The use of deep learning for epileptic seizure detection from electroencephalographic (EEG) data has led to impressive advances in classification accuracy, being able to correctly distinguish between ictal and interictal phases. However, a crucial challenge remains in ensuring that these models are both transparent and reliably calibrated for safe clinical adoption. In fact, neural network models for seizure detection often operate as “black boxes,” yielding predictions without transparent reasoning regarding the level of certainty associated with each decision. In many of these systems, neural networks give out confidence levels that do not always show the real chance of making the right prediction.