On quantitative aspects of model interpretability

15 July 2020

Papers citing "On quantitative aspects of model interpretability"

25 / 25 papers shown

Title
A constraints-based approach to fully interpretable neural networks for detecting learner behaviors Juan D. Pinto Luc Paquette 48 0 0 10 Apr 2025
Axiomatic Explainer Globalness via Optimal Transport Davin Hill Josh Bone A. Masoomi Max Torop Jennifer Dy 105 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 3 0 03 Jan 2025
Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Supriya Manna Niladri Sett 168 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 59 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 38 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 34 1 0 17 Feb 2023
The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus Anna Hedström P. Bommer Kristoffer K. Wickstrom Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne 37 21 0 14 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 39 18 0 10 Nov 2022
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations Ricardo Müller Marco Schreyer Timur Sattarov Damian Borth AAML MLAU 35 7 0 19 Sep 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 13 40 0 01 Jul 2022
Interpretation Quality Score for Measuring the Quality of interpretability methods Sean Xie Soroush Vosoughi Saeed Hassanpour XAI 19 5 0 24 May 2022
Enriching Artificial Intelligence Explanations with Knowledge Fragments Jože M. Rožanec Elena Trajkova I. Novalija Patrik Zajec K. Kenda B. Fortuna Dunja Mladenić 28 9 0 12 Apr 2022
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful Models Alexander Stevens Johannes De Smedt XAI FaML 17 12 0 30 Mar 2022
XAI in the context of Predictive Process Monitoring: Too much to Reveal Ghada Elkhawaga Mervat Abuelkheir M. Reichert 20 1 0 16 Feb 2022
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond Anna Hedström Leander Weber Dilyara Bareeva Daniel G. Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne XAI ELM 21 169 0 14 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 37 30 0 11 Sep 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White Willie Neiswanger 37 65 0 23 Jun 2021
A Framework for Evaluating Post Hoc Feature-Additive Explainers Zachariah Carmichael Walter J. Scheirer FAtt 46 4 0 15 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 44 7 0 23 Oct 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,696 0 28 Feb 2017