Worst of Both Worlds: Biases Compound in Pre-trained Vision-and-Language Models

Tejas Srinivasan; Yonatan Bisk

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arXiv · 2104.08666

Worst of Both Worlds: Biases Compound in Pre-trained Vision-and-Language Models

Tejas Srinivasan·Yonatan Bisk

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Abstract

Numerous works have analyzed biases in vision and pre-trained language models individually - however, less attention has been paid to how these biases interact in multimodal settings. This work extends text-based bias analysis methods to investigate multimodal language models, and analyzes intra- and inter-modality associations and biases learned by these models. Specifically, we demonstrate that VL-BERT (Su et al., 2020) exhibits gender biases, often preferring to reinforce a stereotype over faithfully describing the visual scene. We demonstrate these findings on a controlled case-study and extend them for a larger set of stereotypically gendered entities.

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Tejas Srinivasan, Yonatan Bisk. 2021-04-18. Worst of Both Worlds: Biases Compound in Pre-trained Vision-and-Language Models. https://arxiv.org/abs/2104.08666

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