Social Bias in Elicited Natural Language Inferences
Rachel Rudinger, Chandler May, Benjamin Van Durme · 2017
We analyze the Stanford Natural Language Inference (SNLI) corpus in an investigation of bias and stereotyping in NLP data.The human-elicitation protocol employed in the construction of the SNLI makes it prone to amplifying bias and stereotypical associations, which we demonstrate statistically (using pointwise mutual information) and with qualitative examples.