  Studies have shown that some Natural Language Processing (NLP) systems encode
and replicate harmful biases with potential adverse ethical effects in our
society. In this article, we propose an approach for identifying gender and
racial stereotypes in word embeddings trained on judicial opinions from U.S.
case law. Embeddings containing stereotype information may cause harm when used
by downstream systems for classification, information extraction, question
answering, or other machine learning systems used to build legal research
tools. We first explain how previously proposed methods for identifying these
biases are not well suited for use with word embeddings trained on legal
opinion text. We then propose a domain adapted method for identifying gender
and racial biases in the legal domain. Our analyses using these methods suggest
that racial and gender biases are encoded into word embeddings trained on legal
opinions. These biases are not mitigated by exclusion of historical data, and
appear across multiple large topical areas of the law. Implications for
downstream systems that use legal opinion word embeddings and suggestions for
potential mitigation strategies based on our observations are also discussed.

Computation and Language;Artificial Intelligence