论文标题
应用解释性方法提高NLP模型的公平性方面的挑战
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models
论文作者
论文摘要
可解释的人工智能（XAI）中方法的动机通常包括检测，量化和减轻偏见，以及使机器学习模型更加公平。但是，XAI方法完全可以帮助打击偏见，通常未指定。在本文中，我们简要回顾了NLP研究中解释性和公平性的趋势，确定了当前的实践，其中应用了解释性方法来检测和减轻偏见，并调查了阻止XAI方法在解决公平问题中更广泛使用的障碍。
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.
