On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations

22 January 2022

Papers citing "On the Robustness of Sparse Counterfactual Explanations to Adverse Perturbations"

36 / 36 papers shown

Title
Understanding Fixed Predictions via Confined Regions Connor Lawless Tsui-Wei Weng Berk Ustun Madeleine Udell 70 0 0 22 Feb 2025
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 95 19 0 31 May 2022
On the Adversarial Robustness of Causal Algorithmic Recourse Ricardo Dominguez-Olmedo Amir-Hossein Karimi Bernhard Schölkopf 60 63 0 21 Dec 2021
Retiring Adult: New Datasets for Fair Machine Learning Frances Ding Moritz Hardt John Miller Ludwig Schmidt 146 445 0 10 Aug 2021
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis Martin Pawelczyk Chirag Agarwal Shalmali Joshi Sohini Upadhyay Himabindu Lakkaraju AAML 47 53 0 18 Jun 2021
Counterfactual Explanations Can Be Manipulated Dylan Slack Sophie Hilgard Himabindu Lakkaraju Sameer Singh 53 136 0 04 Jun 2021
Model Learning with Personalized Interpretability Estimation (ML-PIE) M. Virgolin A. D. Lorenzo Francesca Randone Eric Medvet M. Wahde 46 31 0 13 Apr 2021
Evaluating Robustness of Counterfactual Explanations André Artelt Valerie Vaquet Riza Velioglu Fabian Hinder Johannes Brinkrolf M. Schilling Barbara Hammer 69 46 0 03 Mar 2021
Distances with mixed type variables some modified Gower's coefficients M. DÓrazio 29 14 0 07 Jan 2021
Algorithmic Recourse in the Wild: Understanding the Impact of Data and Model Shifts Kaivalya Rawal Ece Kamar Himabindu Lakkaraju 35 41 0 22 Dec 2020
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations Andrea Ferrario M. Loi 47 5 0 09 Oct 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 57 63 0 11 Sep 2020
On Counterfactual Explanations under Predictive Multiplicity Martin Pawelczyk Klaus Broelemann Gjergji Kasneci 102 85 0 23 Jun 2020
Array Programming with NumPy Charles R. Harris K. Millman S. Walt R. Gommers Pauli Virtanen ... Tyler Reddy Warren Weckesser Hameer Abbasi C. Gohlke T. Oliphant 61 14,757 0 18 Jun 2020
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi Julius von Kügelgen Bernhard Schölkopf Isabel Valera CML 94 179 0 11 Jun 2020
Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI) Mark T. Keane Barry Smyth CML 62 146 0 26 May 2020
Genetic programming approaches to learning fair classifiers William La Cava J. Moore FaML 32 19 0 28 Apr 2020
Multi-Objective Counterfactual Explanations Susanne Dandl Christoph Molnar Martin Binder B. Bischl 52 252 0 23 Apr 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions Amir-Hossein Karimi Bernhard Schölkopf Isabel Valera CML 44 340 0 14 Feb 2020
Deep Double Descent: Where Bigger Models and More Data Hurt Preetum Nakkiran Gal Kaplun Yamini Bansal Tristan Yang Boaz Barak Ilya Sutskever 105 925 0 04 Dec 2019
Imperceptible Adversarial Attacks on Tabular Data Vincent Ballet X. Renard Jonathan Aigrain Thibault Laugel P. Frossard Marcin Detyniecki 50 72 0 08 Nov 2019
The Dangers of Post-hoc Interpretability: Unjustified Counterfactual Explanations Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 40 196 0 22 Jul 2019
Interpretable Counterfactual Explanations Guided by Prototypes A. V. Looveren Janis Klaise FAtt 47 380 0 03 Jul 2019
Artificial Intelligence: the global landscape of ethics guidelines Anna Jobin M. Ienca E. Vayena 62 1,607 0 24 Jun 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations R. Mothilal Amit Sharma Chenhao Tan CML 100 1,005 0 19 May 2019
Reconciling modern machine learning practice and the bias-variance trade-off M. Belkin Daniel J. Hsu Siyuan Ma Soumik Mandal 174 1,628 0 28 Dec 2018
This Looks Like That: Deep Learning for Interpretable Image Recognition Chaofan Chen Oscar Li Chaofan Tao A. Barnett Jonathan Su Cynthia Rudin 177 1,172 0 27 Jun 2018
Local Rule-Based Explanations of Black Box Decision Systems Riccardo Guidotti A. Monreale Salvatore Ruggieri D. Pedreschi Franco Turini F. Giannotti 116 436 0 28 May 2018
Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives Amit Dhurandhar Pin-Yu Chen Ronny Luss Chun-Chen Tu Pai-Shun Ting Karthikeyan Shanmugam Payel Das FAtt 91 587 0 21 Feb 2018
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections Xin Zhang Armando Solar-Lezama Rishabh Singh FAtt 88 63 0 21 Feb 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 83 3,922 0 06 Feb 2018
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness Michael Kearns Seth Neel Aaron Roth Zhiwei Steven Wu FaML 109 775 0 14 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 81 2,332 0 01 Nov 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 552 21,613 0 22 May 2017
European Union regulations on algorithmic decision-making and a "right to explanation" B. Goodman Seth Flaxman FaML AILaw 58 1,888 0 28 Jun 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 587 16,828 0 16 Feb 2016