False perfection in machine prediction: Detecting and assessing circularity problems in machine learning

23 June 2021

Papers citing "False perfection in machine prediction: Detecting and assessing circularity problems in machine learning"

10 / 10 papers shown

Title
Beyond Leaderboards: A survey of methods for revealing weaknesses in Natural Language Inference data and models Viktor Schlegel Goran Nenadic Riza Batista-Navarro ELM 47 18 0 29 May 2020
Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal Levi Melnick Nicholas Frosst Xuezhou Zhang Ben Lengerich R. Caruana Geoffrey E. Hinton 72 417 0 29 Apr 2020
Leveraging Implicit Expert Knowledge for Non-Circular Machine Learning in Sepsis Prediction Shigehiko Schamoni H. Lindner Verena Schneider-Lindner M. Thiel Stefan Riezler 42 23 0 20 Sep 2019
Don't Take the Easy Way Out: Ensemble Based Methods for Avoiding Known Dataset Biases Christopher Clark Mark Yatskar Luke Zettlemoyer OOD 69 465 0 09 Sep 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 80 1,009 0 26 Feb 2019
Learning Not to Learn: Training Deep Neural Networks with Biased Data Byungju Kim Hyunwoo Kim Kyungsu Kim Sungjin Kim Junmo Kim OOD 57 409 0 26 Dec 2018
Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation S. Tan R. Caruana Giles Hooker Yin Lou MLAU 101 185 0 17 Oct 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 234 4,249 0 22 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 366 3,776 0 28 Feb 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 854 16,891 0 16 Feb 2016