Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016)

TL;DR

This document compiles the proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning, featuring invited talks from leading experts, and discusses advances in making machine learning models more understandable to humans.

Contribution

It provides a comprehensive overview of recent research and expert insights on interpretability in machine learning from the 2016 workshop.

Findings

Highlights key research directions in interpretability

Includes expert perspectives and invited talks

Summarizes recent advances in human-centered ML

Abstract

This is the Proceedings of the 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), which was held in New York, NY, June 23, 2016. Invited speakers were Susan Athey, Rich Caruana, Jacob Feldman, Percy Liang, and Hanna Wallach.