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We revisit the notion of individual fairness proposed by Dwork et al.
Lapack: A portable linear algebra library for high-performance computers
E. Anderson, Z. Bai, J. Dongarra, A. Greenbaum, A. McKenney, J. Du Croz, S. Hammarling, J. Demmel, C. Bischof, and D. Sorensen · 1990
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Rethinking the American race problem
R. L. Brooks · 1992
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Semantic manifold learning for image retrieval
Y. Lin, T. Liu, and H. Chen · 2005
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Discrimination-aware data mining
D. Pedreschi, S. Ruggieri, and F. Turini · 2008
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Evaluating the predictive validity of the compas risk and needs assessment system
T. Brennan, W. Dieterich, and B. Ehret · 2009
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Building classifiers with independency constraints
T. Calders, F. Kamiran, and M. Pechenizkiy · 2009
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Communities and crime dataset, uci machine learning repository, 2009
R. M · 2009
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Discrimination aware decision tree learning
F. Kamiran, T. Calders, and M. Pechenizkiy · 2010
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Fairness-aware learning through regularization approach
T. Kamishima, S. Akaho, and J. Sakuma · 2011
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Fairness through awareness
C. Dwork, M. Hardt, T. Pitassi, O. Reingold, and R. S. Zemel · 2012
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Considerations on fairness-aware data mining
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2012
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Learning fair representations
R. S. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork · 2013
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Multiview triplet embedding: Learning attributes in multiple maps
E. Amid and A. Ukkonen · 2015
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Certifying and removing disparate impact
M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian · 2015
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Machine bias: There’s software used across the country to predict future criminals and it’s biased against blacks
J. Angwin, J. Larson, S. Mattu, and L. Kirchner · 2016
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Artificial intelligence’s white guy problem
K. Crawford · 2016
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On the (im)possibility of fairness
S. A. Friedler, C. Scheidegger, and S. Venkatasubramanian · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
M. Hardt, E. Price, and N. Srebro · 2016
Cited alongside, same era.
Fair clustering through fairlets
F. Chierichetti, R. Kumar, S. Lattanzi, and S. Vassilvitskii · 2017
Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment
M. B. Zafar, I. Valera, M. Gomez-Rodriguez, and K. P. Gummadi · 2017
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Fairness constraints: Mechanisms for fair classification
M. B. Zafar, I. Valera, M. Gomez-Rodriguez, and K. P. Gummadi · 2017
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Fa*ir: A fair top-k ranking algorithm
M. Zehlike, F. Bonchi, C. Castillo, S. Hajian, M. Megahed, and R. A. Baeza-Yates · 2017
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Equity of attention: Amortizing individual fairness in rankings
J. Biega, K. P. Gummadi, and G. Weikum · 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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A unified approach to quantifying algorithmic unfairness: Measuring individual &group unfairness via inequality indices
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
A. Chouldechova · 2017
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Algorithmic decision making and the cost of fairness
S. Corbett-Davies, E. Pierson, A. Feller, S. Goel, and A. Huq · 2017
Cited alongside, same era.
Representation learning on graphs: Methods and applications
W. L. Hamilton, R. Ying, and J. Leskovec · 2017
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Meritocratic fairness for cross-population selection
M. Kearns, A. Roth, and Z. S. Wu · 2017
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Inherent trade-offs in the fair determination of risk scores
J. M. Kleinberg, S. Mullainathan, and M. Raghavan · 2017
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From parity to preference-based notions of fairness in classification
M. Zafar, I. Valera, M. Rodriguez, K. Gummadi, and A. Weller · 2017
Cited alongside, same era.
T. Speicher, H. Heidari, H. Grgic-Hlaca, K. Gummadi, A. Singla, A. Weller, and M. B. Zafar · 2018
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Online set selection with fairness and diversity constraints
J. Stoyanovich, K. Yang, and H. V. Jagadish · 2018
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Designing fair ranking schemes
A. Asudeh, H. V. Jagadish, J. Stoyanovich, and G. Das · 2019
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Exploring fairness of ranking in online job marketplaces
S. Elbassuoni, S. Amer-Yahia, C. E. Atie, A. Ghizzawi, and B. Oualha · 2019
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Eliciting and enforcing subjective individual fairness
C. Jung, M. Kearns, S. Neel, A. Roth, L. Stapleton, and Z. S. Wu · 2019
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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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Interventional fairness: Causal database repair for algorithmic fairness
B. Salimi, L. Rodriguez, B. Howe, and D. Suciu · 2019
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