@InProceedings{rudinger-may-vandurme:2017:EthNLP,
  author    = {Rudinger, Rachel  and  May, Chandler  and  Van Durme, Benjamin},
  title     = {Social Bias in Elicited Natural Language Inferences},
  booktitle = {Proceedings of the First ACL Workshop on Ethics in Natural Language Processing},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {74--79},
  abstract  = {We analyze the Stanford Natural Language Inference (SNLI) corpus in an
	investigation of bias and stereotyping in NLP data. The SNLI human-elicitation
	protocol makes it prone to amplifying bias and stereotypical associations,
	which we demonstrate statistically (using pointwise mutual information) and
	with qualitative examples.},
  url       = {http://www.aclweb.org/anthology/W17-1609}
}