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Sarah Tan
How Interpretable and Trustworthy are GAMs?

Jun 11, 2020
Chun-Hao Chang, Sarah Tan, Ben Lengerich, Anna Goldenberg, Rich Caruana


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Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models

Nov 12, 2019
Benjamin Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana


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"Why Should You Trust My Explanation?" Understanding Uncertainty in LIME Explanations

Jun 04, 2019
Yujia Zhang, Kuangyan Song, Yiming Sun, Sarah Tan, Madeleine Udell


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Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models

Oct 22, 2018
Xuezhou Zhang, Sarah Tan, Paul Koch, Yin Lou, Urszula Chajewska, Rich Caruana

* Preprint 
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Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation

Oct 11, 2018
Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou

* Camera-ready version for AAAI/ACM AIES 2018. Data and pseudocode at https://github.com/shftan/auditblackbox. Previously titled "Detecting Bias in Black-Box Models Using Transparent Model Distillation". A short version was presented at NIPS 2017 Symposium on Interpretable Machine Learning 
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Investigating Human + Machine Complementarity for Recidivism Predictions

Aug 28, 2018
Sarah Tan, Julius Adebayo, Kori Inkpen, Ece Kamar


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Transparent Model Distillation

Jan 26, 2018
Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo


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A Double Parametric Bootstrap Test for Topic Models

Nov 21, 2017
Skyler Seto, Sarah Tan, Giles Hooker, Martin T. Wells

* Presented at NIPS 2017 Symposium on Interpretable Machine Learning 
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