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Debugging a machine learning model is hard since the bug usually involves the training data and the learning process.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Newsweeder: Learning to filter netnews
Ken Lang. 1995 · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Visualization of an imperfect world
Nahum Gershon. 1998 · 1998
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Answering why and why not questions in user interfaces
Brad A Myers, David A Weitzman, Andrew J Ko, and Duen H Chau. 2006 · 2006
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Trust building with explanation interfaces
Pearl Pu and Li Chen. 2006 · 2006
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Subplex: Towards a better understanding of black box model explanations at the subpopulation level
Gromit Yeuk-Yin Chan, Jun Yuan, Kyle Overton, Brian Barr, Kim Rees, Luis Gustavo Nonato, Enrico Bertini, and Claudio T Silva. 2020 · 2007
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Future trends in authorship attribution
Patrick Juola. 2007 · 2007
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The effects of transparency on trust in and acceptance of a content-based art recommender
Henriette Cramer, Vanessa Evers, Satyan Ramlal, Maarten Van Someren, Lloyd Rutledge, Natalia Stash, Lora Aroyo, and Bob Wielinga. 2008 · 2008
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A metainstrument for interactive, on-the-fly machine learning
Rebecca Fiebrink, Dan Trueman, and Perry R. Cook. 2009 · 2009
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Fixing the program my computer learned: Barriers for end users, challenges for the machine
Todd Kulesza, Weng-Keen Wong, Simone Stumpf, Stephen Perona, Rachel White, Margaret M Burnett, Ian Oberst, and Andrew J Ko. 2009 · 2009
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Why and why not explanations improve the intelligibility of context-aware intelligent systems
Brian Y Lim, Anind K Dey, and Daniel Avrahami. 2009 · 2009
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Machine guides, human supervises: Interactive learning with global explanations
Teodora Popordanoska, Mohit Kumar, and Stefano Teso. 2020 · 2009
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Interacting meaningfully with machine learning systems: Three experiments
Simone Stumpf, Vidya Rajaram, Lida Li, Weng-Keen Wong, Margaret Burnett, Thomas Dietterich, Erin Sullivan, and Jonathan Herlocker. 2009 · 2009
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Designing interactions for robot active learners
Maya Cakmak, Crystal Chao, and Andrea L Thomaz. 2010 · 2010
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Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai
Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg. 2020 · 2010
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Explanatory debugging: Supporting end-user debugging of machine-learned programs
Todd Kulesza, Simone Stumpf, Margaret Burnett, Weng-Keen Wong, Yann Riche, Travis Moore, Ian Oberst, Amber Shinsel, and Kevin McIntosh. 2010 · 2010
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Simultaneous learning and covering with adversarial noise
Andrew Guillory and Jeff Bilmes. 2011 · 2011
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Model-agnostic explanations using minimal forcing subsets
Xing Han and Joydeep Ghosh. 2020 · 2011
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Human-debugging of machines
Devi Parikh and C Zitnick. 2011 · 2011
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Fastif: Scalable influence functions for efficient model interpretation and debugging
Han Guo, Nazneen Fatema Rajani, Peter Hase, Mohit Bansal, and Caiming Xiong. 2020 · 2012
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Power to the people: The role of humans in interactive machine learning
Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza. 2014 · 2014
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Crowdsourcing in hci research
Serge Egelman, Ed H Chi, and Steven Dow. 2014 · 2014
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Convolutional neural networks for sentence classification
Yoon Kim. 2014 · 2014
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad. 2015 · 2015
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Principles of explanatory debugging to personalize interactive machine learning
Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015 · 2015
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Explaining predictions of non-linear classifiers in NLP
Leila Arras, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller, and Wojciech Samek. 2016 · 2016
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Debugging machine learning models
Gabriel Cadamuro, Ran Gilad-Bachrach, and Xiaojin Zhu. 2016 · 2016
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Interacting with predictions: Visual inspection of black-box machine learning models
Josua Krause, Adam Perer, and Kenney Ng. 2016 · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Explanations considered harmful? user interactions with machine learning systems
Simone Stumpf, Adrian Bussone, and Dympna O’sullivan. 2016 · 2016
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Visual7w: Grounded question answering in images
Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. 2016 · 2016
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Using argumentation to improve classification in natural language problems
Lucas Carstens and Francesca Toni. 2017 · 2017
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Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov. 2017 · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
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Palm: Machine learning explanations for iterative debugging
Sanjay Krishnan and Eugene Wu. 2017 · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee. 2017 · 2017
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Developing bug-free machine learning systems with formal mathematics
Daniel Selsam, Percy Liang, and David L Dill. 2017 · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg. 2017 · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan. 2017 · 2017
Cited alongside, same era.
Trends and trajectories for explainable, accountable and intelligible systems: An hci research agenda
A survey of the state of explainable AI for natural language processing
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen. 2020 · 2020
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Explaining black box predictions and unveiling data artifacts through influence functions
Xiaochuang Han, Byron C. Wallace, and Yulia Tsvetkov. 2020 · 2020
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Soliciting human-in-the-loop user feedback for interactive machine learning reduces user trust and impressions of model accuracy
Donald Honeycutt, Mahsan Nourani, and Eric Ragan. 2020 · 2020
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exBERT: A Visual Analysis Tool to Explore Learned Representations in Transformer Models
Benjamin Hoover, Hendrik Strobelt, and Sebastian Gehrmann. 2020 · 2020
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Towards hierarchical importance attribution: Explaining compositional semantics for neural sequence models
Xisen Jin, Zhongyu Wei, Junyi Du, Xiangyang Xue, and Xiang Ren. 2020 · 2020
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Ashraf Abdul, Jo Vermeulen, Danding Wang, Brian Y Lim, and Mohan Kankanhalli. 2018 · 2018
Cited alongside, same era.
Peeking inside the black-box: a survey on explainable artificial intelligence (xai)
Amina Adadi and Mohammed Berrada. 2018 · 2018
Cited alongside, same era.
e-snli: natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
Cited alongside, same era.
Adversarial tableqa: Attention supervision for question answering on tables
Minseok Cho, Reinald Kim Amplayo, Seung-won Hwang, and Jonghyuck Park. 2018 · 2018
Cited alongside, same era.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
Cited alongside, same era.
Model assertions for debugging machine learning
Daniel Kang, Deepti Raghavan, Peter Bailis, and Matei Zaharia. 2018 · 2018
Cited alongside, same era.
The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
Zachary C Lipton. 2018 · 2018
Cited alongside, same era.
Njm-vis: Interpreting neural joint models in nlp
David Johnson, Giuseppe Carenini, and Gabriel Murray. 2020 · 2020
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Interpreting interpretability: Understanding data scientists’ use of interpretability tools for machine learning
Harmanpreet Kaur, Harsha Nori, Samuel Jenkins, Rich Caruana, Hanna Wallach, and Jennifer Wortman Vaughan. 2020 · 2020
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" why is’ chicago’deceptive?" towards building model-driven tutorials for humans
Vivian Lai, Han Liu, and Chenhao Tan. 2020 · 2020
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Explaining machine learning predictions: State-of-the-art, challenges, and opportunities
Himabindu Lakkaraju, Julius Adebayo, and Sameer Singh. 2020 · 2020
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FIND: Human-in-the-Loop Debugging Deep Text Classifiers
Piyawat Lertvittayakumjorn, Lucia Specia, and Francesca Toni. 2020 · 2020
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Bugdoc: A system for debugging computational pipelines
Raoni Lourenço, Juliana Freire, and Dennis Shasha. 2020 · 2020
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Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020 · 2020
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Beyond accuracy: Behavioral testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
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Interpretations are useful: penalizing explanations to align neural networks with prior knowledge
Laura Rieger, Chandan Singh, William Murdoch, and Bin Yu. 2020 · 2020
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Scram: Simple checks for realtime analysis of model training for non-expert ml programmers
Eldon Schoop, Forrest Huang, and Björn Hartmann. 2020 · 2020
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Making deep neural networks right for the right scientific reasons by interacting with their explanations
Patrick Schramowski, Wolfgang Stammer, Stefano Teso, Anna Brugger, Franziska Herbert, Xiaoting Shao, Hans-Georg Luigs, Anne-Katrin Mahlein, and Kristian Kersting. 2020 · 2020
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Towards understanding and arguing with classifiers: Recent progress
Xiaoting Shao, Tjitze Rienstra, Matthias Thimm, and Kristian Kersting. 2020 · 2020
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No explainability without accountability: An empirical study of explanations and feedback in interactive ml
Alison Smith-Renner, Ron Fan, Melissa Birchfield, Tongshuang Wu, Jordan Boyd-Graber, Daniel S Weld, and Leah Findlater. 2020 · 2020
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The language interpretability tool: Extensible, interactive visualizations and analysis for NLP models
Ian Tenney, James Wexler, Jasmijn Bastings, Tolga Bolukbasi, Andy Coenen, Sebastian Gehrmann, Ellen Jiang, Mahima Pushkarna, Carey Radebaugh, Emily Reif, and Ann Yuan. 2020 · 2020
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The relationship between trust in ai and trustworthy machine learning technologies
Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, and Aad van Moorsel. 2020 · 2020
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Datasets
Thomas Wolf, Quentin Lhoest, Patrick von Platen, Yacine Jernite, Mariama Drame, Julien Plu, Julien Chaumond, Clement Delangue, Clara Ma, Abhishek Thakur, Suraj Patil, Joe Davison, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angie McMillan-Major, Simon Brandeis, Sylvain Gugger, François Lagunas, Lysandre Debut, Morgan Funtowicz, Anthony Moi, Sasha Rush, Philipp Schmidd, Pierric Cistac, Victor Muštar, Jeff Boudier, and Anna Tordjmann. 2020 · 2020
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Effect of confidence and explanation on accuracy and trust calibration in ai-assisted decision making
Yunfeng Zhang, Q Vera Liao, and Rachel KE Bellamy. 2020b · 2020
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Yanzhe Bekkemoen and Helge Langseth. 2021 · 2021
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Argflow: A toolkit for deep argumentative explanations for neural networks
Adam Dejl, Peter He, Pranav Mangal, Hasan Mohsin, Bogdan Surdu, Eduard Voinea, Emanuele Albini, Piyawat Lertvittayakumjorn, Antonio Rago, and Francesca Toni. 2021 · 2021
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Explainable active learning (xal) toward ai explanations as interfaces for machine teachers
Bhavya Ghai, Q Vera Liao, Yunfeng Zhang, Rachel Bellamy, and Klaus Mueller. 2021 · 2021
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Towards benchmarking the utility of explanations for model debugging
Maximilian Idahl, Lijun Lyu, Ujwal Gadiraju, and Avishek Anand. 2021 · 2021
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Knowledge integration into deep learning in dynamical systems: an overview and taxonomy
Sung Wook Kim, Iljeok Kim, Jonghwan Lee, and Seungchul Lee. 2021 · 2021
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Supporting complaints investigation for nursing and midwifery regulatory agencies
Piyawat Lertvittayakumjorn, Ivan Petej, Yang Gao, Yamuna Krishnamurthy, Anna Van Der Gaag, Robert Jago, and Kostas Stathis. 2021 · 2021
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A primer in bertology: What we know about how bert works
Anna Rogers, Olga Kovaleva, and Anna Rumshisky. 2021 · 2021
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Informed machine learning-a taxonomy and survey of integrating prior knowledge into learning systems
Laura von Rueden, Sebastian Mayer, Katharina Beckh, Bogdan Georgiev, Sven Giesselbach, Raoul Heese, Birgit Kirsch, Michal Walczak, Julius Pfrommer, Annika Pick, et al. 2021 · 2021
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Putting humans in the natural language processing loop: A survey
Zijie J Wang, Dongjin Choi, Shenyu Xu, and Diyi Yang. 2021 · 2021
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Refining language models with compositional explanations
Huihan Yao, Ying Chen, Qinyuan Ye, Xisen Jin, and Xiang Ren. 2021 · 2021
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HILDIF: Interactive debugging of NLI models using influence functions
Hugo Zylberajch, Piyawat Lertvittayakumjorn, and Francesca Toni. 2021 · 2021
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2031
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