  Social bias in machine learning has drawn significant attention, with work
ranging from demonstrations of bias in a multitude of applications, curating
definitions of fairness for different contexts, to developing algorithms to
mitigate bias. In natural language processing, gender bias has been shown to
exist in context-free word embeddings. Recently, contextual word
representations have outperformed word embeddings in several downstream NLP
tasks. These word representations are conditioned on their context within a
sentence, and can also be used to encode the entire sentence. In this paper, we
analyze the extent to which state-of-the-art models for contextual word
representations, such as BERT and GPT-2, encode biases with respect to gender,
race, and intersectional identities. Towards this, we propose assessing bias at
the contextual word level. This novel approach captures the contextual effects
of bias missing in context-free word embeddings, yet avoids confounding effects
that underestimate bias at the sentence encoding level. We demonstrate evidence
of bias at the corpus level, find varying evidence of bias in embedding
association tests, show in particular that racial bias is strongly encoded in
contextual word models, and observe that bias effects for intersectional
minorities are exacerbated beyond their constituent minority identities.
Further, evaluating bias effects at the contextual word level captures biases
that are not captured at the sentence level, confirming the need for our novel
approach.
