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We present a randomized controlled trial for a model-in-the-loop regression task, with the goal of measuring the extent to which (1) good explanations of model predictions increase human accuracy, and (2) faulty explanations decrease human trust in the model.
Judgment under uncertainty: Heuristics and biases
Daniel Kahneman, Stewart Paul Slovic, Paul Slovic, and Amos Tversky · 1982
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Can computers be teammates?
Clifford Nass, BJ Fogg, and Youngme Moon · 1996
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The three paradigms of hci
Steve Harrison, Deborah Tatar, and Phoebe Sengers · 2007
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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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Making epistemological trouble: Third-paradigm hci as successor science
Steve Harrison, Phoebe Sengers, and Deborah Tatar · 2011
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Algorithm aversion: People erroneously avoid algorithms after seeing them err
Berkeley J Dietvorst, Joseph P Simmons, and Cade Massey · 2015
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Dex: Deep expectation of apparent age from a single image
Rasmus Rothe, Radu Timofte, and Luc Van Gool · 2015
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Generating visual explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell · 2016
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 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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Deep learning for identifying metastatic breast cancer
Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, and Andrew H Beck · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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It takes two to tango: Towards theory of ai’s mind
Arjun Chandrasekaran, Deshraj Yadav, Prithvijit Chattopadhyay, Viraj Prabhu, and Devi Parikh · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Apparent and real age estimation in still images with deep residual regressors on appa-real database
S Escalera X Baro I Guyon R Rothe. E Agustsson, R Timofte · 2017
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Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Counterfactual explanations without opening the black box: Automated decisions and the gdpr
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2017
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Sanity checks for saliency maps
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
Cited alongside, same era.
’it’s reducing a human being to a percentage’ perceptions of justice in algorithmic decisions
Reuben Binns, Max Van Kleek, Michael Veale, Ulrik Lyngs, Jun Zhao, and Nigel Shadbolt · 2018
Cited alongside, same era.
Attention is not explanation
Sarthak Jain and Byron C Wallace · 2019
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The (un) reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 2019
Later among the works it cites.
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
Later among the works it cites.
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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Communicating uncertainty about facts, numbers and science
Anne Marthe van der Bles, Sander van der Linden, Alexandra LJ Freeman, James Mitchell, Ana B Galvao, Lisa Zaval, and David J Spiegelhalter · 2019
Later among the works it cites.
Unremarkable ai: Fitting intelligent decision support into critical, clinical decision-making processes
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Motahhare Eslami, Sneha R Krishna Kumaran, Christian Sandvig, and Karrie Karahalios · 2018
Cited alongside, same era.
Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, and Finale Doshi-Velez · 2018
Cited alongside, same era.
Rise: Randomized input sampling for explanation of black-box models
Vitali Petsiuk, Abir Das, and Kate Saenko · 2018
Cited alongside, same era.
Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
P Jonathon Phillips, Amy N Yates, Ying Hu, Carina A Hahn, Eilidh Noyes, Kelsey Jackson, Jacqueline G Cavazos, Géraldine Jeckeln, Rajeev Ranjan, Swami Sankaranarayanan, et al · 2018
Cited alongside, same era.
Manipulating and measuring model interpretability
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Vaughan, and Hanna Wallach · 2018
Cited alongside, same era.
Investigating human+ machine complementarity for recidivism predictions
Sarah Tan, Julius Adebayo, Kori Inkpen, and Ece Kamar · 2018
Cited alongside, same era.
The effects of example-based explanations in a machine learning interface
Carrie J Cai, Jonas Jongejan, and Jess Holbrook · 2019
Cited alongside, same era.
Qian Yang, Aaron Steinfeld, and John Zimmerman · 2019
Later among the works it cites.
Making sense of recommendations
Michael Yeomans, Anuj Shah, Sendhil Mullainathan, and Jon Kleinberg · 2019
Later among the works it cites.
Understanding the effect of accuracy on trust in machine learning models
Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach · 2019
Later among the works it cites.
https://www.bbc.com/news/technology-49993647 , Last accessed on 2020-06-01
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