| Autor: |
Sithakoul, Samuel, Meftah, Sara, Feutry, Clément |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
World Conference on Explainable Artificial Intelligence, 2024 |
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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