Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search
Jialu Wang, Yang Liu, Xin Eric Wang

TL;DR
This paper investigates gender bias in image search results for gender-neutral queries, diagnosing existing models and proposing novel debiasing methods that significantly reduce bias in benchmark datasets.
Contribution
The paper introduces two innovative debiasing techniques—an in-processing fair sampling method and a post-processing feature clipping approach—applying them to both specialized and pre-trained models.
Findings
Both models exhibit severe gender bias.
Proposed methods effectively reduce gender bias.
Significant bias reduction demonstrated on MS-COCO and Flickr30K.
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…
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Taxonomy
TopicsMedia Influence and Politics · Demographic Trends and Gender Preferences · Misinformation and Its Impacts
