论文标题
无法发现隐性性别偏见
Unsupervised Discovery of Implicit Gender Bias
论文作者
论文摘要
尽管社会偏见在社会上的盛行，但很难确定，主要是因为该领域中的人类判断可能是不可靠的。我们采用一种无监督的方法来确定对女性对女性的性别偏见，并提出一个模型，该模型可能表现出可能包含偏见的文本。我们的主要挑战是迫使模型专注于隐性偏见的迹象，而不是数据中的其他工件。因此，我们的方法涉及通过倾向匹配和对抗性学习来减少混杂的影响。我们的分析表明，针对女性政客的有偏见的评论如何包含不同的批评，而针对其他女性公众人物的评论则集中于外观和性化。最终，我们的工作提供了一种捕获各个领域中微妙的偏见而不依赖主观人类判断的方法。
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.
