Deep Learning And Explainable Artificial Intelligence

This Special Issue seeks to bridge the gap between biological learning and current software capabilities by developing explanations and understanding of functioning AI/ML methods, and to develop AI/ML methods that generate outcomes with predictable properties when fed with data satisfying certain conditions. It is hoped that this will stimulate AI that will increase efficiencies while not compromising safety, trust, fairness, predictability, and reliability when applied to systems with large energy use such as power, water, transport, or financial grids, law and government policy.