Speaker: Ruth Fong, Princeton University

As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions. In this talk, I’ll highlight our work on explaining the decisions and internal representations of deep neural networks. We'll compare how the past decade of interpretability research has tracked with the broader research communities in machine learning and computer vision and highlight several novel directions that are important for keeping pace with the next decade of research breakthroughs.