Does the best AI think like a Human? - Dan Fiehn

In machine learning, understanding why a model makes certain decisions is often just as important as whether those decisions are correct. While tools exist to help experts make sense of a model's reasoning, often these methods only provide insights on one decision at a time, and each must be manually evaluated. Models are commonly trained using millions of data inputs, making it almost impossible for a human to evaluate enough decisions to identify patterns. Now, researchers at MIT and IBM Research have created a method that enables a user to aggregate, sort, and rank these individual explanations to rapidly analyze a machine-learning model's behaviour. Their technique, called Shared Interest, incorporates quantifiable metrics that compare how well a model's reasoning matches that of a human.