Opinions on Interpretable Machine Learning and 70 Summaries of Recent Papers
Peter Hase UNC Chapel Hill Owen Shen UC San Diego With thanks to Robert Kirk and Mohit Bansal for helpful feedback on this post. Introduction Model interpretability was a bullet point in Concrete Problems in AI Safety (2016). Since then, interpretability has come to comprise entire research directions in technical...
Apr 9, 2021142