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"

33 / 83 papers shown

Title
Shapley Explanation Networks Rui Wang Xiaoqian Wang David I. Inouye TDI FAtt 9 44 0 06 Apr 2021
Efficient Explanations from Empirical Explainers Robert Schwarzenberg Nils Feldhus Sebastian Möller FAtt 15 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 17 280 0 08 Mar 2021
EnD: Entangling and Disentangling deep representations for bias correction Enzo Tartaglione C. Barbano Marco Grangetto 11 124 0 02 Mar 2021
Contrastive Explanations for Model Interpretability Alon Jacovi Swabha Swayamdipta Shauli Ravfogel Yanai Elazar Yejin Choi Yoav Goldberg 19 94 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 15 4 0 18 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 24 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 6 8 0 15 Dec 2020
Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations Wolfgang Stammer P. Schramowski Kristian Kersting FAtt 14 106 0 25 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 19 48 0 19 Oct 2020
Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactuals Saloni Dash V. Balasubramanian Amit Sharma CML 9 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 20 42 0 26 Jun 2020
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport Kyle Swanson L. Yu Tao Lei OT 18 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 18 223 0 24 May 2020
Clinical Predictive Models for COVID-19: Systematic Study Patrick Schwab August DuMont Schütte Benedikt Dietz Stefan Bauer OOD ELM 28 35 0 17 May 2020
On Interpretability of Artificial Neural Networks: A Survey Fenglei Fan Jinjun Xiong Mengzhou Li Ge Wang AAML AI4CE 21 300 0 08 Jan 2020
Towards Explainable Artificial Intelligence Wojciech Samek K. Müller XAI 14 433 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 6 80 0 25 Jun 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 17 120 0 19 Jun 2019
Learning Representations by Humans, for Humans Sophie Hilgard Nir Rosenfeld M. Banaji Jack Cao David C. Parkes OCL HAI AI4CE 19 29 0 29 May 2019
Self-Critical Reasoning for Robust Visual Question Answering Jialin Wu Raymond J. Mooney OOD NAI 19 158 0 24 May 2019
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model Tong Wang Qihang Lin 23 19 0 10 May 2019
Saliency Learning: Teaching the Model Where to Pay Attention Reza Ghaeini Xiaoli Z. Fern Hamed Shahbazi Prasad Tadepalli FAtt XAI 11 30 0 22 Feb 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin-Xia Yu XAI HAI 14 1,414 0 14 Jan 2019
Multimodal Explanations by Predicting Counterfactuality in Videos Atsushi Kanehira Kentaro Takemoto S. Inayoshi Tatsuya Harada 18 35 0 04 Dec 2018
Interpretable Neuron Structuring with Graph Spectral Regularization Alexander Tong David van Dijk Jay S. Stanley Matthew Amodio Kristina M. Yim R. Muhle J. Noonan Guy Wolf Smita Krishnaswamy 19 6 0 30 Sep 2018
Learning Qualitatively Diverse and Interpretable Rules for Classification A. Ross Weiwei Pan Finale Doshi-Velez 6 13 0 22 Jun 2018
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems Richard J. Tomsett Dave Braines Daniel Harborne Alun D. Preece Supriyo Chakraborty FaML 11 164 0 20 Jun 2018
Human-in-the-Loop Interpretability Prior Isaac Lage A. Ross Been Kim S. Gershman Finale Doshi-Velez 14 120 0 29 May 2018
Seq2Seq-Vis: A Visual Debugging Tool for Sequence-to-Sequence Models Hendrik Strobelt Sebastian Gehrmann M. Behrisch Adam Perer Hanspeter Pfister Alexander M. Rush VLM HAI 16 239 0 25 Apr 2018
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation Menaka Narayanan Emily Chen Jeffrey He Been Kim S. Gershman Finale Doshi-Velez FAtt XAI 16 241 0 02 Feb 2018
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 19 675 0 26 Nov 2017
Human Understandable Explanation Extraction for Black-box Classification Models Based on Matrix Factorization Jaedeok Kim Ji-Hoon Seo FAtt 8 8 0 18 Sep 2017