Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search

• URL: http://arxiv.org/abs/2109.05433v1
• Date: Sun, 12 Sep 2021 04:47:33 GMT
• Title: Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search
• Authors: Jialu Wang and Yang Liu and Xin Eric Wang
• Abstract summary: We study a unique gender bias in image search. The search images are often gender-imbalanced for gender-neutral natural language queries. We introduce two novel debiasing approaches.
• Score: 8.730027941735804
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Internet search affects people's cognition of the world, so mitigating biases in search results and learning fair models is imperative for social good. We study a unique gender bias in image search in this work: the search images are often gender-imbalanced for gender-neutral natural language queries. We diagnose two typical image search models, the specialized model trained on in-domain datasets and the generalized representation model pre-trained on massive image and text data across the internet. Both models suffer from severe gender bias. Therefore, we introduce two novel debiasing approaches: an in-processing fair sampling method to address the gender imbalance issue for training models, and a post-processing feature clipping method base on mutual information to debias multimodal representations of pre-trained models. Extensive experiments on MS-COCO and Flickr30K benchmarks show that our methods significantly reduce the gender bias in image search models.

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