Definitions, methods, and applications in interpretable machine learning

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 116; no. 44; pp. 22071 - 22080
• Main Authors: Murdoch, W. James, Singh, Chandan, Kumbier, Karl, Abbasi-Asl, Reza, Yu, Bin
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 29.10.2019
• Subjects: Artificial intelligence; Computer simulation; explainability; interpretability; Learning algorithms; Machine learning; MATHEMATICS AND COMPUTING; Modularity; Physical Sciences; Predictions; relevancy; Subgroups; interpretability; relevancy; explainability; machine learning

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this...