Cynthia Rudin

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Cynthia Rudin · Nature Machine Intelligence · 2019 · 9K citations · Full text

Computing, Computer Science, Artificial Intelligence +11

All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Aaron Fisher, Cynthia Rudin, Francesca Dominici · arXiv (Cornell University) · 2018 · 1.2K citations · Full text

Computing, Biostatistics, Data Science +14

Interpretable machine learning: Fundamental principles and 10 grand challenges

Cynthia Rudin, Chaofan Chen, Zhi Chen et al. · Statistics Surveys · 2022 · 844 citations · Full text

Computing, Computer Science, Artificial Intelligence +16

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

Benjamin Letham, Cynthia Rudin, Tyler H. McCormick et al. · The Annals of Applied Statistics · 2015 · 777 citations · Full text

Computing, Cardiology, Computer Science +17

This Looks Like That: Deep Learning for Interpretable Image Recognition

Chaofan Chen, Oscar Li, Chaofan Tao et al. · arXiv (Cornell University) · 2018 · 567 citations · Full text

Computing, Computer Science, Computer Vision +14