Ethics of Artiicial Intelligence (AI) is a growing research ield that has emerged in response to the challenges related to AI. Transparency poses a key challenge for implementing AI ethics in practice. One solution to transparency issues is AI systems that can explain their decisions. Explainable AI (XAI) refers to AI systems that are interpretable or understandable to humans. The research ields of AI ethics and XAI lack a common framework and conceptualization. There is no clarity of the ield’s depth and versatility. A systematic approach to understanding the corpus is needed. A systematic review ofers an opportunity to detect research gaps and focus points. This paper presents the results of a systematic mapping study (SMS) of the research ield of the Ethics of AI. The focus is on understanding the role of XAI and how the topic has been studied empirically. An SMS is a tool for performing a repeatable and continuable literature search. This paper contributes to the research ield with a Systematic Map that visualizes what, how, when, and why XAI has been studied empirically in the ield of AI ethics. The mapping reveals research gaps in the area. Empirical contributions are drawn from the analysis. The contributions are relected on in regards to theoretical and practical implications. As the scope of the SMS is a broader research area of AI ethics the collected dataset opens possibilities to continue the mapping process in other directions.