Towards falsifiable interpretability research

22 October 2020

Papers citing "Towards falsifiable interpretability research"

24 / 24 papers shown

Title
A Mathematical Philosophy of Explanations in Mechanistic Interpretability -- The Strange Science Part I.i Kola Ayonrinde Louis Jaburi MILM 88 1 0 01 May 2025
An Actionability Assessment Tool for Explainable AI Ronal Singh Tim Miller L. Sonenberg Eduardo Velloso F. Vetere Piers Howe Paul Dourish 27 2 0 19 Jun 2024
Acoustic characterization of speech rhythm: going beyond metrics with recurrent neural networks Franccois Deloche Laurent Bonnasse-Gahot Judit Gervain 26 0 0 22 Jan 2024
On the Relationship Between Interpretability and Explainability in Machine Learning Benjamin Leblanc Pascal Germain FaML 29 0 0 20 Nov 2023
Identifying Interpretable Visual Features in Artificial and Biological Neural Systems David A. Klindt Sophia Sanborn Francisco Acosta Frédéric Poitevin Nina Miolane MILM FAtt 44 7 0 17 Oct 2023
Causal Analysis for Robust Interpretability of Neural Networks Ola Ahmad Nicolas Béreux Loïc Baret V. Hashemi Freddy Lecue CML 29 3 0 15 May 2023
The Representational Status of Deep Learning Models Eamon Duede 21 0 0 21 Mar 2023
Tracr: Compiled Transformers as a Laboratory for Interpretability David Lindner János Kramár Sebastian Farquhar Matthew Rahtz Tom McGrath Vladimir Mikulik 29 72 0 12 Jan 2023
Higher-order mutual information reveals synergistic sub-networks for multi-neuron importance Kenzo Clauw S. Stramaglia Daniele Marinazzo SSL FAtt 30 6 0 01 Nov 2022
SoK: Explainable Machine Learning for Computer Security Applications A. Nadeem D. Vos Clinton Cao Luca Pajola Simon Dieck Robert Baumgartner S. Verwer 34 40 0 22 Aug 2022
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 47 18 0 31 May 2022
Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI Greta Warren Mark T. Keane R. Byrne CML 27 22 0 21 Apr 2022
An explainability framework for cortical surface-based deep learning Fernanda L. Ribeiro S. Bollmann R. Cunnington A. M. Puckett FAtt AAML MedIm 24 2 0 15 Mar 2022
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience Antonios Mamalakis E. Barnes I. Ebert‐Uphoff 29 73 0 07 Feb 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 0 06 Dec 2021
How Well do Feature Visualizations Support Causal Understanding of CNN Activations? Roland S. Zimmermann Judy Borowski Robert Geirhos Matthias Bethge Thomas S. A. Wallis Wieland Brendel FAtt 47 31 0 23 Jun 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar A. Madry FAtt 22 88 0 11 May 2021
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset Antonios Mamalakis I. Ebert‐Uphoff E. Barnes OOD 28 75 0 18 Mar 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 23 57 0 25 Feb 2021
Estimating Example Difficulty Using Variance of Gradients Chirag Agarwal Daniel D'souza Sara Hooker 210 107 0 26 Aug 2020
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs Matthew L. Leavitt Ari S. Morcos 58 33 0 03 Mar 2020
Revisiting the Importance of Individual Units in CNNs via Ablation Bolei Zhou Yiyou Sun David Bau Antonio Torralba FAtt 59 116 0 07 Jun 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,690 0 28 Feb 2017