Individualized help for at-risk students using model-agnostic and counterfactual explanations

A Unified Approach to Interpreting Model Predictions

Scott Lundberg, Su‐In Lee · arXiv (Cornell University) · 2017 · 7.6K citations · Full text

Computing, Computer Science, Data Science +17

Bias in random forest variable importance measures: Illustrations, sources and a solution

Carolin Strobl, Anne‐Laure Boulesteix, Achim Zeileis et al. · BMC Bioinformatics · 2007 · 3.6K citations · Full text

Computing, Bioinformatics, Biostatistics +16

Model-Agnostic Interpretability of Machine Learning

Marco Túlio Ribeiro, Sameer Singh, Carlos Guestrin · arXiv (Cornell University) · 2016 · 699 citations · Full text

Computing, Computer Science, Artificial Intelligence +13

Understanding Random Forests: From Theory to Practice

Gilles Louppe · arXiv (Cornell University) · 2014 · 596 citations · Full text

Computing, Computer Science, Algorithms +13

Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR

Sandra Wachter, Brent Mittelstadt, Chris Russell · SSRN Electronic Journal · 2017 · 561 citations · Full text

Computing, Public Policy, Regulatory Policy +17