  The widespread use of artificial intelligence (AI) systems across various
domains is increasingly surfacing issues related to algorithmic fairness,
especially in high-stakes scenarios. Thus, critical considerations of how
fairness in AI systems might be improved -- and what measures are available to
aid this process -- are overdue. Many researchers and policymakers see
explainable AI (XAI) as a promising way to increase fairness in AI systems.
However, there is a wide variety of XAI methods and fairness conceptions
expressing different desiderata, and the precise connections between XAI and
fairness remain largely nebulous. Besides, different measures to increase
algorithmic fairness might be applicable at different points throughout an AI
system's lifecycle. Yet, there currently is no coherent mapping of fairness
desiderata along the AI lifecycle. In this paper, we we distill eight fairness
desiderata, map them along the AI lifecycle, and discuss how XAI could help
address each of them. We hope to provide orientation for practical applications
and to inspire XAI research specifically focused on these fairness desiderata.
