Explaining AI Models

Dan Elton explores the limitations of current explainability techniques for AI models and proposes the concept of self-explaining AI. He discusses the need for models to provide explanations that map onto their actual functioning, and the challenges in achieving this. Kyle Polich delves into the idea of mutual information as a metric for confirming the relevance of explanations.