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A common distinction in fair machine learning, in particular in fair classification, is between group fairness and individual fairness.
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M. E. Celebi and K. Aydin · 2016
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Calibration for the (computationally-identifiable) masses
Ú. Hébert-Johnson, M. P. Kim, O. Reingold, and G. N. Rothblum · 2018
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Meritocratic fairness for infinite and contextual bandits
M. Joseph, M. Kearns, J. Morgenstern, S. Neel, and A. Roth · 2018
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Preventing fairness gerrymandering: Auditing and learning for subgroup fairness
M. Kearns, S. Neel, and Z. S. Roth, A. Wu · 2018
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Privacy preserving clustering with constraints
C. Rösner and M. Schmidt · 2018
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Fair coresets and streaming algorithms for fair k-means clustering
M. Schmidt, C. Schwiegelshohn, and C. Sohler · 2018
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UCI machine learning repository, 2019
D. Dua and C. Graff · 2019
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Coresets for clustering with fairness constraints
L. Huang, S. H.-C. Jiang, and N. K. Vishnoi · 2019
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Multiaccuracy: Black-box post-processing for fairness in classification
M. P. Kim, A. Ghorbani, and J. Zou · 2019
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Making existing clusterings fairer: Algorithms, complexity results and insights
I. Davidson and S. S. Ravi · 2020
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A center in your neighborhood: Fairness in facility location
C. Jung, S. Kannan, and N. Lutz · 2020
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(individual) fairness for k k -clustering
S. Mahabadi and A. Vakilian · 2020
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