Are Gender-Neutral Queries Really Gender-Neutral? Mitigating Gender Bias in Image Search
Jialu Wang, Yang Liu, Xin Wang · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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 (Lin et al., 2014) and Flickr30K (Young et al., 2014) benchmarks show that our methods significantly reduce the gender bias in image search models.