Generating Counterfactual Explanations with Natural Language

26 June 2018

Papers citing "Generating Counterfactual Explanations with Natural Language"

27 / 27 papers shown

Title
Classifier-to-Bias: Toward Unsupervised Automatic Bias Detection for Visual Classifiers Quentin Guimard Moreno DÍncà Massimiliano Mancini Elisa Ricci SSL 72 0 0 29 Apr 2025
Contrastive Explanations That Anticipate Human Misconceptions Can Improve Human Decision-Making Skills Zana Buçinca S. Swaroop Amanda E. Paluch Finale Doshi-Velez Krzysztof Z. Gajos 56 2 0 05 Oct 2024
PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies Audrey Der Chin-Chia Michael Yeh Yan Zheng Junpeng Wang Zhongfang Zhuang Liang Wang Wei Zhang Eamonn J. Keogh AI4TS 45 2 0 16 Jan 2024
Interpretable Reinforcement Learning for Robotics and Continuous Control Rohan R. Paleja Letian Chen Yaru Niu Andrew Silva Zhaoxin Li ... K. Chang H. E. Tseng Yan Wang S. Nageshrao Matthew C. Gombolay 37 7 0 16 Nov 2023
Counterfactual Explanations for Misclassified Images: How Human and Machine Explanations Differ Eoin Delaney A. Pakrashi Derek Greene Markt. Keane 35 16 0 16 Dec 2022
Explainability Via Causal Self-Talk Nicholas A. Roy Junkyung Kim Neil C. Rabinowitz CML 26 7 0 17 Nov 2022
Towards Human-Centred Explainability Benchmarks For Text Classification Viktor Schlegel Erick Mendez Guzman R. Batista-Navarro 28 5 0 10 Nov 2022
Fooling Explanations in Text Classifiers Adam Ivankay Ivan Girardi Chiara Marchiori P. Frossard AAML 35 20 0 07 Jun 2022
MACE: An Efficient Model-Agnostic Framework for Counterfactual Explanation Wenzhuo Yang Jia Li Caiming Xiong Guosheng Lin CML 35 13 0 31 May 2022
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations Leonard Salewski A. Sophia Koepke Hendrik P. A. Lensch Zeynep Akata LRM NAI 33 20 0 05 Apr 2022
Learning Interpretable, High-Performing Policies for Autonomous Driving Rohan R. Paleja Yaru Niu Andrew Silva Chace Ritchie Sugju Choi Matthew C. Gombolay 27 16 0 04 Feb 2022
Towards Relatable Explainable AI with the Perceptual Process Wencan Zhang Brian Y. Lim AAML XAI 25 62 0 28 Dec 2021
Exploring The Role of Local and Global Explanations in Recommender Systems Marissa Radensky Doug Downey Kyle Lo Z. Popović Daniel S. Weld University of Washington LRM 13 20 0 27 Sep 2021
Counterfactual Explainable Recommendation Juntao Tan Shuyuan Xu Yingqiang Ge Yunqi Li Xu Chen Yongfeng Zhang CML 30 141 0 24 Aug 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 29 140 0 17 May 2021
Local Interpretations for Explainable Natural Language Processing: A Survey Siwen Luo Hamish Ivison S. Han Josiah Poon MILM 43 48 0 20 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
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching Scott Cheng-Hsin Yang Wai Keen Vong Ravi B. Sojitra Tomas Folke Patrick Shafto 21 42 0 07 Feb 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 26 162 0 20 Oct 2020
Counterfactual explanation of machine learning survival models M. Kovalev Lev V. Utkin CML OffRL 27 19 0 26 Jun 2020
NILE : Natural Language Inference with Faithful Natural Language Explanations Sawan Kumar Partha P. Talukdar XAI LRM 16 160 0 25 May 2020
SCOUT: Self-aware Discriminant Counterfactual Explanations Pei Wang Nuno Vasconcelos FAtt 30 81 0 16 Apr 2020
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation Raha Moraffah Mansooreh Karami Ruocheng Guo A. Raglin Huan Liu CML ELM XAI 27 213 0 09 Mar 2020
CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis Yao Xie Melody Chen David Kao Ge Gao Xiang Ánthony' Chen 31 126 0 15 Jan 2020
Generating Counterfactual and Contrastive Explanations using SHAP Shubham Rathi 24 56 0 21 Jun 2019
Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations Rohan R. Paleja Andrew Silva Letian Chen Matthew C. Gombolay 22 31 0 14 Jun 2019
Learning Deep Representations of Fine-grained Visual Descriptions Scott E. Reed Zeynep Akata Bernt Schiele Honglak Lee OCL VLM 176 840 0 17 May 2016