Average Individual Fairness: Algorithms, Generalization and Experiments

Multicalibration: Calibration for the (computationally-identifiable) masses

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On general minimax theorems

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An Introduction to Computational Learning Theory

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The new york city high school match

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Fairness through awareness

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Online learning and online convex optimization

Shalev-Shwartz, S. (2012) · 2012

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Equality of opportunity in supervised learning

Hardt, M., Price, E., Srebro, N., et al. (2016) · 2016

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Fairness in learning: Classic and contextual bandits

Joseph, M., Kearns, M., Morgenstern, J. H., and Roth, A. (2016) · 2016

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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments

Chouldechova, A. (2017) · 2017

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Inherent trade-offs in the fair determination of risk scores

Kleinberg, J., Mullainathan, S., and Raghavan, M. (2017) · 2017

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A reductions approach to fair classification

Agarwal, A., Beygelzimer, A., Dudik, M., Langford, J., and Wallach, H. (2018) · 2018

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Online learning with an unknown fairness metric

Gillen, S., Jung, C., Kearns, M., and Roth, A. (2018) · 2018

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Meritocratic fairness for infinite and contextual bandits

Joseph, M., Kearns, M., Morgenstern, J., Neel, S., and Roth, A. (2018) · 2018

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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness

Kearns, M., Neel, S., Roth, A., and Wu, Z. S. (2018) · 2018

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Prediction-based decisions and fairness: A catalogue of choices, assumptions, and definitions

Original

Mitchell, S., Potash, E., and Barocas, S. (2018) · 2018

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Probably approximately metric-fair learning

Original

Yona, G. and Rothblum, G. N. (2018) · 2018

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An empirical study of rich subgroup fairness for machine learning

Kearns, M., Neel, S., Roth, A., and Wu, Z. S. (2019) · 2019

Closest in time.