Pretrained language models are publicly available and constantly finetuned for various real-life applications. As they become capable of grasping complex contextual information, harmful biases are likely increasingly intertwined with those models. This paper analyses gender bias in BERT models with two main contributions: First, a novel bias measure is introduced, defining biases as the difference in sentiment valuation of female and male sample versions. Second, we comprehensively analyse BERT's biases on the example of a realistic IMDB movie classifier. By systematically varying elements of the training pipeline, we can conclude regarding their impact on the final model bias. Seven different public BERT models in nine training conditions, i.e. 63 models in total, are compared. Almost all conditions yield significant gender biases. Results indicate that reflected biases stem from public BERT models rather than task-specific data, emphasising the weight of responsible usage.

本文分析Bert模型的性别偏见，并引入一种新的偏见度量，通过在训练管道中系统地改变元素来综合分析BERT的偏见，发现几乎所有条件下都存在重大的性别偏见，结果表明这些偏见源于公共BERT模型而非任务特定数据，强调了负责任的使用的重要性。

BERT 中的性别偏见--通过情感评级测量和分析在现实下游分类任务中的偏见