Explainable Artificial Intelligence (XAI): An Engineering Perspective

TL;DR

This paper presents an engineering perspective on Explainable AI (XAI), emphasizing its importance for safety-critical systems, and explores its application in autonomous vehicles to enhance transparency and accountability.

Contribution

It offers an engineering framework for understanding XAI, discusses stakeholder roles, and applies XAI concepts to autonomous vehicle components as a case study.

Findings

XAI enhances transparency in AI systems.

Application of XAI in autonomous vehicles improves interpretability.

Identifies new research directions in XAI.

Abstract

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.