On quantitative aspects of model interpretability

15 July 2020

Papers citing "On quantitative aspects of model interpretability"

19 / 19 papers shown

Title
A constraints-based approach to fully interpretable neural networks for detecting learner behaviors Juan D. Pinto Luc Paquette 43 0 0 10 Apr 2025
Axiomatic Explainer Globalness via Optimal Transport Davin Hill Josh Bone A. Masoomi Max Torop Jennifer Dy 100 1 0 13 Mar 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
A Tale of Two Imperatives: Privacy and Explainability Supriya Manna Niladri Sett 94 0 0 30 Dec 2024
A Fresh Look at Sanity Checks for Saliency Maps Anna Hedström Leander Weber Sebastian Lapuschkin Marina M.-C. Höhne FAtt LRM 37 5 0 03 May 2024
Global Counterfactual Directions Bartlomiej Sobieski P. Biecek DiffM 58 5 0 18 Apr 2024
Towards Evaluating Explanations of Vision Transformers for Medical Imaging Piotr Komorowski Hubert Baniecki P. Biecek MedIm 33 27 0 12 Apr 2023
Less is More: The Influence of Pruning on the Explainability of CNNs David Weber F. Merkle Pascal Schöttle Stephan Schlögl Martin Nocker FAtt 29 1 0 17 Feb 2023
What Makes a Good Explanation?: A Harmonized View of Properties of Explanations Zixi Chen Varshini Subhash Marton Havasi Weiwei Pan Finale Doshi-Velez XAI FAtt 33 18 0 10 Nov 2022
Evaluating the Explainers: Black-Box Explainable Machine Learning for Student Success Prediction in MOOCs Vinitra Swamy Bahar Radmehr Natasa Krco Mirko Marras Tanja Kaser FAtt ELM 11 39 0 01 Jul 2022
Enriching Artificial Intelligence Explanations with Knowledge Fragments Jože M. Rožanec Elena Trajkova I. Novalija Patrik Zajec K. Kenda B. Fortuna Dunja Mladenić 26 9 0 12 Apr 2022
XAI in the context of Predictive Process Monitoring: Too much to Reveal Ghada Elkhawaga Mervat Abuelkheir M. Reichert 14 1 0 16 Feb 2022
A Survey on Methods and Metrics for the Assessment of Explainability under the Proposed AI Act Francesco Sovrano Salvatore Sapienza M. Palmirani F. Vitali 14 17 0 21 Oct 2021
An Objective Metric for Explainable AI: How and Why to Estimate the Degree of Explainability Francesco Sovrano F. Vitali 31 30 0 11 Sep 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White W. Neiswanger 34 65 0 23 Jun 2021
Pitfalls of Explainable ML: An Industry Perspective Sahil Verma Aditya Lahiri John P. Dickerson Su-In Lee XAI 16 9 0 14 Jun 2021
Quantifying Explainers of Graph Neural Networks in Computational Pathology Guillaume Jaume Pushpak Pati Behzad Bozorgtabar Antonio Foncubierta-Rodríguez Florinda Feroce A. Anniciello T. Rau Jean-Philippe Thiran M. Gabrani O. Goksel FAtt 26 76 0 25 Nov 2020
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 36 7 0 23 Oct 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017