datasets

@article{Hirota2022GenderAR,
  title={Gender and Racial Bias in Visual Question Answering Datasets},
  author={Yusuke Hirota and Yuta Nakashima and Noa Garc{\'i}a},
  journal={2022 ACM Conference on Fairness, Accountability, and Transparency},
  year={2022}
}
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… 

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