mlsanityData to 14 Oct 2026

An Experimental Investigation into the Evaluation of Explainability Methods

arXiv 2305.16361 ↗First posted 25 May 2023cs.LG cs.AI cs.CVcited by 0

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Abstract (arXiv)

EXplainable Artificial Intelligence (XAI) aims to help users to grasp the reasoning behind the predictions of an Artificial Intelligence (AI) system. Many XAI approaches have emerged in recent years. Consequently, a subfield related to the evaluation of XAI methods has gained considerable attention, with the aim to determine which methods provide the best explanation using various approaches and criteria. However, the literature lacks a comparison of the evaluation metrics themselves, that one can use to evaluate XAI methods. This work aims to fill this gap by comparing 14 different metrics when applied to nine state-of-the-art XAI methods and three dummy methods (e.g., random saliency maps) used as references. Experimental results show which of these metrics produces highly correlated results, indicating potential redundancy. We also demonstrate the significant impact of varying the baseline hyperparameter on the evaluation metric values. Finally, we use dummy methods to assess the reliability of metrics in terms of ranking, pointing out their limitations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 28 of its 40 references that are in the map. First posted on arXiv 25 May 2023.

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References · 28

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16 more
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