Multi-Dimensional Gender Bias Classification

Understand

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.