Reviewing Explainable Artificial Intelligence: Methods, Metrics, and Interpretability
Abstract
Explainable AI (XAI) has become increasingly important for enhancing the transparency, accountability, and trustworthiness of AI systems across various domains. This review paper surveys the methods, metrics, and evaluation techniques for XAI, including model interpretability, feature attribution, and post-hoc explanation methods. It discusses the challenges, ethical considerations, and societal implications of deploying XAI in real-world applications.
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