Multi-Dimensional Gender Bias Classification
This work addresses gender bias in NLP for researchers and practitioners, offering a more nuanced approach to classification and evaluation, though it is incremental in building on existing bias detection methods.
The authors tackled the problem of gender bias in NLP by proposing a framework that decomposes bias along pragmatic and semantic dimensions, enabling the training of fine-grained classifiers and their application to tasks like controlling bias in generative models and detecting gendered offensive language.
Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information. In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites. Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers. We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.