Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search
Jialu Wang, Yang Liu, Xin Wang
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 genderneutral natural language queries. We diagnose two typical image search models, the specialized model trained on in-domain datasets and the generalized representation model pretrained 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 postprocessing feature clipping method base on mutual information to debias multimodal representations of pre-trained models. Extensive experiments on MS-COCO