How Interpretable and Trustworthy are GAMs?

How Interpretable and Trustworthy are GAMs?

11 June 2020

Benjamin J. Lengerich

Anna Goldenberg

Papers citing "How Interpretable and Trustworthy are GAMs?"

17 / 17 papers shown

Title
Challenges in interpretability of additive models Xinyu Zhang Julien Martinelli S. T. John AAML AI4CE 27 1 0 14 Apr 2025
SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator Lev V. Utkin Danila Eremenko A. Konstantinov 30 4 0 07 Aug 2023
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models Julien N. Siems Konstantin Ditschuneit Winfried Ripken Alma Lindborg Maximilian Schambach Johannes Otterbach Martin Genzel 19 6 0 19 May 2023
Interpretability with full complexity by constraining feature information Kieran A. Murphy Danielle Bassett FAtt 27 5 0 30 Nov 2022
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature Interactions Minkyu Kim Hyunjin Choi Jinho Kim FAtt 32 8 0 30 Sep 2022
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization Zijie J. Wang Chudi Zhong Rui Xin Takuya Takagi Zhi Chen Duen Horng Chau Cynthia Rudin Margo Seltzer 33 14 0 19 Sep 2022
Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values Zijie J. Wang Alex Kale Harsha Nori P. Stella M. Nunnally Duen Horng Chau Mihaela Vorvoreanu J. W. Vaughan R. Caruana KELM 56 27 0 30 Jun 2022
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Aparna Balagopalan Haoran Zhang Kimia Hamidieh Thomas Hartvigsen Frank Rudzicz Marzyeh Ghassemi 38 77 0 06 May 2022
Differentially Private Estimation of Heterogeneous Causal Effects Fengshi Niu Harsha Nori B. Quistorff R. Caruana Donald Ngwe A. Kannan CML 17 13 0 22 Feb 2022
GAM Changer: Editing Generalized Additive Models with Interactive Visualization Zijie J. Wang Alex Kale Harsha Nori P. Stella M. Nunnally Duen Horng Chau Mihaela Vorvoreanu Jennifer Wortman Vaughan R. Caruana KELM 19 24 0 06 Dec 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting Harsha Nori R. Caruana Zhiqi Bu J. Shen Janardhan Kulkarni 30 37 0 17 Jun 2021
A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts Gesina Schwalbe Bettina Finzel XAI 26 184 0 15 May 2021
Ensembles of Random SHAPs Lev V. Utkin A. Konstantinov FAtt 16 20 0 04 Mar 2021
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions Zebin Yang Aijun Zhang Agus Sudjianto FAtt 19 126 0 16 Mar 2020
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 323 4,212 0 23 Aug 2019
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,082 0 24 Oct 2016