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Physical Sciences › Computer Science › Artificial Intelligence
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
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Conflicts of Interest
Identified Weaknesses
Rating Explanation
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Paper Summary
Paperzilla title
Opening the XAI Black Box: A Guide to Explainable AI for Trustworthy Artificial Intelligence
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.
Possible Conflicts of Interest
None identified
Identified Weaknesses
Lack of Novel Contributions
The article primarily provides a broad overview of XAI methods and concepts rather than presenting novel research or findings.
Limited Practical Application
While the article discusses various XAI methods and their applications, it lacks a practical demonstration or case study to illustrate their real-world implementation.
Potential for Outdated Information
The article's focus on existing literature and surveys might not fully capture the latest advancements or address emerging trends in the rapidly evolving field of XAI.
Rating Explanation
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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Original Title:
Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
File Name:
1-s2.0-S1566253523001148-main.pdf
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File Size:
7.73 MB
Uploaded:
July 14, 2025 at 05:21 PM
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