Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platform
M. Humayn Kabir, Khondokar Fida Hasan, Mohammad Kamrul Hasan, Keyvan, Ansari

TL;DR
This paper reviews the role of explainable AI in enhancing security and trust in smart city applications, emphasizing the transition from black-box to transparent AI models and discussing future challenges.
Contribution
It provides a comprehensive analysis of XAI's importance in cybersecurity for smart cities and reviews existing platforms and future directions.
Findings
Highlighting the black-box problem in AI for cybersecurity
Discussing the transition to explainable AI models
Reviewing commercial XAI platforms
Abstract
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…
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Taxonomy
TopicsExplainable Artificial Intelligence (XAI) · Adversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications
