Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement

16 November 2022

Papers citing "Comparing Explanation Methods for Traditional Machine Learning Models Part 1: An Overview of Current Methods and Quantifying Their Disagreement"

4 / 4 papers shown

Title
Feature Importance Depends on Properties of the Data: Towards Choosing the Correct Explanations for Your Data and Decision Trees based Models Célia Wafa Ayad Thomas Bonnier Benjamin Bosch Sonali Parbhoo Jesse Read FAtt XAI 92 0 0 11 Feb 2025
Grouped Feature Importance and Combined Features Effect Plot Quay Au J. Herbinger Clemens Stachl B. Bischl Giuseppe Casalicchio FAtt 39 44 0 23 Apr 2021
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 244 425 0 15 Oct 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 227 3,681 0 28 Feb 2017