BEExAI：评估可解释AI的基准
BEExAI: Benchmark to Evaluate Explainable AI
AI总结：
针对黑盒模型事后归因解释缺乏统一评估方法的问题，提出基准工具BEExAI，利用选定评估指标实现不同事后XAI方法的大规模比较。
摘要：
近期可解释性研究催生了众多事后归因方法，旨在增强我们对黑盒机器学习模型输出的理解。然而，评估解释质量缺乏统一方法，也未能就推导量化指标以衡量事后归因方法有效性的方法论达成共识。此外，随着日益复杂的深度学习模型在多样数据应用中的发展，可靠地衡量解释质量与正确性的需求正变得至关重要。我们通过提出BEExAI来解决这一问题，这是一种基准工具，利用一组选定的评估指标，允许对不同的事后XAI方法进行大规模比较。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
