Explainable Artificial Intelligence Technology and Its Applications: Toward Transparent, Human-Centered, and Trustworthy AI

Abstract

Explainable artificial intelligence (XAI) has become a central methodology for developing transparent, accountable, and human-centered AI systems. As data-driven models are increasingly deployed in high-stakes and socially consequential settings, explanations are expected not only to illuminate model behavior but also to support validation, error analysis, fairness auditing, regulatory compliance, and effective human–AI collaboration. This Editorial introduces the Special Issue “Explainable Artificial Intelligence Technology and Its Applications” and situates its contributions within the broader trajectory of XAI research. We briefly review major methodological families, including intrinsic interpretability, local surrogate and Shapley-value explanations, gradient- and perturbation-based visual attribution, counterfactual and causal explanations, and human-centered evaluation. We then highlight representative contributions in this Special Issue, which demonstrate how XAI is moving from generic explanation visualizations toward domain-sensitive, data-aware, and operationally reliable methods in network security, computer vision, knowledge graphs, and financial decision support. Finally, we discuss future directions, emphasizing faithful and plausible explanations, causal and multimodal reasoning, real-time and hardware-efficient deployment, trustworthy governance, and the emerging role of XAI in education.