General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models

8 July 2020

Christian A. Scholbeck

Giuseppe Casalicchio

Moritz Grosse-Wentrup

Papers citing "General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models"

26 / 26 papers shown

Title
In defence of post-hoc explanations in medical AI Joshua Hatherley Lauritz Munch Jens Christian Bjerring 32 0 0 29 Apr 2025
What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models Jan Kapar Niklas Koenen Martin Jullum 64 0 0 29 Apr 2025
ODExAI: A Comprehensive Object Detection Explainable AI Evaluation Loc Phuc Truong Nguyen Hung Truong Thanh Nguyen Hung Cao 68 0 0 27 Apr 2025
The Curious Case of Arbitrariness in Machine Learning Prakhar Ganesh Afaf Taik G. Farnadi 59 2 0 28 Jan 2025
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics Lukas Klein Carsten T. Lüth U. Schlegel Till J. Bungert Mennatallah El-Assady Paul F. Jäger XAI ELM 42 2 0 03 Jan 2025
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study Szymon Bobek Paloma Korycińska Monika Krakowska Maciej Mozolewski Dorota Rak Magdalena Zych Magdalena Wójcik Grzegorz J. Nalepa ELM 32 1 0 21 Oct 2024
CHILLI: A data context-aware perturbation method for XAI Saif Anwar Nathan Griffiths A. Bhalerao T. Popham 35 0 0 10 Jul 2024
Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots Xi Xin Giles Hooker Fei Huang AAML 38 6 0 29 Apr 2024
Variable Importance in High-Dimensional Settings Requires Grouping Ahmad Chamma Bertrand Thirion D. Engemann 43 4 0 18 Dec 2023
Machine Learning For An Explainable Cost Prediction of Medical Insurance U. Orji Elochukwu A. Ukwandu 26 31 0 23 Nov 2023
On the Relationship Between Interpretability and Explainability in Machine Learning Benjamin Leblanc Pascal Germain FaML 26 0 0 20 Nov 2023
Statistically Valid Variable Importance Assessment through Conditional Permutations Ahmad Chamma D. Engemann Bertrand Thirion 20 11 0 14 Sep 2023
Confident Feature Ranking Bitya Neuhof Y. Benjamini FAtt 26 3 0 28 Jul 2023
Don't Lie to Me: Avoiding Malicious Explanations with STEALTH Lauren Alvarez Tim Menzies 26 2 0 25 Jan 2023
A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection M. Wildi Branka Hadji Misheva AI4TS 17 1 0 06 Dec 2022
Comparing Explanation Methods for Traditional Machine Learning Models Part 2: Quantifying Model Explainability Faithfulness and Improvements with Dimensionality Reduction Montgomery Flora Corey K. Potvin A. McGovern Shawn Handler FAtt 26 4 0 18 Nov 2022
Algorithm-Agnostic Interpretations for Clustering Christian A. Scholbeck Henri Funk Giuseppe Casalicchio 23 0 0 21 Sep 2022
REPID: Regional Effect Plots with implicit Interaction Detection J. Herbinger Bernd Bischl Giuseppe Casalicchio FAtt 18 15 0 15 Feb 2022
Causal Explanations and XAI Sander Beckers CML XAI 26 34 0 31 Jan 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
Counterfactual Explanations for Models of Code Jürgen Cito Işıl Dillig V. Murali S. Chandra AAML LRM 29 47 0 10 Nov 2021
Machine learning methods for prediction of cancer driver genes: a survey paper R. Andrades M. R. Mendoza 23 26 0 28 Sep 2021
How to avoid machine learning pitfalls: a guide for academic researchers M. Lones VLM FaML OnRL 62 77 0 05 Aug 2021
Grouped Feature Importance and Combined Features Effect Plot Quay Au J. Herbinger Clemens Stachl B. Bischl Giuseppe Casalicchio FAtt 45 44 0 23 Apr 2021
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017
Measuring and testing dependence by correlation of distances G. Székely Maria L. Rizzo N. K. Bakirov 177 2,577 0 28 Mar 2008