Machine Learning Interpretability: A Survey on Methods and Metrics

Read Article

This article surveys approaches for achieving interpretability in machine learning models and considers societal impacts of interpretability in sensitive, audited, and regulated deployments. The authors also propose metrics for measuring the quality of an explanation.

Diego Carvalho, Eduardo Pereira, and Jaime Cardoso, Electronics
July 26, 2019