Summary of A Unified Framework For Evaluating the Effectiveness and Enhancing the Transparency Of Explainable Ai Methods in Real-world Applications, by Md. Ariful Islam et al.
A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications
by Md. Ariful Islam, M. F. Mridha, Md Abrar Jahin, Nilanjan Dey
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed unified evaluation framework for explainable artificial intelligence (XAI) aims to bridge the gap in standardized procedures for assessing the efficacy and transparency of XAI techniques across various real-world applications. The framework incorporates quantitative and qualitative criteria to evaluate the correctness, interpretability, robustness, fairness, and completeness of explanations generated by AI models. This comprehensive approach prioritizes user-centric and domain-specific adaptations, improving the usability and reliability of AI models in essential domains such as healthcare, finance, agriculture, and autonomous systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a unified framework for evaluating explainable artificial intelligence (XAI) techniques to ensure transparency and accountability in AI-driven applications. The proposed approach includes standardized benchmarks and a systematic evaluation pipeline that ensures explanations are correct, interpretable, robust, fair, and complete. This enhances XAI research by providing a flexible and pragmatic method for assessing the reliability of AI models across various domains. |
