GENEOnet: Statistical analysis supporting explainability and trustworthiness
This work addresses the need for trustworthy AI in computational biochemistry, but it is incremental as it applies an existing method to a specific domain with statistical validation.
The study tackled the problem of verifying the explainability and trustworthiness of GENEOnet, a Group Equivariant Non-Expansive Operator network, in computational biochemistry, showing it has a significantly higher proportion of equivariance and is robust to perturbations from molecular dynamics.
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.