标题： GENEOnet：支持可解释性和可信度的统计分析 显示英文标题

标题： GENEOnet: Statistical analysis supporting explainability and trustworthiness

摘要： 群体等变非扩张算子（GENEOs）已成为构建机器学习和人工智能网络的数学工具。最近的研究表明，由于其固有的可解释性，此类模型可以插入可解释人工智能（XAI）领域。在本研究中，我们旨在通过使用各种统计分析和实验来验证这一说法，针对GENEOnet，这是一种用于计算化学应用的GENEO网络。这些实验首先使我们能够对GENEOnet的参数进行敏感性分析，以测试它们的重要性。随后，我们展示了GENEOnet相比其他方法表现出显著更高的等变性比例。最后，我们证明了GENEOnet平均而言对来自分子动力学的扰动具有鲁棒性。这些结果共同作为GENEOnet可解释性、可信度和鲁棒性的证明，并证实了在可信人工智能背景下使用GENEOs的有益性。 显示英文摘要

摘要： Group Equivariant Non-Expansive Operators (GENEOs) have emerged as mathematical tools for constructing networks for Machine Learning and Artificial Intelligence. Recent findings suggest that such models can be inserted within the domain of eXplainable Artificial Intelligence (XAI) due to their inherent interpretability. In this study, we aim to verify this claim with respect to GENEOnet, a GENEO network developed for an application in computational biochemistry by employing various statistical analyses and experiments. Such experiments first allow us to perform a sensitivity analysis on GENEOnet's parameters to test their significance. Subsequently, we show that GENEOnet exhibits a significantly higher proportion of equivariance compared to other methods. Lastly, we demonstrate that GENEOnet is on average robust to perturbations arising from molecular dynamics. These results collectively serve as proof of the explainability, trustworthiness, and robustness of GENEOnet and confirm the beneficial use of GENEOs in the context of Trustworthy Artificial Intelligence.