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In the ML fairness literature, there have been few investigations through the viewpoint of philosophy, a lens that encourages the critical evaluation of basic assumptions.
Fact, Fiction, and Forecast
N. Goodman · 1983
Earlier work this paper cites.
Minds, brains and programs
J. R. Searle · 1984
Earlier work this paper cites.
Foundations of Measurement Vol 3: Representation, Axiomatization, and Invariance
P. S. A. T. David H. Krantz, R. Duncan Luce · 1990
Earlier work this paper cites.
A Treatise of Human Nature: Being an Attempt to Introduce the Experimental Method of Reasoning into Moral Subjects
D. Hume · 2009
Earlier work this paper cites.
The Oxford Handbook of Value Theory
I. Hirose and J. Olson · 2015
Earlier work this paper cites.
Machine bias
S. M. Julia Angwin, Jeff Larson and L. Kirchner · 2016
Earlier work this paper cites.
"why should i trust you?": Explaining the predictions of any classifier, 2016
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Cited alongside, same era.
Compas risk scales: Demonstrating accuracy equity and predictive parity
C. M. William Dieterich and T. Brennan · 2016
Cited alongside, same era.
Automated inference on criminality using face images
X. Wu and X. Zhang · 2016
Cited alongside, same era.
Fairness in machine learning: Lessons from political philosophy
R. Binns · 2017
Cited alongside, same era.
Towards a rigorous science of interpretable machine learning, 2017
F. Doshi-Velez and B. Kim · 2017
Cited alongside, same era.
Fairness in machine learning
M. Hardt and S. Barocas · 2017
Later among the works it cites.
Introduction to the philosophy and mathematics of algorithmic learning theory
V. S. Harizanov · 2017
Later among the works it cites.
Fairness and abstraction in sociotechnical systems
S. A. F. S. V. J. V. Andrew D. Selbst, Danah Boyd · 2018
Later among the works it cites.
Human perceptions of fairness in algorithmic decision making: A case study of criminal risk prediction, 2018
N. Grgić-Hlača, E. M. Redmiles, K. P. Gummadi, and A. Weller · 2018
Later among the works it cites.
Fairness and Machine Learning
S. Barocas, M. Hardt, and A. Narayanan · 2019
Closest in time.
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