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Counterfactual Explanations Can Be Manipulated

4 June 2021

Himabindu Lakkaraju

Papers citing "Counterfactual Explanations Can Be Manipulated"

39 / 89 papers shown

Title
Faithfulness Tests for Natural Language Explanations Pepa Atanasova Oana-Maria Camburu Christina Lioma Thomas Lukasiewicz J. Simonsen Isabelle Augenstein FAtt 120 67 0 29 May 2023
Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors Giorgos Filandrianos Edmund Dervakos Orfeas Menis Mastromichalakis Chrysoula Zerva Giorgos Stamou AAML 90 5 0 26 May 2023
GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations Dan Ley Saumitra Mishra Daniele Magazzeni LRM 95 19 0 26 May 2023
Sequential Integrated Gradients: a simple but effective method for explaining language models Joseph Enguehard 79 43 0 25 May 2023
Achieving Diversity in Counterfactual Explanations: a Review and Discussion Thibault Laugel Adulam Jeyasothy Marie-Jeanne Lesot Christophe Marsala Marcin Detyniecki CML 62 13 0 10 May 2023
Algorithmic Recourse with Missing Values Kentaro Kanamori Takuya Takagi Ken Kobayashi Yuichi Ike 60 2 0 28 Apr 2023
Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study L. Herm 73 22 0 18 Apr 2023
Iterative Partial Fulfillment of Counterfactual Explanations: Benefits and Risks Yilun Zhou 62 0 0 17 Mar 2023
Explaining Groups of Instances Counterfactually for XAI: A Use Case, Algorithm and User Study for Group-Counterfactuals Greta Warren Markt. Keane Christophe Guéret Eoin Delaney 56 13 0 16 Mar 2023
"How to make them stay?" -- Diverse Counterfactual Explanations of Employee Attrition André Artelt Andreas Gregoriades 86 5 0 08 Mar 2023
GAM Coach: Towards Interactive and User-centered Algorithmic Recourse Zijie J. Wang J. W. Vaughan R. Caruana Duen Horng Chau HAI 94 22 0 27 Feb 2023
Robustness Implies Fairness in Causal Algorithmic Recourse A. Ehyaei Amir-Hossein Karimi Bernhard Schölkopf S. Maghsudi FaML 72 12 0 07 Feb 2023
Finding Regions of Counterfactual Explanations via Robust Optimization Donato Maragno Jannis Kurtz Tabea E. Rober Rob Goedhart cS. .Ilker Birbil D. Hertog 148 22 0 26 Jan 2023
Don't Lie to Me: Avoiding Malicious Explanations with STEALTH Lauren Alvarez Tim Menzies 83 4 0 25 Jan 2023
Bayesian Hierarchical Models for Counterfactual Estimation Natraj Raman Daniele Magazzeni Sameena Shah 56 5 0 21 Jan 2023
"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations André Artelt Barbara Hammer FaML 84 8 0 27 Nov 2022
On the Privacy Risks of Algorithmic Recourse Martin Pawelczyk Himabindu Lakkaraju Seth Neel 86 31 0 10 Nov 2022
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations Julia El Zini M. Awad AAML 62 2 0 17 Oct 2022
FASTER-CE: Fast, Sparse, Transparent, and Robust Counterfactual Explanations Shubham Sharma Alan H. Gee Jette Henderson Joydeep Ghosh CML BDL LRM 54 5 0 12 Oct 2022
Formalising the Robustness of Counterfactual Explanations for Neural Networks Junqi Jiang Francesco Leofante Antonio Rago Francesca Toni AAML 91 27 0 31 Aug 2022
On the Trade-Off between Actionable Explanations and the Right to be Forgotten Martin Pawelczyk Tobias Leemann Asia J. Biega Gjergji Kasneci FaML MU 109 23 0 30 Aug 2022
Inferring Sensitive Attributes from Model Explanations Vasisht Duddu A. Boutet MIACV SILM 85 17 0 21 Aug 2022
Interpretability Guarantees with Merlin-Arthur Classifiers S. Wäldchen Kartikey Sharma Berkant Turan Max Zimmer Sebastian Pokutta FAtt 73 5 0 01 Jun 2022
Global Counterfactual Explanations: Investigations, Implementations and Improvements Dan Ley Saumitra Mishra Daniele Magazzeni LRM 78 12 0 14 Apr 2022
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse Martin Pawelczyk Teresa Datta Johannes van-den-Heuvel Gjergji Kasneci Himabindu Lakkaraju 89 38 0 13 Mar 2022
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four S. Wäldchen Felix Huber Sebastian Pokutta FAtt 61 8 0 23 Feb 2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis Thomas Fel Mélanie Ducoffe David Vigouroux Rémi Cadène Mikael Capelle C. Nicodeme Thomas Serre AAML 68 42 0 15 Feb 2022
Framework for Evaluating Faithfulness of Local Explanations S. Dasgupta Nave Frost Michal Moshkovitz FAtt 212 63 0 01 Feb 2022
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts Sebastian Bordt Michèle Finck Eric Raidl U. V. Luxburg AILaw 106 79 0 25 Jan 2022
On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations M. Virgolin Saverio Fracaros CML 92 36 0 22 Jan 2022
On the Adversarial Robustness of Causal Algorithmic Recourse Ricardo Dominguez-Olmedo Amir-Hossein Karimi Bernhard Schölkopf 103 64 0 21 Dec 2021
Bayesian Persuasion for Algorithmic Recourse Keegan Harris Valerie Chen Joon Sik Kim Ameet Talwalkar Hoda Heidari Zhiwei Steven Wu 64 15 0 12 Dec 2021
Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions Prateek Yadav Peter Hase Joey Tianyi Zhou 68 11 0 01 Nov 2021
A Survey on the Robustness of Feature Importance and Counterfactual Explanations Saumitra Mishra Sanghamitra Dutta Jason Long Daniele Magazzeni AAML 84 58 0 30 Oct 2021
Transport-based Counterfactual Models Lucas de Lara Alberto González Sanz Nicholas M. Asher Laurent Risser Jean-Michel Loubes 52 31 0 30 Aug 2021
When and How to Fool Explainable Models (and Humans) with Adversarial Examples Jon Vadillo Roberto Santana Jose A. Lozano SILM AAML 99 14 0 05 Jul 2021
Fooling Partial Dependence via Data Poisoning Hubert Baniecki Wojciech Kretowicz P. Biecek AAML 83 23 0 26 May 2021
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review Sahil Verma Varich Boonsanong Minh Hoang Keegan E. Hines John P. Dickerson Chirag Shah CML 178 177 0 20 Oct 2020
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.3K 17,197 0 16 Feb 2016