Causal inference for social discrimination reasoning
Qureshi B., Kamiran F., Karim A., Ruggieri S., Pedreschi D.
Computer Science - Computers and Society Social discrimination Artificial Intelligence Information Systems Hardware and Architecture Computer Networks and Communications Computers and Society (cs.CY) Fairness Propensity score Causal analysis FOS: Computer and information sciences Statistics - Applications Accountability and transparency Software Applications (stat.AP)
The discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis, a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms.
Source: Journal of intelligent information systems 54 (2020): 425–437. doi:10.1007/s10844-019-00580-x
Publisher: Kluwer Academic Publishers, Boston , Paesi Bassi
2. Marc Bendick. Situation testing for employment discrimination in the United States of America. Horizons Stratégiques, 3(5):17-39, 2007.
3. Francesco Bonchi, Sara Hajian, Bud Mishra, and Daniele Ramazzotti. Exposing the probabilistic causal structure of discrimination. arXiv preprint arXiv:1510.00552, 2015.
4. Alex Bryson, Richard Dorsett, and Susan Purdon. The use of propensity score matching in the evaluation of active labour market policies. Crown, 2002.
5. T. Calders and S. Verwer. Three naive bayes approaches for discrimination-free classification. Data Mining & Knowledge Discovery, 21(2):277-292, 2010.
6. Toon Calders, Asad Karim, Faisal Kamiran, Wesam Ali, and Xiangliang Zhang. Controlling attribute effect in linear regression. In Proceedings of 13th IEEE International Conference on Data Mining (ICDM), pages 71-80. IEEE, 2013.
7. Marco Caliendo and Sabine Kopeinig. Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1):31-72, 2008.
8. R. B. D'Agostino. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Statistics in Medicine, 17(19):2265-2281, 1998.
9. Michael Feldman, Sorelle A. Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian. Certifying and removing disparate impact. In Proceedings of the Int. Conference on Knowledge Discovery and Data Mining (KDD 2015), pages 259-268. ACM, 2015.
10. Sheila R. Foster. Causation in antidiscrimination law: Beyond intent versus impact. Houston Law Review, 41(5):1469-1548, 2004.
11. F. Kamiran and T. Calders. Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems, 33(1):1-33, 2012.
12. F. Kamiran, A. Karim, and X. Zhang. Decision theory for discrimination-aware classification. In Proc. of the Int. Conference on Data Mining (ICDM), pages 924-929. IEEE, 2012.
13. Faisal Kamiran, Toon Calders, and Mykola Pechenizkiy. Discrimination aware decision tree learning. In G. I. Webb, B. Liu, C. Zhang, D. Gunopulos, and X. Wu, editors, Proc. of the IEEE Int. Conf. on Data Mining (ICDM), pages 869-874. IEEE Computer Society, 2010.
14. T. Kamishima, S. Akaho, and J. Sakuma. Fairness-aware classifier with prejudice remover regularizer. In Proc. of the European Conference on Machine Learning and on Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), volume 7524 of LNCS, pages 35-50. Springer, 2012.
15. B.T. Luong, S. Ruggieri, and F. Turini. k-NN as an implementation of situation testing for discrimination discovery and prevention. In Proc. of the ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD), pages 502-510. ACM, 2011.
16. Koray Mancuhan and Chris Clifton. Combating discrimination using bayesian networks. Artificial Intelligence and Law, 22(2):211-238, 2014.
17. Daniel F McCaffrey, Greg Ridgeway, and Andrew R Morral. Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4):403, 2004.
18. Judea Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, New York, USA, 2 edition, 2009.
19. Greg Ridgeway. Assessing the effect of race bias in post-traffic stop outcomes using propensity scores. Journal of Quantitative Criminology, 22(1):1-29, 2006.
20. A. Romei and S. Ruggieri. A multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review, 29(5):582-638, 2014.
21. P. R. Rosenbaum and D. B. Rubin. The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1):41-55, 1983.
22. S. Ruggieri. Using t-closeness anonymity to control for non-discrimination. Transactions on Data Privacy, 7(2):99-129, 2014.
23. S. Ruggieri, D. Pedreschi, and F. Turini. Data mining for discrimination discovery. ACM Trans. on Knowledge Discovery from Data, 4(2):Article 9, 2010.
24. Campbell D. T Shadish W. R., Cook T. D. Experimental and quasi-experimental designs for generalized causal inference. Houghton-Mifflin, 2002.
25. Indre˙ Z˘liobaitye. A survey on measuring indirect discrimination in machine learning. arXiv preprint arXiv:1511.00148v1, 2015.
26. R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork. Learning fair representations. In Proc. of the 30st Int. Conf. on Machine Learning, ICML, 2013.
Metrics
Back to previous page
@article{oai:it.cnr:prodotti:422748,
title = {Causal inference for social discrimination reasoning},
author = {Qureshi B. and Kamiran F. and Karim A. and Ruggieri S. and Pedreschi D.},
publisher = {Kluwer Academic Publishers, Boston , Paesi Bassi},
doi = {10.1007/s10844-019-00580-x and 10.48550/arxiv.1608.03735},
journal = {Journal of intelligent information systems},
volume = {54},
pages = {425–437},
year = {2020}
}
0000-0003-4801-3225ISTI Repository
DOI
10.1007/s10844-019-00580-x
10.48550/arxiv.1608.03735
arXiv.org e-Print Archive
Journal of Intelligent Information Systems
link.springer.com