  The remarkable advancements in Deep Learning (DL) algorithms have fueled
enthusiasm for using Artificial Intelligence (AI) technologies in almost every
domain; however, the opaqueness of these algorithms put a question mark on
their applications in safety-critical systems. In this regard, the
`explainability' dimension is not only essential to both explain the inner
workings of black-box algorithms, but it also adds accountability and
transparency dimensions that are of prime importance for regulators, consumers,
and service providers. eXplainable Artificial Intelligence (XAI) is the set of
techniques and methods to convert the so-called black-box AI algorithms to
white-box algorithms, where the results achieved by these algorithms and the
variables, parameters, and steps taken by the algorithm to reach the obtained
results, are transparent and explainable. To complement the existing literature
on XAI, in this paper, we take an `engineering' approach to illustrate the
concepts of XAI. We discuss the stakeholders in XAI and describe the
mathematical contours of XAI from engineering perspective. Then we take the
autonomous car as a use-case and discuss the applications of XAI for its
different components such as object detection, perception, control, action
decision, and so on. This work is an exploratory study to identify new avenues
of research in the field of XAI.
