Explainable AI for Cyber-Physical Systems: Issues and Challenges

Abstract

Artificial intelligence and cyber-physical systems (CPS) are two of the key technologies of the future that are enabling major global shifts. However, most of the current implementations of AI in CPS are not explainable, which creates serious problems in ethical, legal, regulatory, and other domains. Therefore, it is necessary for explainable artificial intelligence (XAI) to be integrated with cyber-physical systems to meet the vital needs for control, fairness, accountability, safety, cyber-resilience, and cybersecurity. The goal of this review is to demonstrate the need, benefits, challenges, and implementation of XAI for CPS. We review the existing literature about XAI and CPS, discuss the current state of the art, examine applications in different domains, and make recommendations for future research directions. To the best of our knowledge, this is the first peer-reviewed academic article to provide a comprehensive review of general XAI for CPS. We also contribute new research ideas including development of multisensory explanations and outputs for these systems, application of XAI to CPS to decrease occupational burnout and increase employee engagement, and enumeration of the multidisciplinary goals and benefits of XAI as applied to cyber-physical systems.

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