Innovative Models for Explainable Artificial Intelligence

Traditional data-driven ML approaches show very interesting performance even if their internal mechanisms are very cryptic (black box). However, in some critical contexts, model interpretability is mandatory to explain the learned functionality, becoming even a legal requirement. Among the benefits of reformulating neural networks through the geometric calculus paradigm, geometric interpretability could potentially serve as a characteristic that improves model transparency. This work proposes the use of higher-dimensional neurons to reduce computational complexity while preserving model accuracy.