Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations

10 March 2017

Finale Doshi-Velez

Papers citing "Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations"

50 / 115 papers shown

Title
Modeling Techniques for Machine Learning Fairness: A Survey Mingyang Wan Daochen Zha Ninghao Liu Na Zou SyDa FaML 30 36 0 04 Nov 2021
MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks Nicholas Hoernle Rafael-Michael Karampatsis Vaishak Belle Y. Gal 19 58 0 02 Nov 2021
SIM-ECG: A Signal Importance Mask-driven ECGClassification System K. Dharma Chicheng Zhang C. Gniady P. Agarwal Sushil Sharma 26 0 0 28 Oct 2021
Interpreting Deep Learning Models in Natural Language Processing: A Review Xiaofei Sun Diyi Yang Xiaoya Li Tianwei Zhang Yuxian Meng Han Qiu Guoyin Wang Eduard H. Hovy Jiwei Li 17 44 0 20 Oct 2021
Consistent Explanations by Contrastive Learning Vipin Pillai Soroush Abbasi Koohpayegani Ashley Ouligian Dennis Fong Hamed Pirsiavash FAtt 20 21 0 01 Oct 2021
Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models Yunhan Zheng Shenhao Wang Jinhuan Zhao HAI 24 27 0 25 Sep 2021
Toward a Unified Framework for Debugging Concept-based Models A. Bontempelli Fausto Giunchiglia Andrea Passerini Stefano Teso 20 4 0 23 Sep 2021
Enjoy the Salience: Towards Better Transformer-based Faithful Explanations with Word Salience G. Chrysostomou Nikolaos Aletras 32 16 0 31 Aug 2021
Improving the trustworthiness of image classification models by utilizing bounding-box annotations K. Dharma Chicheng Zhang 24 5 0 15 Aug 2021
EDITS: Modeling and Mitigating Data Bias for Graph Neural Networks Yushun Dong Ninghao Liu B. Jalaeian Jundong Li 23 117 0 11 Aug 2021
Leveraging Explainability for Comprehending Referring Expressions in the Real World Fethiye Irmak Dogan G. I. Melsión Iolanda Leite 37 8 0 12 Jul 2021
Towards Robust Classification Model by Counterfactual and Invariant Data Generation C. Chang George Adam Anna Goldenberg OOD CML 19 31 0 02 Jun 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 26 138 0 17 May 2021
Evading the Simplicity Bias: Training a Diverse Set of Models Discovers Solutions with Superior OOD Generalization Damien Teney Ehsan Abbasnejad Simon Lucey A. Hengel 23 86 0 12 May 2021
Learning by Design: Structuring and Documenting the Human Choices in Machine Learning Development Simon Enni Ira Assent 14 3 0 03 May 2021
Shapley Explanation Networks Rui Wang Xiaoqian Wang David I. Inouye TDI FAtt 19 44 0 06 Apr 2021
Efficient Explanations from Empirical Explainers Robert Schwarzenberg Nils Feldhus Sebastian Möller FAtt 32 9 0 29 Mar 2021
Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do P. Schramowski Cigdem Turan Nico Andersen Constantin Rothkopf Kristian Kersting 25 281 0 08 Mar 2021
EnD: Entangling and Disentangling deep representations for bias correction Enzo Tartaglione C. Barbano Marco Grangetto 26 124 0 02 Mar 2021
Contrastive Explanations for Model Interpretability Alon Jacovi Swabha Swayamdipta Shauli Ravfogel Yanai Elazar Yejin Choi Yoav Goldberg 38 95 0 02 Mar 2021
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays Joseph Paul Cohen Rupert Brooks Sovann En Evan Zucker Anuj Pareek M. Lungren Akshay S. Chaudhari FAtt MedIm 29 3 0 18 Feb 2021
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data Peter Hase Mohit Bansal XAI 14 87 0 03 Feb 2021
Answer Questions with Right Image Regions: A Visual Attention Regularization Approach Y. Liu Yangyang Guo Jianhua Yin Xuemeng Song Weifeng Liu Liqiang Nie 29 28 0 03 Feb 2021
Rule Extraction from Binary Neural Networks with Convolutional Rules for Model Validation Sophie Burkhardt Jannis Brugger Nicolas Wagner Zahra Ahmadi Kristian Kersting Stefan Kramer NAI FAtt 25 8 0 15 Dec 2020
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 21 18 0 10 Dec 2020
Neural Prototype Trees for Interpretable Fine-grained Image Recognition Meike Nauta Ron van Bree C. Seifert 71 262 0 03 Dec 2020
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations Wolfgang Stammer P. Schramowski Kristian Kersting FAtt 14 107 0 25 Nov 2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander DÁmour Katherine A. Heller D. Moldovan Ben Adlam B. Alipanahi ... Kellie Webster Steve Yadlowsky T. Yun Xiaohua Zhai D. Sculley OffRL 48 669 0 06 Nov 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 29 48 0 19 Oct 2020
Remembering for the Right Reasons: Explanations Reduce Catastrophic Forgetting Sayna Ebrahimi Suzanne Petryk Akash Gokul William Gan Joseph E. Gonzalez Marcus Rohrbach Trevor Darrell CLL 29 45 0 04 Oct 2020
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers Hanjie Chen Yangfeng Ji AAML VLM 13 62 0 01 Oct 2020
A Survey on Explainability in Machine Reading Comprehension Mokanarangan Thayaparan Marco Valentino André Freitas FaML 12 50 0 01 Oct 2020
Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals Saloni Dash V. Balasubramanian Amit Sharma CML 27 64 0 17 Sep 2020
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation L. Guan Mudit Verma Sihang Guo Ruohan Zhang Subbarao Kambhampati 43 42 0 26 Jun 2020
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport Kyle Swanson L. Yu Tao Lei OT 29 37 0 27 May 2020
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning Joseph Paul Cohen Lan Dao Paul Morrison Karsten Roth Yoshua Bengio ... A. Abbasi M. Hoshmand-Kochi Marzyeh Ghassemi Haifang Li T. Duong 27 222 0 24 May 2020
Clinical Predictive Models for COVID-19: Systematic Study Patrick Schwab August DuMont Schütte Benedikt Dietz Stefan Bauer OOD ELM 42 35 0 17 May 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 44 82 0 17 Mar 2020
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI L. Arras Ahmed Osman Wojciech Samek XAI AAML 21 150 0 16 Mar 2020
Deceptive AI Explanations: Creation and Detection Johannes Schneider Christian Meske Michalis Vlachos 14 28 0 21 Jan 2020
Making deep neural networks right for the right scientific reasons by interacting with their explanations P. Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Xiaoting Shao Hans-Georg Luigs Anne-Katrin Mahlein Kristian Kersting 26 207 0 15 Jan 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 38 300 0 08 Jan 2020
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot S. Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 37 6,110 0 22 Oct 2019
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 27 436 0 26 Sep 2019
Improving performance of deep learning models with axiomatic attribution priors and expected gradients G. Erion Joseph D. Janizek Pascal Sturmfels Scott M. Lundberg Su-In Lee OOD BDL FAtt 13 80 0 25 Jun 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 28 120 0 19 Jun 2019
Learning Representations by Humans, for Humans Sophie Hilgard Nir Rosenfeld M. Banaji Jack Cao David C. Parkes OCL HAI AI4CE 28 29 0 29 May 2019
Self-Critical Reasoning for Robust Visual Question Answering Jialin Wu Raymond J. Mooney OOD NAI 24 159 0 24 May 2019
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model Tong Wang Qihang Lin 28 19 0 10 May 2019
Attention is not Explanation Sarthak Jain Byron C. Wallace FAtt 29 1,298 0 26 Feb 2019