Legal and Policy Implications of Model Interpretability with Solon Barocas

Today we’re joined by Solon Barocas, Assistant Professor of Information Science at Cornell University.

Solon and I caught up to discuss his work on model interpretability and the legal and policy implications of the use of machine learning models. In our conversation, we explore the gap between law, policy, and ML, and how to build the bridge between them, including formalizing ethical frameworks for machine learning. We also look at his paper ”The Intuitive Appeal of Explainable Machines.”