@InProceedings{zhao-EtAl:2017:EMNLP20173,
  author    = {Zhao, Jieyu  and  Wang, Tianlu  and  Yatskar, Mark  and  Ordonez, Vicente  and  Chang, Kai-Wei},
  title     = {Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2979--2989},
  abstract  = {Language is increasingly being used to de-fine rich visual recognition problems
	with supporting image collections sourced from the web. Structured prediction
	models are used  in  these  tasks  to  take  advantage              of correlations 
	between  co-occurring  labels and visual input but risk inadvertently encoding
	social biases found in web corpora. In this work, we study data and models
	associated with multilabel object classification and visual semantic role
	labeling. We find that (a) datasets for these tasks contain significant gender
	bias and (b) models  trained  on  these  datasets  further  amplify existing
	bias.             For example,  the activity cooking is over 33% more likely to 
	involve 
	females  than  males  in  a  training set, and a trained model further
	amplifies the disparity to 68% at test time.  We propose to inject corpus-level
	constraints for calibrating existing structured prediction models and design an
	algorithm based on Lagrangian relaxation for collective inference.  Our method
	results in almost no performance loss for the underlying recognition task but
	decreases the magnitude of bias amplification by 47.5% and 40.5% for multilabel
	classification and visual semantic role labeling, respectively。},
  url       = {https://www.aclweb.org/anthology/D17-1323}
}