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.

翻译：群等变非扩张算子（GENEOs）已发展成为构建机器学习和人工智能网络的数学工具。最新研究表明，此类模型因其固有的可解释性，可被纳入可解释人工智能（XAI）的范畴。在本研究中，我们旨在通过多种统计分析与实验，验证这一论断在GENEOnet上的适用性——GENEOnet是一种为计算生物化学应用而开发的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，以检验其显著性。随后，我们证明GENEOnet相比其他方法展现出显著更高的等变性比例。最后，我们证实GENEOnet平均而言对分子动力学产生的扰动具有鲁棒性。这些结果共同证明了GENEOnet的可解释性、可信度与鲁棒性，并确认了GENEOs在可信人工智能背景下的有益应用。