Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

10 November 2020

Papers citing "Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End"

21 / 21 papers shown

Title
Exploring the Impact of Explainable AI and Cognitive Capabilities on Users' Decisions Federico Maria Cau Lucio Davide Spano 31 0 0 02 May 2025
Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attacks Jeremy Goldwasser Giles Hooker AAML 28 0 0 21 Apr 2025
Robot Pouring: Identifying Causes of Spillage and Selecting Alternative Action Parameters Using Probabilistic Actual Causation Jaime Maldonado Jonas Krumme C. Zetzsche Vanessa Didelez K. Schill 106 0 0 13 Feb 2025
Unlearning-based Neural Interpretations Ching Lam Choi Alexandre Duplessis Serge Belongie FAtt 47 0 0 10 Oct 2024
Towards a Non-Ideal Methodological Framework for Responsible ML Ramaravind Kommiya Mothilal Shion Guha Syed Ishtiaque Ahmed 44 7 0 20 Jan 2024
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations Emanuele Albini Shubham Sharma Saumitra Mishra Danial Dervovic Daniele Magazzeni FAtt 46 2 0 13 Jul 2023
Counterfactual Explanations of Neural Network-Generated Response Curves Giorgio Morales John W. Sheppard 21 1 0 08 Apr 2023
Towards Learning and Explaining Indirect Causal Effects in Neural Networks Abbaavaram Gowtham Reddy Saketh Bachu Harsh Nilesh Pathak Ben Godfrey V. Balasubramanian V. Varshaneya Satya Narayanan Kar CML 31 0 0 24 Mar 2023
SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective Xiwei Xuan Ziquan Deng Hsuan-Tien Lin Z. Kong Kwan-Liu Ma AAML FAtt 35 2 0 01 Mar 2023
The Counterfactual-Shapley Value: Attributing Change in System Metrics Amit Sharma Hua Li Jian Jiao 29 2 0 17 Aug 2022
Combining Counterfactuals With Shapley Values To Explain Image Models Aditya Lahiri Kamran Alipour Ehsan Adeli Babak Salimi FAtt 28 6 0 14 Jun 2022
Can counterfactual explanations of AI systems' predictions skew lay users' causal intuitions about the world? If so, can we correct for that? Marko Tešić U. Hahn CML 17 5 0 12 May 2022
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection Esma Balkir I. Nejadgholi Kathleen C. Fraser S. Kiritchenko FAtt 33 27 0 06 May 2022
Causal Explanations and XAI Sander Beckers CML XAI 26 34 0 31 Jan 2022
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts Sebastian Bordt Michèle Finck Eric Raidl U. V. Luxburg AILaw 39 77 0 25 Jan 2022
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning Sindre Benjamin Remman Inga Strümke A. Lekkas CML 13 7 0 04 Nov 2021
Counterfactual Shapley Additive Explanations Emanuele Albini Jason Long Danial Dervovic Daniele Magazzeni 26 49 0 27 Oct 2021
Model Explanations via the Axiomatic Causal Lens Gagan Biradar Vignesh Viswanathan Yair Zick XAI CML 25 1 0 08 Sep 2021
Desiderata for Representation Learning: A Causal Perspective Yixin Wang Michael I. Jordan CML 32 80 0 08 Sep 2021
GeCo: Quality Counterfactual Explanations in Real Time Maximilian Schleich Zixuan Geng Yihong Zhang D. Suciu 46 61 0 05 Jan 2021
Determinantal point processes for machine learning Alex Kulesza B. Taskar 162 1,123 0 25 Jul 2012