The Accuracy Interpretability Trade Off

Kyle and Dan discuss the Accuracy Interpretability trade off in machine learning. While some argue that interpretable models can be found for any task, the complexity of real-world data often leads to complex and hard to interpret models. They also explore the challenges of explaining the functioning of deep neural nets and the need for self-explaining AI networks.