  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.
