The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus -- a comprehensive, evaluation toolkit in Python that includes a growing, well-organised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://github.com/understandable-machine-intelligence-lab/Quantus/).

翻译：解释方法的评估是一个尚未深入探索的研究课题，然而，由于可解释性旨在增强对人工智能的信任，因此有必要系统性地审查和比较解释方法，以确认其正确性。迄今为止，尚无专注于XAI评估的工具能够全面且高效地让研究人员评估神经网络预测解释的性能。为提高该领域的透明度和可重复性，我们构建了Quantus——一个基于Python的综合性评估工具包，其中包含不断增长且组织良好的评估指标集合及教程，用于评估可解释方法。该工具包已通过全面测试，并以开源许可证形式发布于PyPi（或访问https://github.com/understandable-machine-intelligence-lab/Quantus/）。