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The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
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Testing and validating machine learning classifiers by metamorphic testing
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Cams: Cameras for allsky meteor surveillance to establish minor meteor showers
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Power to the people: The role of humans in interactive machine learning
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Managing innovation in architecturally hierarchical systems: Three switchback mechanisms that impact practice
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Recommendations on the use and design of risk matrices
N. Duijm · 2015
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Probabilistic machine learning and artificial intelligence
Zoubin Ghahramani · 2015
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D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-François Crespo, and Dan Dennison · 2015
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Taking the human out of the loop: A review of bayesian optimization
B. Shahriari, Kevin Swersky, Ziyu Wang, R. Adams, and N. D. Freitas · 2016
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The ml test score: A rubric for ml production readiness and technical debt reduction
Eric Breck, Shanqing Cai, E. Nielsen, M. Salib, and D. Sculley · 2017
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Reliable decision support using counterfactual models
P. Schulam and S. Saria · 2017
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Concrete problems for autonomous vehicle safety: Advantages of bayesian deep learning
Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, A. Shah, R. Cipolla, and Adrian Weller · 2017
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J. Tobin, Rachel H Fong, Alex Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
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Searching for long-period comets with deep learning tools
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Agile software development methods: Review and analysis
P. Abrahamsson, Outi Salo, Jussi Ronkainen, and Juhani Warsta · 2017
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Hybrid software and system development in practice: waterfall, scrum, and beyond
Marco Kuhrmann, Philipp Diebold, Jürgen Münch, Paolo Tell, Vahid Garousi, Michael Felderer, Kitija Trektere, Fergal McCaffery, Oliver Linssen, Eckhart Hanser, and Christian R. Prause · 2017
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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A generic framework for privacy preserving deep learning
T. Ryffel, Andrew Trask, M. Dahl, Bobby Wagner, J. Mancuso, D. Rueckert, and J. Passerat-Palmbach · 2018
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Towards deep learning models resistant to adversarial attacks
A. Madry, Aleksandar Makelov, Ludwig Schmidt, D. Tsipras, and Adrian Vladu · 2018
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Generating natural adversarial examples
Zhengli Zhao, Dheeru Dua, and Sameer Singh · 2018
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Timnit Gebru, J. Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, H. Wallach, Hal Daumé, and K. Crawford · 2018
Technology readiness levels for AI & ML
Alexander Lavin and Gregory Renard · 2020
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Data governance: Organizing data for trustworthy artificial intelligence
M. Janssen, P. Brous, Elsa Estevez, L. Barbosa, and T. Janowski · 2020
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Towards compliant data management systems for healthcare ml
Goutham Ramakrishnan, A. Nori, Hannah Murfet, and Pashmina Cameron · 2020
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Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, S. Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and P. Eckersley · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and V. Smith · 2020
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Beyond accuracy: Behavioral testing of nlp models with checklist
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Cited alongside, same era.
Nature Biomedical Engineering
Towards trustable machine learning · 2018
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Unity: A general platform for intelligent agents
Arthur Juliani, Vincent-Pierre Berges, Esh Vckay, Yuan Gao, Hunter Henry, M. Mattar, and D. Lange · 2018
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An introduction to probabilistic programming
Jan-Willem van de Meent, Brooks Paige, H. Yang, and Frank Wood · 2018
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Theoretical impediments to machine learning with seven sparks from the causal revolution
J. Pearl · 2018
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Double/debiased machine learning for treatment and structural parameters
V. Chernozhukov, D. Chetverikov, M. Demirer, E. Duflo, Christian L. Hansen, Whitney K. Newey, and J. Robins · 2018
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A survey of southern hemisphere meteor showers
Peter Jenniskens, Jack Baggaley, Ian Crumpton, Peter Aldous, Petr Pokorny, Diego Janches, Peter S. Gural, Dave Samuels, Jim Albers, Andreas Howell, Carl Johannink, Martin Breukers, Mohammad Odeh, Nicholas Moskovitz, Jack Collison, and Siddha Ganju · 2018
Cited alongside, same era.
Harnessing the power of real-world evidence (rwe): A checklist to ensure regulatory-grade data quality
R. Miksad and A. Abernethy · 2018
Cited alongside, same era.
Marco Túlio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh · 2020
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Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, K. Heller, D. Moldovan, Ben Adlam, B. Alipanahi, Alex Beutel, C. Chen, Jonathan Deaton, Jacob Eisenstein, M. Hoffman, Farhad Hormozdiari, N. Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, M. Lucic, Y. Ma, Cory Y. McLean, Diana Mincu, Akinori Mitani, A. Montanari, Zachary Nado, V. Natarajan, C. Nielson, Thomas F. Osborne, R. Raman, K. Ramasamy, Rory Sayres, J. Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, V. Veitch, Max Vladymyrov, Xuezhi Wang, K. Webster, S. Yadlowsky, Taedong Yun, Xiaohua Zhai, and D. Sculley · 2020
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Manifolds for unsupervised visual anomaly detection
Louise Naud and Alexander Lavin · 2020
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The frontier of simulation-based inference
K. Cranmer, J. Brehmer, and Gilles Louppe · 2020
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Collider bias undermines our understanding of covid-19 disease risk and severity
Gareth J Griffith, Tim T Morris, Matthew J Tudball, Annie Herbert, Giulia Mancano, Lindsey Pike, Gemma C Sharp, Jonathan Sterne, Tom M Palmer, George Davey Smith, et al · 2020
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Split-treatment analysis to rank heterogeneous causal effects for prospective interventions
Yanbo Xu, Divyat Mahajan, Liz Manrao, A. Sharma, and E. Kiciman · 2020
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Improving the accuracy of medical diagnosis with causal machine learning
Jonathan G Richens, C. M. Lee, and Saurabh Johri · 2020
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Challenges in deploying machine learning: a survey of case studies
Andrei Paleyes, Raoul-Gabriel Urma, and N. Lawrence · 2020
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Sense and sensitivity analysis: Simple post-hoc analysis of bias due to unobserved confounding
Victor Veitch and Anisha Zaveri · 2020
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Learnings from frontier development lab and spaceml - ai accelerators for nasa and esa
Siddha Ganju, Anirudh Koul, Alexander Lavin, J. Veitch-Michaelis, Meher Kasam, and J. Parr · 2020
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Andrew Gelman, Aki Vehtari, Daniel Simpson, Charles Margossian, Bob Carpenter, Yuling Yao, Lauren Kennedy, Jonah Gabry, Paul-Christian Burkner, and Martin Modrak · 2020
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Understanding and visualizing data iteration in machine learning
Fred Hohman, Kanit Wongsuphasawat, Mary Beth Kery, and Kayur Patel · 2020
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Closing the ai accountability gap: defining an end-to-end framework for internal algorithmic auditing
Inioluwa Deborah Raji, Andrew Smart, Rebecca White, M. Mitchell, Timnit Gebru, B. Hutchinson, Jamila Smith-Loud, Daniel Theron, and P. Barnes · 2020
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Regulatory frameworks for development and evaluation of artificial intelligence–based diagnostic imaging algorithms: Summary and recommendations
D. B. Larson, Hugh Harvey, D. Rubin, Neville Irani, J. R. Tse, and C. Langlotz · 2020
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Bias in data-driven ai systems - an introductory survey
Eirini Ntoutsi, P. Fafalios, U. Gadiraju, Vasileios Iosifidis, W. Nejdl, Maria-Esther Vidal, S. Ruggieri, F. Turini, S. Papadopoulos, Emmanouil Krasanakis, I. Kompatsiaris, K. Kinder-Kurlanda, Claudia Wagner, F. Karimi, Miriam Fernández, Harith Alani, B. Berendt, Tina Kruegel, C. Heinze, Klaus Broelemann, Gjergji Kasneci, T. Tiropanis, and Steffen Staab · 2020
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Lessons from archives: strategies for collecting sociocultural data in machine learning
E. Jo and Timnit Gebru · 2020
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Diagnosing bias in data-driven algorithms for healthcare
J. Wiens, W. Price, and M. Sjoding · 2020
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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the spirit-ai extension
Samantha Cruz Rivera, Xiaoxuan Liu, A. Chan, A. K. Denniston, and M. Calvert · 2020
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Towards accountability for machine learning datasets: Practices from software engineering and infrastructure
B. Hutchinson, A. Smart, A. Hanna, Emily L. Denton, Christina Greer, Oddur Kjartansson, P. Barnes, and Margaret Mitchell · 2021
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Common pitfalls and recommendations for using machine learning to detect and prognosticate for covid-19 using chest radiographs and ct scans
Michael Roberts, Derek Driggs, Matthew Thorpe, Julian Gilbey, Michael Yeung, Stephan Ursprung, Angelica I. Avilés-Rivero, Christian Etmann, Cathal McCague, Lucian Beer, Jonathan R. Weir-McCall, Zhongzhao Teng, Effrossyni Gkrania-Klotsas, James H. F. Rudd, Evis Sala, and Carola-Bibiane Schönlieb · 2021
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Unity perception: Generate synthetic data for computer vision
Steve Borkman, Adam Crespi, Saurav Dhakad, Sujoy Ganguly, Jonathan Hogins, You-Cyuan Jhang, Mohsen Kamalzadeh, Bowen Li, Steven Leal, Pete Parisi, Cesar Romero, Wesley Smith, Alex Thaman, Samuel Warren, and Nupur Yadav · 2021
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