Fetching the paper…
Reading the bibliography…
Methods for building fair predictors often involve tradeoffs between fairness and accuracy and between different fairness criteria, but the nature of these tradeoffs varies.
1905
Earlier work this paper cites.
Neyman J (1923) Justification of applications of the calculus of probabilities to the solutions of certain questions in agricultural experimentation. Excerpts english translation (Reprinted). Statistical Science 5:463–472
1923
Earlier work this paper cites.
Nabi R, Shpitser I (2018) Fair inference on outcomes. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence, pp 1931–1940, URL https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16683
1940
Earlier work this paper cites.
Woodworth B, Gunasekar S, Ohannessian MI, et al (2017) Learning non-discriminatory predictors. In: Kale S, Shamir O (eds) Proceedings of the 2017 Conference on Learning Theory, Proceedings of Machine Learning Research, vol 65. PMLR, Amsterdam, Netherlands, pp 1920–1953, URL http://proceedings.mlr.press/v65/woodworth17a.html
1953
Earlier work this paper cites.
Holland PW (1986) Statistics and causal inference. Journal of the American Statistical Association 81(396):968. doi:10.2307/2289069
1986
Earlier work this paper cites.
Rosenbaum PR (1987) Sensitivity analysis for certain permutation inferences in matched observational studies. Biometrika 74(1):13–26. doi:10.2307/2336017
1987
Earlier work this paper cites.
Shapiro A (1991) Asymptotic analysis of stochastic programs. Annals of Operations Research 30(1):169–186. doi:10.1007/BF02204815
1991
Earlier work this paper cites.
Bickel PJ, Klaassen CA, Ritov Y, et al (1993) Efficient and adaptive estimation for semiparametric models. Johns Hopkins series in the mathematical sciences, Johns Hopkins University Press, Baltimore, MD
1993
Earlier work this paper cites.
Breiman L (1996) Stacked regressions. Machine Learning 24(1):49–64. doi:10.1007/BF00117832
1996
Earlier work this paper cites.
Juditsky A, Nemirovski A (2000) Functional aggregation for nonparametric regression. The Annals of Statistics 28(3). doi:10.1214/aos/1015951994
2000
Earlier work this paper cites.
Györfi L, Kohler M, Krzyzak A, et al (2002) A Distribution-Free Theory of Nonparametric Regression. Springer Series in Statistics, Springer-Verlag, New York, NY
2002
Earlier work this paper cites.
van der Laan MJ, Robins JM (2003) Unified Methods for Censored Longitudinal Data and Causality. Springer Series in Statistics, Springer, New York, NY, doi:10.1007/978-0-387-21700-0
2003
Earlier work this paper cites.
Tsybakov AB (2003) Optimal rates of aggregation. In: Schölkopf B, Warmuth MK (eds) Learning Theory and Kernel Machines, Lecture Notes in Computer Science, vol 2777. Springer Berlin Heidelberg, p 303–313, doi:10.1007/978-3-540-45167-9˙23
2003
Earlier work this paper cites.
Tsiatis AA (2006) Semiparametric Theory and Missing Data. Springer, New York, NY, doi:10.1007/0-387-37345-4
2006
Earlier work this paper cites.
2008
Earlier work this paper cites.
Calders T, Kamiran F, Pechenizkiy M (2009) Building classifiers with independency constraints. In: 2009 IEEE International Conference on Data Mining Workshops. IEEE, pp 13–18, doi:10.1109/ICDMW.2009.83
2009
Earlier work this paper cites.
Calders T, Verwer S (2010) Three naive bayes approaches for discrimination-free classification. Data Mining and Knowledge Discovery 21(2):277–292. doi:10.1007/s10618-010-0190-x
2010
Earlier work this paper cites.
Polley EC, Van Der Laan MJ (2010) Super learner in prediction. UC Berkeley Division of Biostatistics Working Paper Series 266. URL https://biostats.bepress.com/ucbbiostat/paper266
2010
Earlier work this paper cites.
Raskutti G, Wainwright MJ, Yu B (2011) Minimax rates of estimation for high-dimensional linear regression over ℓ q \ell_{q} -balls. IEEE transactions on information theory 57(10):6976–6994. URL https://doi.org/10.1109/TIT.2011.2165799
2011
Earlier work this paper cites.
Kamiran F, Calders T (2012) Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems 33(1):1–33. doi:10.1007/s10115-011-0463-8
2012
Earlier work this paper cites.
Kamishima T, Akaho S, Asoh H, et al (2012) Fairness-aware classifier with prejudice remover regularizer. In: Flach PA, De Bie T, Cristianini N (eds) Machine Learning and Knowledge Discovery in Databases, vol 7524. Springer Berlin Heidelberg, pp 35–50, doi:10.1007/978-3-642-33486-3˙3
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
Wachsmuth G (2013) On LICQ and the uniqueness of lagrange multipliers. Operations Research Letters 41(1):78–80. doi:10.1016/j.orl.2012.11.009
2012
Earlier work this paper cites.
Liu W, Kuramoto SJ, Stuart EA (2013) An introduction to sensitivity analysis for unobserved confounding in nonexperimental prevention research. Prevention Science 14(6):570–580. doi:10.1007/s11121-012-0339-5
2013
Cited alongside, same era.
Zemel R, Wu Y, Swersky K, et al (2013) Learning fair representations. In: Dasgupta S, McAllester D (eds) Proceedings of the 30th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol 28. PMLR, Atlanta, Georgia, USA, pp 325–333, URL https://proceedings.mlr.press/v28/zemel13.html
2013
Cited alongside, same era.
Richardson A, Hudgens MG, Gilbert PB, et al (2014) Nonparametric bounds and sensitivity analysis of treatment effects. Statistical Science 29(4). doi:10.1214/14-STS499
2014
Cited alongside, same era.
2015
2018
Later among the works it cites.
Buolamwini J, Gebru T (2018) Gender shades: Intersectional accuracy disparities in commercial gender classification. In: Friedler SA, Wilson C (eds) Proceedings of the 1st Conference on Fairness, Accountability and Transparency, Proceedings of Machine Learning Research, vol 81. PMLR, New York, NY, USA, pp 77–91, URL http://proceedings.mlr.press/v81/buolamwini18a.html
2018
Later among the works it cites.
Chernozhukov V, Chetverikov D, Demirer M, et al (2018) Double/debiased machine learning for treatment and structural parameters. The Econometrics Journal 21(1):C1–C68. doi:10.1111/ectj.12097
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Feldman M, Friedler SA, Moeller J, et al (2015) Certifying and removing disparate impact. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, KDD ’15, p 259–268, doi:10.1145/2783258.2783311
2015
Cited alongside, same era.
Northpointe (2015) Practitioners guide to COMPAS core. URL http://www.northpointeinc.com/downloads/compas/Practitioners-Guide-COMPAS-Core-_031915.pdf
2015
Cited alongside, same era.
Angwin J, Larson J (2016) Bias in criminal risk scores is mathematically inevitable, researchers say. ProPublica URL https://www.propublica.org/article/bias-in-criminal-risk-scores-is-mathematically-inevitable-researchers-say
2016
Cited alongside, same era.
Angwin J, Larson J, Mattu S, et al (2016) Machine bias. ProPublica URL https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
2016
Cited alongside, same era.
Chouldechova A (2017) Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big data 5(2):153–163. doi:10.1089/big.2016.0047
2016
Cited alongside, same era.
Hardt M, Price E, Price E, et al (2016) Equality of opportunity in supervised learning. In: Lee DD, Sugiyama M, Luxburg UV, et al (eds) Advances in Neural Information Processing Systems 29. Curran Associates, Inc., NIPS 2016, pp 3315–3323, URL http://papers.nips.cc/paper/6374-equality-of-opportunity-in-supervised-learning.pdf
2016
Cited alongside, same era.
Kennedy EH (2016) Semiparametric theory and empirical processes in causal inference. In: He H, Wu P, Chen DGD (eds) Statistical Causal Inferences and Their Applications in Public Health Research. Springer International Publishing, p 141–167, doi:10.1007/978-3-319-41259-7˙8
2016
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Later among the works it cites.
Menon AK, Williamson RC (2018) The cost of fairness in binary classification. In: Friedler SA, Wilson C (eds) Proceedings of the 1st Conference on Fairness, Accountability and Transparency, Proceedings of Machine Learning Research, vol 81. PMLR, New York, NY, pp 107–118, URL http://proceedings.mlr.press/v81/menon18a.html
2018
Later among the works it cites.
Zhang BH, Lemoine B, Mitchell M (2018) Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. ACM, pp 335–340, doi:10.1145/3278721.3278779
2018
Later among the works it cites.
Friedler SA, Scheidegger C, Venkatasubramanian S, et al (2019) A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency - FAT* ’19. ACM Press, pp 329–338, doi:10.1145/3287560.3287589, URL http://dl.acm.org/citation.cfm?doid=3287560.3287589
2019
Later among the works it cites.
Kim MP, Ghorbani A, Zou J (2019) Multiaccuracy: Black-box post-processing for fairness in classification. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. Association for Computing Machinery, New York, NY, USA, AIES ’19, p 247–254, doi:10.1145/3306618.3314287
2019
Later among the works it cites.
Mishler A (2019) Modeling Risk and Achieving Algorithmic Fairness Using Potential Outcomes. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society - AIES ’19. ACM Press, Honolulu, HI, pp 555–556, doi:10.1145/3306618.3314323
2019
Later among the works it cites.
Nabi R, Malinsky D, Shpitser I (2019) Learning optimal fair policies. In: Chaudhuri K, Salakhutdinov R (eds) Proceedings of the 36th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol 97. PMLR, pp 4674–4682, URL http://proceedings.mlr.press/v97/nabi19a.html
2019
Later among the works it cites.
Obermeyer Z, Powers B, Vogeli C, et al (2019) Dissecting racial bias in an algorithm used to manage the health of populations. Science 366(6464):447–453. doi:10.1126/science.aax2342
2019
Later among the works it cites.
Zhao H, Gordon G (2019) Inherent tradeoffs in learning fair representations. In: Wallach H, Larochelle H, Beygelzimer A, et al (eds) Advances in Neural Information Processing Systems, vol 32. Curran Associates, Inc., URL https://proceedings.neurips.cc/paper/2019/file/b4189d9de0fb2b9cce090bd1a15e3420-Paper.pdf
2019
Later among the works it cites.
Bonvini M, Kennedy EH (2021) Sensitivity analysis via the proportion of unmeasured confounding. Journal of the American Statistical Association pp 1–11. doi:10.1080/01621459.2020.1864382
2020
Later among the works it cites.
2020
Later among the works it cites.
Coston A, Mishler A, Kennedy EH, et al (2020) Counterfactual risk assessments, evaluation, and fairness. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, New York, NY, USA, FAT* ’20, p 582–593, doi:10.1145/3351095.3372851
2020
Later among the works it cites.
Dutta S, Wei D, Yueksel H, et al (2020) Is there a trade-off between fairness and accuracy? A perspective using mismatched hypothesis testing. In: III HD, Singh A (eds) Proceedings of the 37th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol 119. PMLR, pp 2803–2813, URL https://proceedings.mlr.press/v119/dutta20a.html
2020
Later among the works it cites.
Kim JS, Chen J, Talwalkar A (2020) FACT: A diagnostic for group fairness trade-offs. In: III HD, Singh A (eds) Proceedings of the 37th International Conference on Machine Learning, Proceedings of Machine Learning Research, vol 119. PMLR, pp 5264–5274, URL https://proceedings.mlr.press/v119/kim20a.html
2020
Later among the works it cites.
Rudin C, Wang C, Coker B (2020) The age of secrecy and unfairness in recidivism prediction. Harvard Data Science Review 2(1). doi:10.1162/99608f92.6ed64b30
2020
Later among the works it cites.
2021
Closest in time.
Mishler A, Kennedy EH, Chouldechova A (2021) Fairness in risk assessment instruments: Post-processing to achieve counterfactual equalized odds. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. ACM, pp 386–400, doi:10.1145/3442188.3445902
2021
Closest in time.
Kennedy EH, Balakrishnan S, G’Sell M (2020) Sharp instruments for classifying compliers and generalizing causal effects. Annals of Statistics 48(4):2008–2030. doi:10.1214/19-AOS1874
2030
Closest in time.
Zhang J, Bareinboim E (2018) Fairness in decision-making – the causal explanation formula. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence, pp 2037–2045, URL https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16949/15911
2045
Closest in time.