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Explanations are hypothesized to improve human understanding of machine learning models and achieve a variety of desirable outcomes, ranging from model debugging to enhancing human decision making.
Generalized linear models
John Ashworth Nelder and Robert WM Wedderburn · 1972
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Explanatory coherence
Paul Thagard · 1989
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Wizard of oz studies—why and how
Nils Dahlbäck, Arne Jönsson, and Lars Ahrenberg · 1993
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Uci repository of machine learning databases
Catherine Blake · 1998
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Numeracy and decision making
Ellen Peters, Daniel Västfjäll, Paul Slovic, CK Mertz, Ketti Mazzocco, and Stephan Dickert · 2006
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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
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Causality
Judea Pearl · 2009
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Causality with gates
John Winn · 2012
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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The role of explanations on trust and reliance in clinical decision support systems
Adrian Bussone, Simone Stumpf, and Dympna O’Sullivan · 2015
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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
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Interpretable decision sets: A joint framework for description and prediction
Himabindu Lakkaraju, Stephen H Bach, and Jure Leskovec · 2016
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The mythos of model interpretability
Zachary C Lipton · 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
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Human-centric justification of machine learning predictions
Or Biran and Kathleen R McKeown · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Do explanations make vqa models more predictable to a human?
Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, and Devi Parikh · 2018
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What can ai do for me: Evaluating machine learning interpretations in cooperative play
Shi Feng and Jordan Boyd-Graber · 2018
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Metrics for explainable ai: Challenges and prospects
Robert R Hoffman, Shane T Mueller, Gary Klein, and Jordan Litman · 2018
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Human decisions and machine predictions
Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, and Sendhil Mullainathan · 2018
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Comparing automatic and human evaluation of local explanations for text classification
Dong Nguyen · 2018
Human factors in model interpretability: Industry practices, challenges, and needs
Sungsoo Ray Hong, Jessica Hullman, and Enrico Bertini · 2020
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Impact of a deep learning assistant on the histopathologic classification of liver cancer
Amirhossein Kiani, Bora Uyumazturk, Pranav Rajpurkar, Alex Wang, Rebecca Gao, Erik Jones, Yifan Yu, Curtis P Langlotz, Robyn L Ball, Thomas J Montine, et al · 2020
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" why is’ chicago’deceptive?" towards building model-driven tutorials for humans
Vivian Lai, Han Liu, and Chenhao Tan · 2020
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Why does my model fail? contrastive local explanations for retail forecasting
Ana Lucic, Hinda Haned, and Maarten de Rijke · 2020
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Yunfeng Zhang, Q Vera Liao, and Rachel KE Bellamy · 2020
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Anchors: High-precision model-agnostic explanations
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2018
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Beyond accuracy: The role of mental models in human-ai team performance
Gagan Bansal, Besmira Nushi, Ece Kamar, Walter S Lasecki, Daniel S Weld, and Eric Horvitz · 2019
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Assessing the local interpretability of machine learning models
Sorelle A Friedler, Chitradeep Dutta Roy, Carlos Scheidegger, and Dylan Slack · 2019
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Visualizing uncertainty and alternatives in event sequence predictions
Shunan Guo, Fan Du, Sana Malik, Eunyee Koh, Sungchul Kim, Zhicheng Liu, Donghyun Kim, Hongyuan Zha, and Nan Cao · 2019
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Human evaluation of models built for interpretability
Isaac Lage, Emily Chen, Jeffrey He, Menaka Narayanan, Been Kim, Samuel J Gershman, and Finale Doshi-Velez · 2019
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On human predictions with explanations and predictions of machine learning models: A case study on deception detection
Vivian Lai and Chenhao Tan · 2019
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Explaining machine learning classifiers through diverse counterfactual explanations
Ramaravind Kommiya Mothilal, Amit Sharma, and Chenhao Tan · 2019
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From human explanation to model interpretability: A framework based on weight of evidence
David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna Wallach, and Jennifer Wortman Vaughan · 2021
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Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z Gajos · 2021
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I think i get your point, ai! the illusion of explanatory depth in explainable ai
Michael Chromik, Malin Eiband, Felicitas Buchner, Adrian Krüger, and Andreas Butz · 2021
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Towards a science of human-ai decision making: A survey of empirical studies
Vivian Lai, Chacha Chen, Q Vera Liao, Alison Smith-Renner, and Chenhao Tan · 2021
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Han Liu, Vivian Lai, and Chenhao Tan · 2021
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The effectiveness of feature attribution methods and its correlation with automatic evaluation scores
Giang Nguyen, Daeyoung Kim, and Anh Nguyen · 2021
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Anchoring bias affects mental model formation and user reliance in explainable ai systems
Mahsan Nourani, Chiradeep Roy, Jeremy E Block, Donald R Honeycutt, Tahrima Rahman, Eric Ragan, and Vibhav Gogate · 2021
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Manipulating and measuring model interpretability
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Wortman Vaughan, and Hanna Wallach · 2021
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Counterfactual invariance to spurious correlations in text classification
Victor Veitch, Alexander D’Amour, Steve Yadlowsky, and Jacob Eisenstein · 2021
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Are explanations helpful? a comparative study of the effects of explanations in ai-assisted decision-making
Xinru Wang and Ming Yin · 2021
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Post hoc explanations may be ineffective for detecting unknown spurious correlation
Julius Adebayo, Michael Muelly, Harold Abelson, and Been Kim · 2022
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What i cannot predict, i do not understand: A human-centered evaluation framework for explainability methods
Julien Colin, Thomas Fel, Rémi Cadène, and Thomas Serre · 2022
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Human-algorithm collaboration: Achieving complementarity and avoiding unfairness
Kate Donahue, Alexandra Chouldechova, and Krishnaram Kenthapadi · 2022
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Hive: evaluating the human interpretability of visual explanations
Sunnie SY Kim, Nicole Meister, Vikram V Ramaswamy, Ruth Fong, and Olga Russakovsky · 2022
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Visual correspondence-based explanations improve ai robustness and human-ai team accuracy
Mohammad Reza Taesiri, Giang Nguyen, and Anh Nguyen · 2022
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A psychological theory of explainability
Scott Cheng-Hsin Yang, Nils Erik Tomas Folke, and Patrick Shafto · 2022
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