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Motivations for methods in explainable artificial intelligence (XAI) ofteninclude detecting, quantifying and mitigating bias, and contributing to makingmachine learning models fairer. However, exactly how an XAI method can help incombating biases is often left unspecified. In this paper, we briefly reviewtrends in explainability and fairness in NLP research, identify the currentpractices in which explainability methods are applied to detect and mitigatebias, and investigate the barriers preventing XAI methods from being used morewidely in tackling fairness issues.

Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models