Towards A Rigorous Science of Interpretable Machine Learning

28 February 2017

Finale Doshi-Velez

Papers citing "Towards A Rigorous Science of Interpretable Machine Learning"

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Title
Are Metrics Enough? Guidelines for Communicating and Visualizing Predictive Models to Subject Matter Experts Ashley Suh G. Appleby Erik W. Anderson Luca A. Finelli Remco Chang Dylan Cashman 27 8 0 11 May 2022
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine Learning Ulrike Kuhl André Artelt Barbara Hammer 27 17 0 06 May 2022
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Aparna Balagopalan Haoran Zhang Kimia Hamidieh Thomas Hartvigsen Frank Rudzicz Marzyeh Ghassemi 35 77 0 06 May 2022
Designing for Responsible Trust in AI Systems: A Communication Perspective Q. V. Liao S. Sundar 17 99 0 29 Apr 2022
Do Users Benefit From Interpretable Vision? A User Study, Baseline, And Dataset Leon Sixt M. Schuessler Oana-Iuliana Popescu Philipp Weiß Tim Landgraf FAtt 24 14 0 25 Apr 2022
Towards Involving End-users in Interactive Human-in-the-loop AI Fairness Yuri Nakao Simone Stumpf Subeida Ahmed A. Naseer Lorenzo Strappelli 13 34 0 22 Apr 2022
Features of Explainability: How users understand counterfactual and causal explanations for categorical and continuous features in XAI Greta Warren Mark T. Keane R. Byrne CML 25 22 0 21 Apr 2022
Perception Visualization: Seeing Through the Eyes of a DNN Loris Giulivi Mark J. Carman Giacomo Boracchi 13 6 0 21 Apr 2022
Calibrating Trust of Multi-Hop Question Answering Systems with Decompositional Probes Kaige Xie Sarah Wiegreffe Mark O. Riedl ReLM 16 12 0 16 Apr 2022
ProtoTEx: Explaining Model Decisions with Prototype Tensors Anubrata Das Chitrank Gupta Venelin Kovatchev Matthew Lease J. Li 24 26 0 11 Apr 2022
Measuring AI Systems Beyond Accuracy Violet Turri R. Dzombak Eric T. Heim Nathan M. VanHoudnos Jay Palat Anusha Sinha 22 0 0 07 Apr 2022
Interpretation of Black Box NLP Models: A Survey Shivani Choudhary N. Chatterjee S. K. Saha FAtt 32 10 0 31 Mar 2022
Towards Explainable Evaluation Metrics for Natural Language Generation Christoph Leiter Piyawat Lertvittayakumjorn M. Fomicheva Wei-Ye Zhao Yang Gao Steffen Eger AAML ELM 22 20 0 21 Mar 2022
Interpretability for Language Learners Using Example-Based Grammatical Error Correction Masahiro Kaneko Sho Takase Ayana Niwa Naoaki Okazaki 33 26 0 14 Mar 2022
Symbolic Learning to Optimize: Towards Interpretability and Scalability Wenqing Zheng Tianlong Chen Ting-Kuei Hu Zhangyang Wang 37 18 0 13 Mar 2022
Robustness and Usefulness in AI Explanation Methods Erick Galinkin FAtt 20 1 0 07 Mar 2022
Interpretable part-whole hierarchies and conceptual-semantic relationships in neural networks Nicola Garau N. Bisagno Zeno Sambugaro Nicola Conci 16 21 0 07 Mar 2022
Interpretable Off-Policy Learning via Hyperbox Search D. Tschernutter Tobias Hatt Stefan Feuerriegel OffRL CML 42 5 0 04 Mar 2022
Sparse Bayesian Optimization Sulin Liu Qing Feng David Eriksson Benjamin Letham E. Bakshy 25 7 0 03 Mar 2022
Reinforcement Learning in Practice: Opportunities and Challenges Yuxi Li OffRL 34 9 0 23 Feb 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 16 22 0 22 Feb 2022
Interpreting Language Models with Contrastive Explanations Kayo Yin Graham Neubig MILM 15 77 0 21 Feb 2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis Thomas Fel Mélanie Ducoffe David Vigouroux Rémi Cadène Mikael Capelle C. Nicodeme Thomas Serre AAML 20 41 0 15 Feb 2022
Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient Max W. Shen 25 18 0 10 Feb 2022
Evaluation Methods and Measures for Causal Learning Algorithms Lu Cheng Ruocheng Guo Raha Moraffah Paras Sheth K. S. Candan Huan Liu CML ELM 21 50 0 07 Feb 2022
Learning Interpretable, High-Performing Policies for Autonomous Driving Rohan R. Paleja Yaru Niu Andrew Silva Chace Ritchie Sugju Choi Matthew C. Gombolay 16 16 0 04 Feb 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 174 185 0 03 Feb 2022
Hierarchical Shrinkage: improving the accuracy and interpretability of tree-based methods Abhineet Agarwal Yan Shuo Tan Omer Ronen Chandan Singh Bin-Xia Yu 65 27 0 02 Feb 2022
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 17 1 0 30 Jan 2022
Black-box Error Diagnosis in Deep Neural Networks for Computer Vision: a Survey of Tools Piero Fraternali Federico Milani Rocio Nahime Torres Niccolò Zangrando AAML 25 9 0 17 Jan 2022
An Accelerator for Rule Induction in Fuzzy Rough Theory Suyun Zhao Zhi-Gang Dai Xizhao Wang Peng Ni Hengheng Luo Hong Chen Cuiping Li 11 11 0 07 Jan 2022
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence Kacper Sokol Peter A. Flach 31 20 0 29 Dec 2021
AcME -- Accelerated Model-agnostic Explanations: Fast Whitening of the Machine-Learning Black Box David Dandolo Chiara Masiero Mattia Carletti Davide Dalle Pezze Gian Antonio Susto FAtt LRM 22 22 0 23 Dec 2021
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations Siddhant Arora Danish Pruthi Norman M. Sadeh William W. Cohen Zachary Chase Lipton Graham Neubig FAtt 30 38 0 17 Dec 2021
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction Aaron Chan Maziar Sanjabi Lambert Mathias L Tan Shaoliang Nie Xiaochang Peng Xiang Ren Hamed Firooz 36 41 0 16 Dec 2021
Interpretable Design of Reservoir Computing Networks using Realization Theory Wei Miao Vignesh Narayanan Jr-Shin Li 35 5 0 13 Dec 2021
Evaluating saliency methods on artificial data with different background types Céline Budding Fabian Eitel K. Ritter Stefan Haufe XAI FAtt MedIm 13 5 0 09 Dec 2021
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 0 06 Dec 2021
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View Di Jin Elena Sergeeva W. Weng Geeticka Chauhan Peter Szolovits OOD 31 55 0 05 Dec 2021
Learning Optimal Predictive Checklists Haoran Zhang Q. Morris Berk Ustun Marzyeh Ghassemi 18 11 0 02 Dec 2021
On Two XAI Cultures: A Case Study of Non-technical Explanations in Deployed AI System Helen Jiang Erwen Senge 25 7 0 02 Dec 2021
Explainable AI (XAI): A Systematic Meta-Survey of Current Challenges and Future Opportunities Waddah Saeed C. Omlin XAI 36 414 0 11 Nov 2021
Counterfactual Explanations for Models of Code Jürgen Cito Işıl Dillig V. Murali S. Chandra AAML LRM 24 47 0 10 Nov 2021
Self-Interpretable Model with TransformationEquivariant Interpretation Yipei Wang Xiaoqian Wang 24 23 0 09 Nov 2021
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods Zohaib Salahuddin Henry C. Woodruff A. Chatterjee Philippe Lambin 13 301 0 01 Nov 2021
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining Andreas Madsen Nicholas Meade Vaibhav Adlakha Siva Reddy 96 35 0 15 Oct 2021
Interpretable Neural Networks with Frank-Wolfe: Sparse Relevance Maps and Relevance Orderings Jan Macdonald Mathieu Besançon S. Pokutta 27 11 0 15 Oct 2021
Can Explanations Be Useful for Calibrating Black Box Models? Xi Ye Greg Durrett FAtt 19 25 0 14 Oct 2021
Clustering-Based Interpretation of Deep ReLU Network Nicola Picchiotti Marco Gori FAtt 15 0 0 13 Oct 2021
A Survey on Legal Question Answering Systems J. Martinez-Gil AILaw ELM 25 26 0 12 Oct 2021