Designing Inherently Interpretable Machine Learning Models
The authors argue that machine learning models in use cases that are highly sensitive and/or sectors that are highly regulated require inherent interpretability. The paper provides an approach for qualitatively assessing the interpretability of models based on feature effects and model architecture constraints.
Agus Sudjianto and Aijun Zhang; Cornell University
November 2021