… research community and industry. This paper first introduces the history of Explainable AI, …
of modern deep learning, and then describes the major research areas and the state-of-art …

… In fact, early AI was re-traceable, interpretable, thus understandable by and explainable
to humans. The goal of this research is to articulate the big picture ideas and their role in …

… and explainability in AI [4], key regulations/laws, and techniques/tools for providing explainability
as part of AI/ML … Finally, we will discuss open challenges and research directions for the …

… explainability of AI models in data fusion contexts with regards to data privacy and model
confidentiality. This, along with other research … Specifically, we identify research needs around …

… research on eXplainable Artificial Intelligence (XAI) systems. We took a global look at the
contributions of scholars in XAI as well as in the subfields of AI … contributions with AI to promote …

… how an AI system may impact … AI explainability research and practices. We first looked at
the computer science (CS) community research to identify the main research themes about AI …

… solutions; then collates potentially interesting and promising future research directions
that we think are effective for improving the explainability and responsibility of AI solutions. …

… mapping study (SMS) of the research field of the Ethics of AI. The focus is on understanding
… current research question, “What is the role of explainable AI in the AI ethics research field?” …

… , explainable artificial intelligence (XAI) has become an area of interest in research community.
… with artificial general intelligence, and gives proposals for further research directions. …

… Eventually, this review concludes by critically discussing these gaps and limitations, and it
defines future research directions with explainability as the starting component of any artificial …