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This paper provides an in-depth examination of Explainable Artificial Intelligence (XAI) and its significance in developing responsible AI. It covers the history of AI, the fundamental principles, the difference between explainability and interpretability, and the types of explanations. The paper highlights the role of transparency in explainable systems. Moreover, it discusses applications across high-stakes domains. Additionally, it touches on the current regulatory landscape and presents an outlook on the future of XAI research and regulation.
DeSimone et al. (2024) studied this question.