  The increasing complexity of AI systems has led to the growth of the field of
Explainable Artificial Intelligence (XAI), which aims to provide explanations
and justifications for the outputs of AI algorithms. While there is
considerable demand for XAI, there remains a scarcity of studies aimed at
comprehensively understanding the practical distinctions among different
methods and effectively aligning each method with users individual needs, and
ideally, offer a mapping function which can map each user with its specific
needs to a method of explainability. This study endeavors to bridge this gap by
conducting a thorough review of extant research in XAI, with a specific focus
on Explainable Machine Learning (XML), and a keen eye on user needs. Our main
objective is to offer a classification of XAI methods within the realm of XML,
categorizing current works into three distinct domains: philosophy, theory, and
practice, and providing a critical review for each category. Moreover, our
study seeks to facilitate the connection between XAI users and the most
suitable methods for them and tailor explanations to meet their specific needs
by proposing a mapping function that take to account users and their desired
properties and suggest an XAI method to them. This entails an examination of
prevalent XAI approaches and an evaluation of their properties. The primary
outcome of this study is the formulation of a clear and concise strategy for
selecting the optimal XAI method to achieve a given goal, all while delivering
personalized explanations tailored to individual users.
