  Artificial Intelligence (AI) is one of the disruptive technologies that is
shaping the future. It has growing applications for data-driven decisions in
major smart city solutions, including transportation, education, healthcare,
public governance, and power systems. At the same time, it is gaining
popularity in protecting critical cyber infrastructure from cyber threats,
attacks, damages, or unauthorized access. However, one of the significant
issues of those traditional AI technologies (e.g., deep learning) is that the
rapid progress in complexity and sophistication propelled and turned out to be
uninterpretable black boxes. On many occasions, it is very challenging to
understand the decision and bias to control and trust systems' unexpected or
seemingly unpredictable outputs. It is acknowledged that the loss of control
over interpretability of decision-making becomes a critical issue for many
data-driven automated applications. But how may it affect the system's security
and trustworthiness? This chapter conducts a comprehensive study of machine
learning applications in cybersecurity to indicate the need for explainability
to address this question. While doing that, this chapter first discusses the
black-box problems of AI technologies for Cybersecurity applications in smart
city-based solutions. Later, considering the new technological paradigm,
Explainable Artificial Intelligence (XAI), this chapter discusses the
transition from black-box to white-box. This chapter also discusses the
transition requirements concerning the interpretability, transparency,
understandability, and Explainability of AI-based technologies in applying
different autonomous systems in smart cities. Finally, it has presented some
commercial XAI platforms that offer explainability over traditional AI
technologies before presenting future challenges and opportunities.
