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We present a new data-driven model of fairness that, unlike existing static definitions of individual or group fairness is guided by the unfairness complaints received by the system.
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. Zemel · 2012
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Online learning with switching costs and other adaptive adversaries
N. Cesa-Bianchi, O. Dekel, and O. Shamir · 2013
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R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Fairness in multi-agent sequential decision-making
C. Zhang and J. A. Shah · 2014
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A computer program used for bail and sentencing decisions was labeled biased against blacks. it’s actually not that clear
A. Feller, E. Pierson, S. Corbett-Davies, and S. Goel · 2016
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Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
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Fairness in learning: Classic and contextual bandits
M. Joseph, M. Kearns, J. H. Morgenstern, and A. Roth · 2016
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L. E. Celis and N. K. Vishnoi · 2017
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Fair clustering through fairlets
F. Chierichetti, R. Kumar, S. Lattanzi, and S. Vassilvitskii · 2017
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Importance sampling for fair policy selection
S. Doroudi, P. S. Thomas, and E. Brunskill · 2017
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On calibration of modern neural networks
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger · 2017
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Fairness in reinforcement learning
S. Jabbari, M. Joseph, M. Kearns, J. Morgenstern, and A. Roth · 2017
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Meritocratic fairness for cross-population selection
M. J. Kearns, A. Roth, and Z. S. Wu · 2017
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J. Kleinberg, S. Mullainathan, and M. Raghavan · 2017
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Counterfactual fairness
M. J. Kusner, J. R. Loftus, C. Russell, and R. Silva · 2017
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Calibrated fairness in bandits
Y. Liu, G. Radanovic, C. Dimitrakakis, D. Mandal, and D. C. Parkes · 2017
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On fairness and calibration
G. Pleiss, M. Raghavan, F. Wu, J. Kleinberg, and K. Q. Weinberger · 2017
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A reductions approach to fair classification
A. Agarwal, A. Beygelzimer, M. Dudík, J. Langford, and H. Wallach · 2018
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On preserving non-discrimination when combining expert advice
A. Blum, S. Gunasekar, T. Lykouris, and N. Srebro · 2018
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Ranking with fairness constraints
L. E. Celis, D. Straszak, and N. K. Vishnoi · 2018
Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks. 2016
J. Angwin, J. Larson, S. Mattu, and L. Kirchner · 2019
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A. Backurs, P. Indyk, K. Onak, B. Schieber, A. Vakilian, and T. Wagner · 2019
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Fairness in recommendation ranking through pairwise comparisons
A. Beutel, J. Chen, T. Doshi, H. Qian, L. Wei, Y. Wu, L. Heldt, Z. Zhao, L. Hong, E. H. Chi, et al · 2019
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Fair transfer learning with missing protected attributes
A. Coston, K. N. Ramamurthy, D. Wei, K. R. Varshney, S. Speakman, Z. Mustahsan, and S. Chakraborty · 2019
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Individual fairness in hindsight
S. Gupta and V. Kamble · 2019
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Decoupled classifiers for group-fair and efficient machine learning
C. Dwork, N. Immorlica, A. T. Kalai, and M. Leiserson · 2018
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Online learning with an unknown fairness metric
S. Gillen, C. Jung, M. Kearns, and A. Roth · 2018
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M. Gupta, A. Cotter, M. M. Fard, and S. Wang · 2018
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Fairness without demographics in repeated loss minimization
T. B. Hashimoto, M. Srivastava, H. Namkoong, and P. Liang · 2018
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Meritocratic fairness for infinite and contextual bandits
M. Joseph, M. J. Kearns, J. Morgenstern, S. Neel, and A. Roth · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
M. J. Kearns, S. Neel, A. Roth, and Z. S. Wu · 2018
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S. Kannan, A. Roth, and J. Ziani · 2019
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Average individual fairness: Algorithms, generalization and experiments
M. Kearns, A. Roth, and S. Sharifi-Malvajerdi · 2019
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Noise-tolerant fair classification
A. Lamy, Z. Zhong, A. K. Menon, and N. Verma · 2019
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From fair decision making to social equality
H. Mouzannar, M. I. Ohannessian, and N. Srebro · 2019
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Pairwise fairness for ranking and regression
H. Narasimhan, A. Cotter, M. Gupta, and S. Wang · 2019
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Average individual fairness: Algorithms, generalization and experiments
S. Sharifi-Malvajerdi, M. J. Kearns, and A. Roth · 2019
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Phase transitions and cyclic phenomena in bandits with switching constraints
D. Simchi-Levi and Y. Xu · 2019
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M. Wen, O. Bastani, and U. Topcu · 2019
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Individual fairness in pipelines
C. Dwork, C. Ilvento, and M. Jagadeesan · 2020
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Optimizing long-term social welfare in recommender systems: A constrained matching approach
M. Mladenov, E. Creager, O. Ben-Porat, K. Swersky, R. Zemel, and C. Boutilier · 2020
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Robust optimization for fairness with noisy protected groups
S. Wang, W. Guo, H. Narasimhan, A. Cotter, M. Gupta, and M. I. Jordan · 2020
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