Interpretations of Learning

Artificial Intelligence (AI) is becoming ubiquitous in both science and industry due to deep learning's superiority in very specific tasks such as image classification¹. Considering this juxtaposition, can we trust deep learning as a scientific tool to explain reality, and in turn exploit these discoveries within industry? Arguably no, as we are blind-sided by hidden risks and explanations that deep learning cannot provide, especially when making predictions beyond training examples. Being able to interpret a prediction, and then explain the problem, is fundamental for the general performance of AI and absolutely crucial for safety, reliability, and fairness². Thus, in order to advance AI we must also advance interpretations of learning.