Learning to Complement Humans

Judgment under uncertainty: Heuristics and biases

Amos Tversky and Daniel Kahneman · 1974

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Using analytic qp and sparseness to speed training of support vector machines

John C Platt · 1999

Earlier work this paper cites.

Predicting good probabilities with supervised learning

Alexandru Niculescu-Mizil and Rich Caruana · 2005

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Complementary computing: policies for transferring callers from dialog systems to human receptionists

Eric Horvitz and Tim Paek · 2007

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Galaxy zoo: morphologies derived from visual inspection of galaxies from the sloan digital sky survey

Chris J Lintott, Kevin Schawinski, Anže Slosar, Kate Land, Steven Bamford, Daniel Thomas, M Jordan Raddick, Robert C Nichol, Alex Szalay, Dan Andreescu, et al · 2008

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Combining human and machine intelligence in large-scale crowdsourcing

Ece Kamar, Severin Hacker, and Eric Horvitz · 2012

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Rethinking the inception architecture for computer vision

Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016

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Implementation of camelyon’16 grand challenge

Arjun Vekariya · 2016

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Deep learning for identifying metastatic breast cancer

Original

Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad, and Andrew H Beck · 2016

Cited alongside, same era.

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes Van Diest, Bram Van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen AWM Van Der Laak, Meyke Hermsen, Quirine F Manson, Maschenka Balkenhol, et al · 2017

Cited alongside, same era.

Dermatologist-level classification of skin cancer with deep neural networks

Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017

Cited alongside, same era.

The rise of deep learning in drug discovery

Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, and Thomas Blaschke · 2018

Cited alongside, same era.

Human-in-the-loop interpretability prior

Isaac Lage, Andrew Ross, Samuel J Gershman, Been Kim, and Finale Doshi-Velez · 2018

Cited alongside, same era.

The principles and limits of algorithm-in-the-loop decision making

Ben Green and Yiling Chen · 2019

Later among the works it cites.

Human decision making with machine assistance: An experiment on bailing and jailing

Nina Grgić-Hlača, Christoph Engel, and Krishna P Gummadi · 2019

Later among the works it cites.

Learning representations by humans, for humans

Original

Sophie Hilgard, Nir Rosenfeld, Mahzarin R Banaji, Jack Cao, and David C Parkes · 2019

Later among the works it cites.

The great disruption: How machine intelligence will transform the role of lawyers in the delivery of legal services

John O McGinnis and Russell G Pearce · 2019

Later among the works it cites.

The algorithmic automation problem: Prediction, triage, and human effort

Original

Maithra Raghu, Katy Blumer, Greg Corrado, Jon Kleinberg, Ziad Obermeyer, and Sendhil Mullainathan · 2019