BEExAI: Benchmark to Evaluate Explainable AI

• URL: http://arxiv.org/abs/2407.19897v1
• Date: Mon, 29 Jul 2024 11:21:17 GMT
• Title: BEExAI: Benchmark to Evaluate Explainable AI
• Authors: Samuel Sithakoul, Sara Meftah, Clément Feutry,
• Abstract summary: We propose BEExAI, a benchmark tool that allows large-scale comparison of different post-hoc XAI methods. We argue that the need for a reliable way of measuring the quality and correctness of explanations is becoming critical.
• Score: 0.9176056742068812
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent research in explainability has given rise to numerous post-hoc attribution methods aimed at enhancing our comprehension of the outputs of black-box machine learning models. However, evaluating the quality of explanations lacks a cohesive approach and a consensus on the methodology for deriving quantitative metrics that gauge the efficacy of explainability post-hoc attribution methods. Furthermore, with the development of increasingly complex deep learning models for diverse data applications, the need for a reliable way of measuring the quality and correctness of explanations is becoming critical. We address this by proposing BEExAI, a benchmark tool that allows large-scale comparison of different post-hoc XAI methods, employing a set of selected evaluation metrics.

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