NICE: An Algorithm for Nearest Instance Counterfactual Explanations

15 April 2021

Papers citing "NICE: An Algorithm for Nearest Instance Counterfactual Explanations"

40 / 40 papers shown

Title
Enhancing Metabolic Syndrome Prediction with Hybrid Data Balancing and Counterfactuals Sanyam Paresh Shah Abdullah Mamun Shovito Barua Soumma Hassan Ghasemzadeh 118 0 0 09 Apr 2025
GraphXAIN: Narratives to Explain Graph Neural Networks Mateusz Cedro David Martens 110 0 0 04 Nov 2024
Distributional Counterfactual Explanations With Optimal Transport Lei You Lele Cao Mattias Nilsson Bo Zhao Lei Lei OT OffRL 90 1 0 23 Jan 2024
A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data Raphael Mazzine David Martens 64 33 0 09 Jul 2021
Understanding Consumer Preferences for Explanations Generated by XAI Algorithms Yanou Ramon T. Vermeire Olivier Toubia David Martens Theodoros Evgeniou 55 10 0 06 Jul 2021
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques Mark T. Keane Eoin M. Kenny Eoin Delaney Barry Smyth CML 58 146 0 26 Feb 2021
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research Markus Langer Daniel Oster Timo Speith Holger Hermanns Lena Kästner Eva Schmidt Andreas Sesing Kevin Baum XAI 100 423 0 15 Feb 2021
GeCo: Quality Counterfactual Explanations in Real Time Maximilian Schleich Zixuan Geng Yihong Zhang D. Suciu 66 63 0 05 Jan 2021
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End R. Mothilal Divyat Mahajan Chenhao Tan Amit Sharma FAtt CML 42 100 0 10 Nov 2020
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects Amir-Hossein Karimi Gilles Barthe Bernhard Schölkopf Isabel Valera FaML 58 172 0 08 Oct 2020
Instance-based Counterfactual Explanations for Time Series Classification Eoin Delaney Derek Greene Mark T. Keane CML AI4TS 44 91 0 28 Sep 2020
On Counterfactual Explanations under Predictive Multiplicity Martin Pawelczyk Klaus Broelemann Gjergji Kasneci 119 86 0 23 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 64 146 0 26 May 2020
Multi-Objective Counterfactual Explanations Susanne Dandl Christoph Molnar Martin Binder B. Bischl 57 257 0 23 Apr 2020
Explaining Data-Driven Decisions made by AI Systems: The Counterfactual Approach Carlos Fernandez F. Provost Xintian Han CML 41 70 0 21 Jan 2020
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches Kacper Sokol Peter A. Flach XAI 80 302 0 11 Dec 2019
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers Divyat Mahajan Chenhao Tan Amit Sharma OOD CML 89 205 0 06 Dec 2019
Counterfactual Explanation Algorithms for Behavioral and Textual Data Yanou Ramon David Martens F. Provost Theodoros Evgeniou FAtt 90 88 0 04 Dec 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 113 6,235 0 22 Oct 2019
Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systems Shalmali Joshi Oluwasanmi Koyejo Warut D. Vijitbenjaronk Been Kim Joydeep Ghosh FaML 58 187 0 22 Jul 2019
The What-If Tool: Interactive Probing of Machine Learning Models James Wexler Mahima Pushkarna Tolga Bolukbasi Martin Wattenberg F. Viégas Jimbo Wilson VLM 79 491 0 09 Jul 2019
Interpretable Counterfactual Explanations Guided by Prototypes A. V. Looveren Janis Klaise FAtt 61 384 0 03 Jul 2019
Model Agnostic Contrastive Explanations for Structured Data Amit Dhurandhar Tejaswini Pedapati Avinash Balakrishnan Pin-Yu Chen Karthikeyan Shanmugam Ruchi Puri FAtt 74 83 0 31 May 2019
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 88 321 0 27 May 2019
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations R. Mothilal Amit Sharma Chenhao Tan CML 106 1,015 0 19 May 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 82 1,009 0 26 Feb 2019
Explaining Models: An Empirical Study of How Explanations Impact Fairness Judgment Jonathan Dodge Q. V. Liao Yunfeng Zhang Rachel K. E. Bellamy Casey Dugan FaML 49 126 0 23 Jan 2019
This Looks Like That: Deep Learning for Interpretable Image Recognition Chaofan Chen Oscar Li Chaofan Tao A. Barnett Jonathan Su Cynthia Rudin 199 1,177 0 27 Jun 2018
The Challenge of Crafting Intelligible Intelligence Daniel S. Weld Gagan Bansal 45 244 0 09 Mar 2018
On Cognitive Preferences and the Plausibility of Rule-based Models Johannes Furnkranz Tomáš Kliegr Heiko Paulheim LRM 55 70 0 04 Mar 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 105 589 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 120 3,938 0 06 Feb 2018
Inverse Classification for Comparison-based Interpretability in Machine Learning Thibault Laugel Marie-Jeanne Lesot Christophe Marsala X. Renard Marcin Detyniecki 112 101 0 22 Dec 2017
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 187 1,834 0 30 Nov 2017
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 98 2,348 0 01 Nov 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 236 4,249 0 22 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 811 21,760 0 22 May 2017
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison Randal S. Olson William La Cava Patryk Orzechowski Ryan J. Urbanowicz J. Moore 305 378 0 01 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 371 3,776 0 28 Feb 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 863 16,891 0 16 Feb 2016