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A Novel Metric for XAI Evaluation Incorporating Pixel Analysis and Distance Measurement

  • Explainable Artificial Intelligence (XAI) seeks to enhance transparency and trust in AI systems. Evaluating the quality of XAI explanation methods remains challenging due to limitations in existing metrics. To address these issues, we propose a novel metric called Explanation Significance Assessment (ESA) and its extension, the Weighted Explanation Significance Assessment (WESA). These metrics offer a comprehensive evaluation of XAI explanations, considering spatial precision, focus overlap, and relevance accuracy. In this paper, we demonstrate the applicability of ESA and WESA on medical data. These metrics quantify the understandability and reliability of XAI explanations, assisting practitioners in interpreting AI-based decisions and promoting informed choices in critical domains like healthcare. Moreover, ESA and WESA can play a crucial role in AI certification, ensuring both accuracy and explainability. By evaluating the performance of XAI methods and underlying AI models, these metrics contribute to trustworthy AI systems. Incorporating ESA and WESA in AI certification efforts advances the field of XAI and bridges the gap between accuracy and interpretability. In summary, ESA and WESA provide comprehensive metrics to evaluate XAI explanations, benefiting research, critical domains, and AI certification, thereby enabling trustworthy and interpretable AI systems.

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Metadaten
Author:Jan StodtORCiDGND, Christoph ReichORCiDGND, Nathan Clarke
DOI:https://doi.org/10.1109/ICTAI59109.2023.00009
ISBN:979-8-3503-4273-4
Parent Title (English):2023 IEEE 35th International Conference on Tools with Artificial Intelligence : ICTAI 2023, 6-8 November 2023, Atlanta GA, USA
Publisher:IEEE
Document Type:Conference Proceeding
Language:English
Year of Completion:2023
Release Date:2024/01/09
Tag:Artificial intelligence; Distance measurement; Medical services; Reliability
Page Number:9
Open-Access-Status: Closed Access 
Licence (German):License LogoUrheberrechtlich geschützt