Unsupervised Discovery of Implicit Gender Bias
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Despite their prevalence in society, social biases are difficult to define and identify, primarily because human judgements in this domain can be unreliable. Therefore, we take an unsupervised approach to identifying gender bias at a comment or sentence level, and present a model that can surface text likely to contain bias. The main challenge in this approach is forcing the model to focus on signs of implicit bias, rather than other artifacts in the data. Thus, the core of our methodology relies on reducing the influence of confounds through propensity score matching and adversarial learning. Our analysis shows how biased comments directed towards female politicians contain mixed criticisms and references to their spouses, while comments directed towards other female public figures focus on appearance and sexualization. Ultimately, our work offers a way to capture subtle biases in various domains without relying on subjective human judgements.
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