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This document serves as a brief overview of the "Safe and Reliable Machine Learning" tutorial given at the 2019 ACM Conference on Fairness, Accountability, and Transparency (FAT* 2019).
Dataset shift in machine learning
J Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D Lawrence. 2009 · 2009
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Beat the machine: Challenging workers to find the unknown unknowns. In Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence
Josh M Attenberg, Pagagiotis G Ipeirotis, and Foster Provost. 2011 · 2011
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Transportability of causal and statistical relations: a formal approach. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence . AAAI Press, 247–254
Judea Pearl and Elias Bareinboim. 2011 · 2011
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
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Reliability Engineering
Kailash C Kapur and Michael Pecht. 2014 · 2014
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Machine learning: The high interest credit card of technical debt
D Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, and Michael Young. 2014 · 2014
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Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. 2016 · 2016
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To predict and serve?
Kristian Lum and William Isaac. 2016 · 2016
Cited alongside, same era.
Reliable decision support using counterfactual models. In Advances in Neural Information Processing Systems . 1697–1708
Peter Schulam and Suchi Saria. 2017 · 2017
Cited alongside, same era.
Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency . 77–91
Joy Buolamwini and Timnit Gebru. 2018 · 2018
Cited alongside, same era.
A dual approach to scalable verification of deep networks
Krishnamurthy Dvijotham, Robert Stanforth, Sven Gowal, Timothy Mann, and Pushmeet Kohli. 2018 · 2018
Cited alongside, same era.
Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumeé III, and Kate Crawford. 2018 · 2018
Certifying some distributional robustness with principled adversarial training. In International Conference on Learning Representations
Aman Sinha, Hongseok Namkoong, and John Duchi. 2018 · 2018
Later among the works it cites.
Counterfactual Normalization: Proactively Addressing Dataset Shift Using Causal Mechanisms. In Uncertainty in Artificial Intelligence
Adarsh Subbaswamy and Suchi Saria. 2018 · 2018
Later among the works it cites.
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study
John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, and Eric Karl Oermann. 2018 · 2018
Later among the works it cites.
Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency . ACM, 220–229
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019 · 2019
Closest in time.
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Cited alongside, same era.
To trust or not to trust a classifier. In Advances in Neural Information Processing Systems . 5546–5557
Heinrich Jiang, Been Kim, Melody Guan, and Maya Gupta. 2018 · 2018
Cited alongside, same era.
Peter Schulam and Suchi Saria. 2019 · 2019
Closest in time.
Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport. In Artificial Intelligence and Statistics (AISTATS)
Adarsh Subbaswamy, Peter Schulam, and Suchi Saria. 2019 · 2019
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