In the last decade, significant technological progress has been driven rapidly by numerous advances in applications of machine learning. Novel ML techniques have revolutionized industries by cracking historically elusive problems in computer vision, natural language processing, robotics, and many others. Today it’s not hyperbolic to say that ML has changed how we work, how we shop, and how we play.
While many models have increased in performance, delivering state-of-the-art results on popular datasets and challenges, models have also increased in complexity. In particular, the ability to introspect and understand why a model made a particular prediction has become more and more difficult.
Now that ML models power experiences that impact important parts of our lives, it has become even more important that we have the ability to explain how they make their predictions.
