The role of explainable AI in the research field of AI ethics : systematic mapping study

Abstract

This paper presents the Systemic Mapping Study results of the Ethics of Artificial Intelligence (AI) research. AI ethics is an emerging and versatile topic interesting to different domains. This paper focuses on understanding the role of Explainable AI in the research field and how the topic has been studied. Explainable AI refers to AI systems that are interpretable or understandable to humans. It aims to increase the transparency of systems and make systems more trustworthy. Non-transparent AI systems are have already shown some of their weaknesses, such as in some cases favoring men over women in the hiring process. The research fields of AI ethics and Explainable AI lack a common framework and conceptualization. There is no clarity of the field’s depth and versatility; hence a systemic approach to understanding the corpus was needed. The systemic review offers an opportunity to detect research gaps and focus points. A Systemic Mapping Study is a tool to performing a repeatable and continuable literature search. This paper contributes to the research field with a Systemic Map that visualizes what, how, when, and why Explainable AI has been studied in AI ethics. Within the scope is the detection of primary papers in AI ethics, which opens possibilities to continue the mapping process in other papers. The third contribution is the primary empirical conclusions drawn from the analysis and reflect existing research and practical implementation.