arXiv:2204.09591cs.CL
A Survey on Bias and Fairness in Natural Language Processing
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Abstract
As NLP models become more integrated with the everyday lives of people, it becomes important to examine the social effect that the usage of these systems has. While these models understand language and have increased accuracy on difficult downstream tasks, there is evidence that these models amplify gender, racial and cultural stereotypes and lead to a vicious cycle in many settings. In this survey, we analyze the origins of biases, the definitions of fairness, and how different subfields of NLP mitigate bias. We finally discuss how future studies can work towards eradicating pernicious biases from NLP algorithms.
That's not _completely_ true. There are a bunch of datasets (specific to some cultural/lingual contexts) like CrowsPairs[1], StereoSet[2]. There is a lot of work you can do to make sure that the model's predictions are fair as well [3]. But at the end, yes these datasets don't…
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