Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
Descripción general
Resumen del artículo
The review explores the concept of explainable AI (XAI), its techniques, and significance in attaining trustworthy AI. It divides XAI methods into four axes: data explainability, model explainability, post-hoc explainability, and assessment of explanations, while also addressing legal demands, user perspectives, and application orientations related to XAI.
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Scientists are studying how to make smart computers, called AI, explain why they do things. This helps us understand them better, just like you explain your choices so people can trust you.
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Explicación de la calificación
This review provides a comprehensive and up-to-date overview of XAI, covering various aspects like data, model, and post-hoc explainability. It also discusses the assessment of explanations and highlights future research directions. While it does not offer groundbreaking contributions, the article's scope, depth, and structured approach make it a valuable resource for XAI researchers and practitioners.
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