Fetching the paper…
Reading the bibliography…
AI practitioners typically strive to develop the most accurate systems, making an implicit assumption that the AI system will function autonomously.
Addressing the loss-metric mismatch with adaptive loss alignment
Huang, C.; Zhai, S.; Talbott, W.; Bautista, M. A.; Sun, S.-Y.; Guestrin, C.; and Susskind, J. 2019 · 1905
Earlier work this paper cites.
Eliciting and Enforcing Subjective Individual Fairness
Jung, C.; Kearns, M.; Neel, S.; Roth, A.; Stapleton, L.; and Wu, Z. S. 2019 · 1905
Earlier work this paper cites.
Theory of games and economic behavior
Morgenstern, O.; and Von Neumann, J. 1953 · 1953
Earlier work this paper cites.
Expert judgment about uncertainty: Bayesian decision making in realistic settings
Beach, B. H. 1975 · 1975
Earlier work this paper cites.
The use of a heuristic problem-solving hierarchy to facilitate the explanation of hypothesis-directed reasoning
Horvitz, E.; Heckerman, D.; Nathwani, B.; and Fagan, L. 1986 · 1986
Earlier work this paper cites.
Statlog (German Credit Data) Data Set
Hofmann, H. 1994 · 1994
Earlier work this paper cites.
A tutorial on support vector machines for pattern recognition
Burges, C. J. 1998 · 1998
Earlier work this paper cites.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
Platt, J. 1999 · 1999
Earlier work this paper cites.
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
Zadrozny, B.; and Elkan, C. 2001 · 2001
Earlier work this paper cites.
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
Zadrozny, B.; Langford, J.; and Abe, N. 2003 · 2003
Earlier work this paper cites.
A survey of outlier detection methodologies
Hodge, V.; and Austin, J. 2004 · 2004
Earlier work this paper cites.
Predicting good probabilities with supervised learning
Niculescu-Mizil, A.; and Caruana, R. 2005 · 2005
Earlier work this paper cites.
Considering cost asymmetry in learning classifiers
Bach, F. R.; Heckerman, D.; and Horvitz, E. 2006 · 2006
Earlier work this paper cites.
Does the Whole Exceed its Parts? The Effect of Explanations on Complementary Team Performance
Bansal, G.; Wu, T.; Zhou, J.; Fok, R.; Nushi, B.; Kamar, E.; Ribeiro, M. T.; and Weld, D. S. 2020 · 2006
Cited alongside, same era.
Making business predictions by combining human and machine intelligence in prediction markets
Nagar, Y.; and Malone, T. 2011 · 2011
Cited alongside, same era.
Combining human and machine intelligence in large-scale crowdsourcing
Kamar, E.; Hacker, S.; and Horvitz, E. 2012 · 2012
Cited alongside, same era.
Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Caruana, R.; Lou, Y.; Gehrke, J.; Koch, P.; Sturm, M.; and Elhadad, N. 2015 · 2015
Cited alongside, same era.
Probabilistic machine learning and artificial intelligence
Ghahramani, Z. 2015 · 2015
Cited alongside, same era.
Fairness constraints: Mechanisms for fair classification
Zafar, M. B.; Valera, I.; Rodriguez, M. G.; and Gummadi, K. P. 2017 · 2017
Later among the works it cites.
Explainable Machine Learning Challenge
Fico. 2018 · 2018
Later among the works it cites.
Deep anomaly detection with outlier exposure
Hendrycks, D.; Mazeika, M.; and Dietterich, T. G. 2018 · 2018
Later among the works it cites.
Predict responsibly: improving fairness and accuracy by learning to defer
Madras, D.; Pitassi, T.; and Zemel, R. 2018 · 2018
Later among the works it cites.
Human–machine partnership with artificial intelligence for chest radiograph diagnosis
Patel, B. N.; Rosenberg, L.; Willcox, G.; Baltaxe, D.; Lyons, M.; Irvin, J.; Rajpurkar, P.; Amrhein, T.; Gupta, R.; Halabi, S.; et al. 2019 · 2019
Later among the works it cites.
The Challenge of Crafting Intelligible Intelligence
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y.; and Ghahramani, Z. 2016 · 2016
Cited alongside, same era.
How We Analyzed the COMPAS Recidivism Algorithm
ProPublica. 2016 · 2016
Cited alongside, same era.
"Why should I trust you?": Explaining the predictions of any classifier
Ribeiro, M. T.; Singh, S.; and Guestrin, C. 2016 · 2016
Cited alongside, same era.
On calibration of modern neural networks
Guo, C.; Pleiss, G.; Sun, Y.; and Weinberger, K. Q. 2017 · 2017
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D.; and Gimpel, K. 2017 · 2017
Cited alongside, same era.
Training confidence-calibrated classifiers for detecting out-of-distribution samples
Lee, K.; Lee, H.; Lee, K.; and Shin, J. 2017 · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S. M.; and Lee, S.-I. 2017 · 2017
Cited alongside, same era.
Weld, D. S.; and Bansal, G. 2019 · 2019
Later among the works it cites.
Art. 22 GDPR, Automated individual decision-making, including profiling
GDPR. 2020 · 2020
Closest in time.
Consistent Estimators for Learning to Defer to an Expert
Mozannar, H.; and Sontag, D. 2020 · 2020
Closest in time.
Washington state passes landmark facial recognition bill, reining in government use of AI
Nickelsburg, M. 2020 · 2020
Closest in time.
Learning to Complement Humans
Wilder, B.; Horvitz, E.; and Kamar, E. 2020 · 2020
Closest in time.
Regional Tree Regularization for Interpretability in Black Box Models
Wu, M.; Parbhoo, S.; Hughes, M.; Kindle, R.; Celi, L.; Zazzi, M.; Roth, V.; and Doshi-Velez, F. 2020 · 2020
Closest in time.
Multitask learning and benchmarking with clinical time series data
Harutyunyan, H.; Khachatrian, H.; Kale, D. C.; Ver Steeg, G.; and Galstyan, A. 2019 · 2052
Closest in time.