Rudin, Cynthia

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

Rudin, C. (2019) · Nature Machine Intelligence

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 10,561

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

Fisher, A., Rudin, C., & Dominici, F. (2018) · arXiv

Explainable Artificial Intelligence (XAI)Statistical Methods and Inference

Cited by 1,165

Interpretable machine learning: Fundamental principles and 10 grand challenges

Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., & Zhong, C. (2022) · Statistics Surveys

Explainable Artificial Intelligence (XAI)Machine Learning and Data Classification

Cited by 958

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

Letham, B., Rudin, C., McCormick, T. H., & Madigan, D. (2015) · The Annals of Applied Statistics

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare

Cited by 766

This Looks Like That: Deep Learning for Interpretable Image Recognition

Chen, C., Li, O., Tao, C., Barnett, A. J., Su, J., & Rudin, C. (2018) · arXiv

Explainable Artificial Intelligence (XAI)Advanced Neural Network Applications

Cited by 702

Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions

Li, O., Liu, H., Chen, C., & Rudin, C. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Explainable Artificial Intelligence (XAI)Neural Networks and Applications

Cited by 541

Why are we using black box models in AI when we don't need to? A lesson from an explainable AI competition

Rudin, C., & Radin, J. (2019) · Harvard Data Science Review

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 432

The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5

Ustun, B., Adler, L. A., Rudin, C., Faraone, S. V., Spencer, T. J., Berglund, P., Gruber, M. J., & Kessler, R. C. (2017) · JAMA Psychiatry

Attention Deficit Hyperactivity DisorderHistorical and Modern Theater Studies

Cited by 423

Interpretable classification models for recidivism prediction

Zeng, J., Ustun, B., & Rudin, C. (2017) · Journal of the Royal Statistical Society Series A: Statistics in Society

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 220

A Shared Vision for Machine Learning in Neuroscience

Vu, M.-A. T., Adalı, T., Ba, D., Buzsáki, G., Carlson, D., Heller, K., Liston, C., Rudin, C., Sohal, V. S., Widge, A. S., et al. (2018) · The Journal of Neuroscience

Functional Brain Connectivity StudiesUndergraduate Neuroscience Education and Research

Cited by 191

The age of secrecy and unfairness in recidivism prediction

Rudin, C., Wang, C., & Coker, B. (2020) · Harvard Data Science Review

Ethics and Social Impacts of AIArtificial Intelligence in Law

Cited by 177

Machine learning for science and society

Rudin, C., & Wagstaff, K. L. (2014) · Machine Learning

Artificial Intelligence ApplicationsEthics and Social Impacts of AI

Cited by 114

Learning Certifiably Optimal Rule Lists for Categorical Data

Angelino, E., Larus-Stone, N., Alabi, D., Seltzer, M., & Rudin, C. (2017) · arXiv

Explainable Artificial Intelligence (XAI)Data Mining Algorithms and Applications

Cited by 108

In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction

Wang, C., Han, B., Patel, B., & Rudin, C. (2023) · Journal of Quantitative Criminology

Ethics and Social Impacts of AICriminal Justice and Corrections Analysis

Cited by 101

Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms

Ustun, B., Westover, M. B., Rudin, C., & Bianchi, M. T. (2016) · Journal of Clinical Sleep Medicine

Obstructive Sleep Apnea ResearchArtificial Intelligence in Healthcare

Cited by 99

On the Existence of Simpler Machine Learning Models

Semenova, L., Rudin, C., & Parr, R. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Machine Learning and AlgorithmsMachine Learning and Data Classification

Cited by 90

Interpretable algorithmic forensics

Garrett, B. L., & Rudin, C. (2023) · Proceedings of the National Academy of Sciences

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 68

Supersparse Linear Integer Models for Interpretable Classification

Ustun, B., Tracà, S., & Rudin, C. (2013) · arXiv

Explainable Artificial Intelligence (XAI)Statistical Methods and Inference

Cited by 26

From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues

The University of North Carolina at Charlotte, Zhou, L., Rudin, C., Duke University, Gombolay, M., Georgia Institute of Technology, Spohrer, J., International Society of Service Innovation Professionals, Zhou, M., Juji, et al. (2023) · AIS Transactions on Human-Computer Interaction

Human-Automation Interaction and SafetyInformation Systems Theories and Implementation

Cited by 20

Exploring the Whole Rashomon Set of Sparse Decision Trees

Xin, R., Zhong, C., Chen, Z., Takagi, T., Seltzer, M., & Rudin, C. (2022) · Advances in Neural Information Processing Systems 35

Data Mining Algorithms and ApplicationsStatistical Methods and Inference

Cited by 12

Learning Cost-Effective Treatment Regimes using Markov Decision Processes

Lakkaraju, H., & Rudin, C. (2016) · arXiv

Advanced Causal Inference TechniquesMachine Learning in Healthcare

Cited by 12