Fetching the paper…
Reading the bibliography…
When the performance of a machine learning model varies over groups defined by sensitive attributes (e.g., gender or ethnicity), the performance disparity can be expressed in terms of the probability distributions of the input and output variables over each group.
Counterfactual probabilities: Computational methods, bounds and applications
Balke, A. and Pearl, J · 1994
Earlier work this paper cites.
Labor market institutions and the distribution of wages, 1973-1992: A semiparametric approach
DiNardo, J., Fortin, N. M., and Lemieux, T · 1995
Earlier work this paper cites.
A computational fluid mechanics solution to the monge-kantorovich mass transfer problem
Benamou, J.-D. and Brenier, Y · 2000
Earlier work this paper cites.
Minimizing flows for the monge–kantorovich problem
Angenent, S., Haker, S., and Tannenbaum, A · 2003
Earlier work this paper cites.
Inequalities for the l 1 l_{1} deviation of the empirical distribution
Weissman, T., Ordentlich, E., Seroussi, G., Verdu, S., and Weinberger, M. J · 2003
Earlier work this paper cites.
Causal inference using potential outcomes: Design, modeling, decisions
Rubin, D. B · 2005
Earlier work this paper cites.
Euclidean information theory
Borade, S. and Zheng, L · 2008
Earlier work this paper cites.
Optimal transport: old and new , volume 338
Villani, C · 2008
Earlier work this paper cites.
Prototype selection for interpretable classification
Bien, J. and Tibshirani, R · 2011
Earlier work this paper cites.
Decomposition methods in economics
Fortin, N., Lemieux, T., and Firpo, S · 2011
Earlier work this paper cites.
Robust statistics
Huber, P. J · 2011
Earlier work this paper cites.
Elements of information theory
Cover, T. M. and Thomas, J. A · 2012
Earlier work this paper cites.
Fairness through awareness
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel, R · 2012
Earlier work this paper cites.
Data preprocessing techniques for classification without discrimination
Kamiran, F. and Calders, T · 2012
Earlier work this paper cites.
Anantharam, V., Gohari, A., Kamath, S., and Nair, C · 2013
Earlier work this paper cites.
UCI Machine Learning Repository, 2013
Bache, K. and Lichman, M · 2013
Earlier work this paper cites.
Inference on counterfactual distributions
Chernozhukov, V., Fernández-Val, I., and Melly, B · 2013
Earlier work this paper cites.
Efficient statistics: Extracting information from iid observations
Huang, S.-L., Makur, A., Kozynski, F., and Zheng, L · 2014
Earlier work this paper cites.
A multidisciplinary survey on discrimination analysis
Romei, A. and Ruggieri, S · 2014
Earlier work this paper cites.
Certifying and removing disparate impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., and Venkatasubramanian, S · 2015
Earlier work this paper cites.
Machine bias
Angwin, J., Larson, J., Mattu, S., and Kirchner, L · 2016
Cited alongside, same era.
Big data’s disparate impact
Barocas, S. and Selbst, A. D · 2016
Cited alongside, same era.
Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
Datta, A., Sen, S., and Zick, Y · 2016
Cited alongside, same era.
Equality of opportunity in supervised learning
Hardt, M., Price, E., Srebro, N., et al · 2016
Cited alongside, same era.
Learning representations for counterfactual inference
Johansson, F., Shalit, U., and Sontag, D · 2016
Cited alongside, same era.
Examples are not enough, learn to criticize! criticism for interpretability
Kim, B., Khanna, R., and Koyejo, O. O · 2016
Cited alongside, same era.
Fast threshold tests for detecting discrimination
Pierson, E., Corbett-Davies, S., and Goel, S · 2017
Later among the works it cites.
On fairness and calibration
Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., and Weinberger, K. Q · 2017
Later among the works it cites.
The problem of infra-marginality in outcome tests for discrimination
Simoiu, C., Corbett-Davies, S., Goel, S., et al · 2017
Later among the works it cites.
Dataset shift in machine learning
Sugiyama, M., Lawrence, N. D., Schwaighofer, A., et al · 2017
Later among the works it cites.
From parity to preference-based notions of fairness in classification
Zafar, M. B., Valera, I., Rodriguez, M., Gummadi, K., and Weller, A · 2017
Later among the works it cites.
Measuring discrimination in algorithmic decision making
Žliobaitė, I · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Kleinberg, J., Mullainathan, S., and Raghavan, M · 2016
Cited alongside, same era.
Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models
Žliobaitė, I. and Custers, B · 2016
Cited alongside, same era.
Optimized pre-processing for discrimination prevention
Calmon, F., Wei, D., Vinzamuri, B., Ramamurthy, K. N., and Varshney, K. R · 2017
Cited alongside, same era.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A · 2017
Cited alongside, same era.
Algorithmic decision making and the cost of fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., and Huq, A · 2017
Cited alongside, same era.
Proxy non-discrimination in data-driven systems
Datta, A., Fredrikson, M., Ko, G., Mardziel, P., and Sen, S · 2017
Cited alongside, same era.
Later among the works it cites.
Auditing black-box models for indirect influence
Adler, P., Falk, C., Friedler, S. A., Nix, T., Rybeck, G., Scheidegger, C., Smith, B., and Venkatasubramanian, S · 2018
Later among the works it cites.
A reductions approach to fair classification
Agarwal, A., Beygelzimer, A., Dudík, M., Langford, J., and Wallach, H · 2018
Later among the works it cites.
Why is my classifier discriminatory?
Chen, I., Johansson, F. D., and Sontag, D · 2018
Later among the works it cites.
Amazon scraps secret ai recruiting tool that showed bias against women
Dastin, J · 2018
Later among the works it cites.
Decoupled classifiers for group-fair and efficient machine learning
Dwork, C., Immorlica, N., Kalai, A. T., and Leiserson, M. D · 2018
Later among the works it cites.
Visually communicating and teaching intuition for influence functions
Fisher, A. and Kennedy, E. H · 2018
Later among the works it cites.
Why we need to audit algorithms, 2018
Guszcza, J., Rahwan, I., Bible, W., Cebrian, M., and Katyal, V · 2018
Later among the works it cites.
Algorithmic fairness
Kleinberg, J., Ludwig, J., Mullainathan, S., and Rambachan, A · 2018
Later among the works it cites.
Does mitigating ml’s impact disparity require treatment disparity?
Lipton, Z. C., Chouldechova, A., and McAuley, J · 2018
Later among the works it cites.
The cost of fairness in binary classification
Menon, A. K. and Williamson, R. C · 2018
Later among the works it cites.
On the direction of discrimination: An information-theoretic analysis of disparate impact in machine learning
Wang, H., Ustun, B., and Calmon, F. P · 2018
Later among the works it cites.
From soft classifiers to hard decisions: How fair can we be?
Canetti, R., Cohen, A., Dikkala, N., Ramnarayan, G., Scheffler, S., and Smith, A · 2019
Closest in time.
Obtaining fairness using optimal transport theory
Del Barrio, E., Gamboa, F., Gordaliza, P., and Loubes, J.-M · 2019
Closest in time.