Despite their prevalence in society, social biases are difficult to identify,
primarily because human judgements in this domain can be unreliable. We take an
unsupervised approach to identifying gender bias against women at a comment
level and present a model that can surface text likely to contain bias. Our
main challenge is forcing the model to focus on signs of implicit bias, rather
than other artifacts in the data. Thus, our methodology involves reducing the
influence of confounds through propensity matching and adversarial learning.
Our analysis shows how biased comments directed towards female politicians
contain mixed criticisms, 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.

本文提出了一种无监督的方法，通过倾向匹配和对抗性学习来降低混淆，并着重于展现隐含的性别偏见，并揭示了针对女性政治家的有偏见评论包含各种批评，而针对其他女性公众人物的评论则集中于外貌和性感方面。最终，我们的工作提供了一种在各个领域捕捉微妙偏见的方法，而无需依赖主观的人类判断。