An Interpretable Model with Globally Consistent Explanations for Credit
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An Interpretable Model with Globally Consistent Explanations for Credit Risk

30 November 2018

Yaron Shaposhnik

Papers citing "An Interpretable Model with Globally Consistent Explanations for Credit Risk"

17 / 17 papers shown

Title
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations Dan Ley Saumitra Mishra Daniele Magazzeni LRM 38 16 0 26 May 2023
Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance Dangxing Chen Luyao Zhang SyDa 18 6 0 17 Jan 2023
Interpretable ML for Imbalanced Data Damien Dablain C. Bellinger Bartosz Krawczyk D. Aha Nitesh V. Chawla 24 1 0 15 Dec 2022
Computing Rule-Based Explanations by Leveraging Counterfactuals Zixuan Geng Maximilian Schleich Dan Suciu 43 4 0 31 Oct 2022
Interpretable Selective Learning in Credit Risk Dangxing Chen Weicheng Ye Jiahui Ye FaML 30 14 0 21 Sep 2022
Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring Dangxing Chen Weicheng Ye FaML 29 13 0 21 Sep 2022
To what extent should we trust AI models when they extrapolate? Roozbeh Yousefzadeh Xuenan Cao 27 5 0 27 Jan 2022
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence Kacper Sokol Peter A. Flach 39 20 0 29 Dec 2021
Unpacking the Black Box: Regulating Algorithmic Decisions Laura Blattner Scott Nelson Jann Spiess MLAU FaML 28 19 0 05 Oct 2021
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang Tong Wang 19 41 0 04 Jun 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 59 653 0 20 Mar 2021
Score-Based Explanations in Data Management and Machine Learning Leopoldo Bertossi FAtt XAI 18 4 0 24 Jul 2020
The Penalty Imposed by Ablated Data Augmentation Frederick Liu A. Najmi Mukund Sundararajan 17 6 0 08 Jun 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
VINE: Visualizing Statistical Interactions in Black Box Models M. Britton FAtt 17 21 0 01 Apr 2019
Interpreting Neural Networks Using Flip Points Roozbeh Yousefzadeh D. O’Leary AAML FAtt 22 17 0 21 Mar 2019
Learning Optimized Risk Scores Berk Ustun Cynthia Rudin 17 82 0 01 Oct 2016