  Motivations for methods in explainable artificial intelligence (XAI) often
include detecting, quantifying and mitigating bias, and contributing to making
machine learning models fairer. However, exactly how an XAI method can help in
combating biases is often left unspecified. In this paper, we briefly review
trends in explainability and fairness in NLP research, identify the current
practices in which explainability methods are applied to detect and mitigate
bias, and investigate the barriers preventing XAI methods from being used more
widely in tackling fairness issues.
