BEExAI: Benchmark to Evaluate Explainable AI
<|reference_start|>BEExAI: Benchmark to Evaluate Explainable AI: 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.<|reference_end|>
@article{sithakoul2024beexai:,
    title={BEExAI: Benchmark to Evaluate Explainable AI},
    author={Samuel Sithakoul, Sara Meftah and Cl'ement Feutry},
    journal={World Conference on Explainable Artificial Intelligence, 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19897},
    primaryClass={cs.LG cs.AI cs.CL}
}