Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring

28 September 2020

Papers citing "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring"

10 / 10 papers shown

Title
Axiomatic Explainer Globalness via Optimal Transport Davin Hill Josh Bone A. Masoomi Max Torop Jennifer Dy 107 1 0 13 Mar 2025
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots Xi Xin Giles Hooker Fei Huang AAML 51 7 0 29 Apr 2024
Glocal Explanations of Expected Goal Models in Soccer Mustafa Cavus Adrian Stando P. Biecek 35 4 0 29 Aug 2023
Interpretable Selective Learning in Credit Risk Dangxing Chen Weicheng Ye Jiahui Ye FaML 35 15 0 21 Sep 2022
Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview Florian Karl Tobias Pielok Julia Moosbauer Florian Pfisterer Stefan Coors ... Jakob Richter Michel Lang Eduardo C. Garrido-Merchán Juergen Branke B. Bischl AI4CE 31 57 0 15 Jun 2022
Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring Marc Schmitt 46 20 0 21 May 2022
Deep Learning in Business Analytics: A Clash of Expectations and Reality Marc Schmitt 43 53 0 19 May 2022
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 59 93 0 05 Mar 2020
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R Marvin N. Wright A. Ziegler 124 2,741 0 18 Aug 2015