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We turn the definition of individual fairness on its head---rather than ascertaining the fairness of a model given a predetermined metric, we find a metric for a given model that satisfies individual fairness.
On the problem of the most efficient tests of statistical hypotheses
Jerzy Neyman and Egon Sharpe Pearson · 1933
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Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state
Ralph G Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E Elger · 2001
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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Extremal mechanisms for local differential privacy
Peter Kairouz, Sewoong Oh, and Pramod Viswanath · 2016
Cited alongside, same era.
Optimized pre-processing for discrimination prevention
Flavio Calmon, Dennis Wei, Bhanukiran Vinzamuri, Karthikeyan Natesan Ramamurthy, and Kush R Varshney · 2017
Cited alongside, same era.
Strategic Subject List
City of Chicago · 2017
Cited alongside, same era.
UCI machine learning repository
Dheeru Dua and Efi Karra Taniskidou · 2017
Cited alongside, same era.
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, and Krishna P Gummadi · 2017
Cited alongside, same era.
The frontiers of fairness in machine learning
Alexandra Chouldechova and Aaron Roth · 2018
From soft classifiers to hard decisions: How fair can we be?
Ran Canetti, Aloni Cohen, Nishanth Dikkala, Govind Ramnarayan, Sarah Scheffler, and Adam Smith · 2019
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Certified adversarial robustness via randomized smoothing
Jeremy Cohen, Elan Rosenfeld, and Zico Kolter · 2019
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Practitioner’s guide to COMPAS core
Equivant · 2019
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Metric learning for individual fairness
Christina Ilvento · 2019
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Eliciting and enforcing subjective individual fairness
Christopher Jung, Michael Kearns, Seth Neel, Aaron Roth, Logan Stapleton, and Zhiwei Steven Wu · 2019
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Cited alongside, same era.
Online learning with an unknown fairness metric
Stephen Gillen, Christopher Jung, Michael Kearns, and Aaron Roth · 2018
Cited alongside, same era.
Learning adversarially fair and transferable representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 2018
Cited alongside, same era.
Pranay K Lohia, Karthikeyan Natesan Ramamurthy, Manish Bhide, Diptikalyan Saha, Kush R Varshney, and Ruchir Puri · 2019
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Learning fair representations for kernel models
Zilong Tan, Samuel Yeom, Matt Fredrikson, and Ameet Talwalkar · 2019
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