Algorithmic fairness and explainability are foundational elements for achieving responsible AI. In this paper, we focus on their interplay, a research area that is recently receiving increasing attention. To this end, we first present two comprehensive taxonomies, each representing one of the two complementary fields of study: fairness and explanations. Then, we categorize explanations for fairness into three types: (a) Explanations to enhance fairness metrics, (b) Explanations to help us understand the causes of (un)fairness, and (c) Explanations to assist us in designing methods for mitigating unfairness. Finally, based on our fairness and explanation taxonomies, we present undiscovered literature paths revealing gaps that can serve as valuable insights for future research.

翻译：算法公平性与可解释性是实现负责任人工智能的基础要素。本文聚焦两者之间的相互作用这一近年日益受到关注的研究领域。为此，我们首先提出两个全面的分类体系，分别代表公平性和解释这两个互补的研究领域。随后，我们将针对公平性的解释划分为三类：(a) 用于提升公平性指标的解释，(b) 用于帮助理解（不）公平成因的解释，以及(c) 用于协助设计不公平性缓解方法的解释。最后，基于提出的公平性与解释分类体系，我们揭示了未被探索的文献路径，这些路径所呈现的研究空白可为未来研究提供重要洞察。