Actionable Recourse in Linear Classification

18 September 2018

Yang Liu

Papers citing "Actionable Recourse in Linear Classification"

50 / 151 papers shown

Title
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods E. Amparore Alan Perotti P. Bajardi FAtt 33 68 0 01 Jun 2021
Leveraging Sparse Linear Layers for Debuggable Deep Networks Eric Wong Shibani Santurkar A. Madry FAtt 22 88 0 11 May 2021
Optimal Counterfactual Explanations for Scorecard modelling Guillermo Navas-Palencia 11 9 0 17 Apr 2021
Consequence-aware Sequential Counterfactual Generation Philip Naumann Eirini Ntoutsi OffRL 17 24 0 12 Apr 2021
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice David S. Watson Limor Gultchin Ankur Taly Luciano Floridi 22 63 0 27 Mar 2021
Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals Sainyam Galhotra Romila Pradhan Babak Salimi CML 19 105 0 22 Mar 2021
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties Lisa Schut Oscar Key R. McGrath Luca Costabello Bogdan Sacaleanu Medb Corcoran Y. Gal CML 26 47 0 16 Mar 2021
Interpretable Machine Learning: Moving From Mythos to Diagnostics Valerie Chen Jeffrey Li Joon Sik Kim Gregory Plumb Ameet Talwalkar 32 29 0 10 Mar 2021
Strategic Classification Made Practical Sagi Levanon Nir Rosenfeld 42 55 0 02 Mar 2021
Contrastive Explanations for Model Interpretability Alon Jacovi Swabha Swayamdipta Shauli Ravfogel Yanai Elazar Yejin Choi Yoav Goldberg 44 95 0 02 Mar 2021
Counterfactual Explanations for Oblique Decision Trees: Exact, Efficient Algorithms Miguel Á. Carreira-Perpiñán Suryabhan Singh Hada CML AAML 18 33 0 01 Mar 2021
Information Discrepancy in Strategic Learning Yahav Bechavod Chara Podimata Zhiwei Steven Wu Juba Ziani 41 45 0 01 Mar 2021
Towards Robust and Reliable Algorithmic Recourse Sohini Upadhyay Shalmali Joshi Himabindu Lakkaraju 25 108 0 26 Feb 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 27 146 0 26 Feb 2021
Towards a Unified Framework for Fair and Stable Graph Representation Learning Chirag Agarwal Himabindu Lakkaraju Marinka Zitnik 27 157 0 25 Feb 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 34 126 0 24 Jan 2021
GeCo: Quality Counterfactual Explanations in Real Time Maximilian Schleich Zixuan Geng Yihong Zhang D. Suciu 46 61 0 05 Jan 2021
ROBY: Evaluating the Robustness of a Deep Model by its Decision Boundaries Jinyin Chen Zhen Wang Haibin Zheng Jun Xiao Zhaoyan Ming AAML 24 5 0 18 Dec 2020
Declarative Approaches to Counterfactual Explanations for Classification Leopoldo Bertossi 39 17 0 15 Nov 2020
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End R. Mothilal Divyat Mahajan Chenhao Tan Amit Sharma FAtt CML 27 100 0 10 Nov 2020
Linear Classifiers that Encourage Constructive Adaptation Yatong Chen Jialu Wang Yang Liu 39 16 0 31 Oct 2020
Incorporating Interpretable Output Constraints in Bayesian Neural Networks Wanqian Yang Lars Lorch Moritz Graule Himabindu Lakkaraju Finale Doshi-Velez UQCV BDL 17 16 0 21 Oct 2020
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 26 164 0 20 Oct 2020
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges Christoph Molnar Giuseppe Casalicchio B. Bischl AI4TS AI4CE 25 396 0 19 Oct 2020
Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples Yael Mathov Eden Levy Ziv Katzir A. Shabtai Yuval Elovici AAML 33 14 0 07 Oct 2020
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses Kaivalya Rawal Himabindu Lakkaraju 27 11 0 15 Sep 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 46 62 0 11 Sep 2020
Neither Private Nor Fair: Impact of Data Imbalance on Utility and Fairness in Differential Privacy Tom Farrand Fatemehsadat Mireshghallah Sahib Singh Andrew Trask FedML 11 88 0 10 Sep 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 33 51 0 03 Sep 2020
Reliable Post hoc Explanations: Modeling Uncertainty in Explainability Dylan Slack Sophie Hilgard Sameer Singh Himabindu Lakkaraju FAtt 29 162 0 11 Aug 2020
On Counterfactual Explanations under Predictive Multiplicity Martin Pawelczyk Klaus Broelemann Gjergji Kasneci 25 85 0 23 Jun 2020
Getting a CLUE: A Method for Explaining Uncertainty Estimates Javier Antorán Umang Bhatt T. Adel Adrian Weller José Miguel Hernández-Lobato UQCV BDL 47 112 0 11 Jun 2020
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach Amir-Hossein Karimi Julius von Kügelgen Bernhard Schölkopf Isabel Valera CML 28 178 0 11 Jun 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 51 82 0 17 Mar 2020
ViCE: Visual Counterfactual Explanations for Machine Learning Models Oscar Gomez Steffen Holter Jun Yuan E. Bertini AAML 59 93 0 05 Mar 2020
The Problem with Metrics is a Fundamental Problem for AI Rachel L. Thomas D. Uminsky 19 67 0 20 Feb 2020
Algorithmic Recourse: from Counterfactual Explanations to Interventions Amir-Hossein Karimi Bernhard Schölkopf Isabel Valera CML 24 337 0 14 Feb 2020
Decisions, Counterfactual Explanations and Strategic Behavior Stratis Tsirtsis Manuel Gomez Rodriguez 27 58 0 11 Feb 2020
Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers Divyat Mahajan Chenhao Tan Amit Sharma OOD CML 28 206 0 06 Dec 2019
Learning Model-Agnostic Counterfactual Explanations for Tabular Data Martin Pawelczyk Johannes Haug Klaus Broelemann Gjergji Kasneci OOD CML 33 199 0 21 Oct 2019
FACE: Feasible and Actionable Counterfactual Explanations Rafael Poyiadzi Kacper Sokol Raúl Santos-Rodríguez T. D. Bie Peter A. Flach 11 365 0 20 Sep 2019
Predictive Multiplicity in Classification Charles Marx Flavio du Pin Calmon Berk Ustun 36 136 0 14 Sep 2019
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning Fan Yang Mengnan Du Xia Hu XAI ELM 32 67 0 16 Jul 2019
FlipTest: Fairness Testing via Optimal Transport Emily Black Samuel Yeom Matt Fredrikson 30 94 0 21 Jun 2019
Model-Agnostic Counterfactual Explanations for Consequential Decisions Amir-Hossein Karimi Gilles Barthe Borja Balle Isabel Valera 44 318 0 27 May 2019
Optimal Decision Making Under Strategic Behavior Stratis Tsirtsis Behzad Tabibian M. Khajehnejad Adish Singla Bernhard Schölkopf Manuel Gomez Rodriguez 18 31 0 22 May 2019
Interpreting Neural Networks Using Flip Points Roozbeh Yousefzadeh D. O’Leary AAML FAtt 22 17 0 21 Mar 2019
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions Hao Wang Berk Ustun Flavio du Pin Calmon FaML 28 83 0 29 Jan 2019
Efficient Search for Diverse Coherent Explanations Chris Russell 17 234 0 02 Jan 2019
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias Rachel K. E. Bellamy Kuntal Dey Michael Hind Samuel C. Hoffman Stephanie Houde ... Diptikalyan Saha P. Sattigeri Moninder Singh Kush R. Varshney Yunfeng Zhang FaML SyDa 62 796 0 03 Oct 2018