For decades, the distinction between statistical models and machine learning ones has been clear. The former are optimized to produce interpretable results, whereas the latter seeks to maximize the predictive performance of the task at hand. This is valid for any scientific field and for any method belonging to the two categories mentioned above. When attempting to predict natural hazards, this difference has lead researchers to make drastic decisions on which aspect to prioritize, a difficult choice to make. In fact, one would always seek the highest performance because at higher performances correspond better decisions for disaster risk reduction. However, scientists also wish to understand the results, as a way to rely on the tool they developed. Today, very recent development in deep learning have brought forward a new generation of interpretable artificial intelligence, where the prediction power typical of machine learning tools is equipped with a level of explanatory power typical of statistical approaches. In this work, we attempt to demonstrate the capabilities of this new generation of explainable artificial intelligence (ExAI). To do so, we take the landslide susceptibility context as reference. Specifically, we build an ExAI trained to model landslides occurred in response to the Gorkha earthquake (25 April 2015), providing an educational overview of the model design and its querying opportunities. The results are surprising, the performance are extremely high, while the interpretability can be extended to the probabilistic result assigned to single mapping units. This is also showcased in a web-GIS (\textcolor{blue}{https://arcg.is/0unziD}) platform we built.
