Natural Language Processing (NLP) systems learn harmful societal biases that
cause them to amplify inequality as they are deployed in more and more
situations. To guide efforts at debiasing these systems, the NLP community
relies on a variety of metrics that quantify bias in models. Some of these
metrics are intrinsic, measuring bias in word embedding spaces, and some are
extrinsic, measuring bias in downstream tasks that the word embeddings enable.
Do these intrinsic and extrinsic metrics correlate with each other? We compare
intrinsic and extrinsic metrics across hundreds of trained models covering
different tasks and experimental conditions. Our results show no reliable
correlation between these metrics that holds in all scenarios across tasks and
languages. We urge researchers working on debiasing to focus on extrinsic
measures of bias, and to make using these measures more feasible via creation
of new challenge sets and annotated test data. To aid this effort, we release
code, a new intrinsic metric, and an annotated test set focused on gender bias
in hate speech.

本文比较了不同任务和实验条件下数百个已训练模型的内在和外在偏差度量标准之间的关系，发现这些度量标准在所有情况下都没有可靠的相关性。作者呼吁重点关注外在的偏差度量标准，并通过创建新的挑战集和注释测试数据使使用这些度量标准更加可行。在此基础上，作者发布了一份基于性别偏见的仇恨言论的代码、新的内部度量标准和一个注释的测试集。