The great AI debate: Interpretability

Deep learning (DL) has crept into almost all parts of Artificial Intelligence. DL methods constitute the state of the art for almost all tasks in image processing, natural language processing, recommendation systems…etc. Consequently, as more and more of these models are being deployed, the interpretability of these models is coming into question. Deep learning is a form of machine learning based on learning hierarchical, distributed models to solve difficult AI problems. Broadly speaking, these models work extremely well for most tasks, but because they have so many learnable parameters (order of millions), it's hard to predict what the effect of 1 single parameter, or a few of them, would be on the overall neural network.