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In this paper we propose a causal modeling approach to intersectional fairness, and a flexible, task-specific method for computing intersectionally fair rankings.
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J. M. Robins · 2003
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J. Zhang and E. Bareinboim · 2006
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S. A. Shields · 2008
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Machine bias: There’s software used across the country to predict future criminals
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Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
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Fairness in machine learning
S. Barocas, M. Hardt, and A. Narayanan · 2017
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Avoiding discrimination through causal reasoning
N. Kilbertus, M. R. Carulla, G. Parascandolo, M. Hardt, D. Janzing, and B. Schölkopf · 2017
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Counterfactual fairness
M. J. Kusner, J. R. Loftus, C. Russell, and R. Silva · 2017
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When worlds collide: integrating different counterfactual assumptions in fairness
C. Russell, M. J. Kusner, J. Loftus, and R. Silva · 2017
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Measuring fairness in ranked outputs
K. Yang and J. Stoyanovich · 2017
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On discrimination discovery and removal in ranked data using causal graph
Y. Wu, L. Zhang, and X. Wu · 2018
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Designing fair ranking schemes
A. Asudeh, H. V. Jagadish, J. Stoyanovich, and G. Das · 2019
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Path-specific counterfactual fairness
S. Chiappa · 2019
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The sensitivity of counterfactual fairness to unmeasured confounding
N. Kilbertus, P. J. Ball, M. J. Kusner, A. Weller, and R. Silva · 2019
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Multiaccuracy: Black-box post-processing for fairness in classification
M. P. Kim, A. Ghorbani, and J. Y. Zou · 2019
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M. Kusner, C. Russell, J. Loftus, and R. Silva · 2019
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M. Zehlike, F. Bonchi, C. Castillo, S. Hajian, M. Megahed, and R. Baeza-Yates · 2017
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R. K. E. Bellamy, K. Dey, M. Hind, S. C. Hoffman, S. Houde, K. Kannan, P. Lohia, J. Martino, S. Mehta, A. Mojsilovic, S. Nagar, K. N. Ramamurthy, J. T. Richards, D. Saha, P. Sattigeri, M. Singh, K. R. Varshney, and Y. Zhang · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
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Ranking with fairness constraints
L. E. Celis, D. Straszak, and N. K. Vishnoi · 2018
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Multicalibration: Calibration for the (computationally-identifiable) masses
U. Hebert-Johnson, M. Kim, O. Reingold, and G. Rothblum · 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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ifair: Learning individually fair data representations for algorithmic decision making
P. Lahoti, K. P. Gummadi, and G. Weikum · 2019
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Learning optimal fair policies
R. Nabi, D. Malinsky, and I. Shpitser · 2019
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Balanced ranking with diversity constraints
K. Yang, V. Gkatzelis, and J. Stoyanovich · 2019
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CSRankings: Computer Science Rankings, 2017-2020
E. Berger · 2020
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Interventions for ranking in the presence of implicit bias
L. E. Celis, A. Mehrotra, and N. K. Vishnoi · 2020
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A snapshot of the frontiers of fairness in machine learning
A. Chouldechova and A. Roth · 2020
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Data feminism
C. D’Ignazio and L. F. Klein · 2020
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An intersectional definition of fairness
J. R. Foulds, R. Islam, K. N. Keya, and S. Pan · 2020
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The long road to fairer algorithms, 2020
M. J. Kusner and J. R. Loftus · 2020
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Reducing disparate exposure in ranking: A learning to rank approach
M. Zehlike and C. Castillo · 2020
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