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Fair Feature Selection: A Comparison of Multi-Objective Genetic Algorithms (2310.02752v1)

Published 4 Oct 2023 in cs.LG

Abstract: Machine learning classifiers are widely used to make decisions with a major impact on people's lives (e.g. accepting or denying a loan, hiring decisions, etc). In such applications,the learned classifiers need to be both accurate and fair with respect to different groups of people, with different values of variables such as sex and race. This paper focuses on fair feature selection for classification, i.e. methods that select a feature subset aimed at maximising both the accuracy and the fairness of the predictions made by a classifier. More specifically, we compare two recently proposed Genetic Algorithms (GAs) for fair feature selection that are based on two different multi-objective optimisation approaches: (a) a Pareto dominance-based GA; and (b) a lexicographic optimisation-based GA, where maximising accuracy has higher priority than maximising fairness. Both GAs use the same measures of accuracy and fairness, allowing for a controlled comparison. As far as we know, this is the first comparison between the Pareto and lexicographic approaches for fair classification. The results show that, overall, the lexicographic GA outperformed the Pareto GA with respect to accuracy without degradation of the fairness of the learned classifiers. This is an important result because at present nearly all GAs for fair classification are based on the Pareto approach, so these results suggest a promising new direction for research in this area.

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References (27)
  1. A. Freitas, “Comprehensible classification models: a position paper,” ACM SIGKDD Explorations Newsletter, vol. 15, no. 1, pp. 1–10, 2014.
  2. N. Burkart and M. Huber, “A survey on the explainability of supervised machine learning,” Journal of Machine Learning Research, vol. 70, pp. 245–317, 2021.
  3. N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan, “A survey on bias and fairness in machine learning,” arXiv preprint arXiv:1908.09635, 2019.
  4. R. Binns, “Fairness in machine learning: lessons from political philosophy,” Journal of Machine Learning Research, vol. 81, pp. 1–11, 2018.
  5. B. van Giffen, D. Herhausen, and T. Fahse, “Overcoming the pitfalls and perils of algorithms: a classification of machine learning biases and mitigation methods,” Journal of Business Research, vol. 144, pp. 93–106, 2022.
  6. J. Angwin, J. Larson, S. Mattu, and L. Kirchner, “Machine bias: There’s software used across the country to predict future criminals. and it’s biased against blacks.” 2016. [Online]. Available: https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
  7. S. Verma and J. Rubin, “Fairness definitions explained,” in 2018 IEEE/ACM International Workshop on Software Fairness (FairWare).   IEEE, 2018, pp. 1–7.
  8. J. Kleinberg, S. Mullainathan, and M. Raghavan, “Inherent trade-offs in the fair determination of risk scores,” arXiv preprint arXiv:1609.05807, 2016.
  9. A. Chouldechova, “Fair prediction with disparate impact: A study of bias in recidivism prediction instruments,” Big data, vol. 5, no. 2, pp. 153–163, 2017.
  10. A. U. Rehman, A. Nadeem, and M. Z. Malik, “Fair feature subset selection using multiobjective genetic algorithm,” in Proceedings of the GECCO-22 Companion (Genetic and Evolutionary Computation Conference).   New York: ACM Press, 2022, pp. 360–363.
  11. J. Brookhouse and A. Freitas, “Fair feature selection with a lexicographic multi-objective genetic algorithm,” in Proceedings of PPSN 2022: Parallel Problem Solving from Nature - PPSN XVII, LNCS, vol. 13399.   Berlin: Springer International Publishing, 2022, pp. 151–163.
  12. T. Calders and S. Verwer, “Three naive bayes approaches for discrimination-free classification,” Data Mining and Knowledge Discovery, vol. 21, no. 2, pp. 277–292, 2010.
  13. R. Zemel, Y. Wu, K. Swersky, T. Pitassi, and C. Dwork, “Learning fair representations,” in International Conference on Machine Learning, 2013, pp. 325–333.
  14. N. Srinivas and K. Deb, “Muiltiobjective optimization using nondominated sorting in genetic algorithms,” Evolutionary computation, vol. 2, no. 3, pp. 221–248, 1994.
  15. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: Nsga-ii,” IEEE transactions on evolutionary computation, vol. 6, no. 2, pp. 182–197, 2002.
  16. A. Valdivia, J. Sanchez-Monedero, and J. Casillas, “How fair can we go in machine learning? assessing the boundaries of accuracy and fairness,” International Journal of Intelligent Systems, vol. 36, no. 4, pp. 1619–1643, 2021.
  17. S. Dandl, F. Pfisterer, and B. Bischl, “Multi-objective counterfactual fairness,” in Proc. of the GECCO’22 Companion (Genetic and Evolutionary Computation Conference).   New York: ACM Press, 2022, pp. 328–331.
  18. W. La Cava and J. Moore, “Genetic programming approaches to learning fair classifiers,” in Proc. Genetic and Evolutionary Computation Conference (GECCO-2020), 2020, pp. 967–975.
  19. K. A. Kaufman and R. S. Michalski, “Learning from inconsistent and noisy data: the aq18 approach,” in International Symposium on Methodologies for Intelligent Systems.   Springer, 1999, pp. 411–419.
  20. M. P. Basgalupp, R. C. Barros, A. C. de Carvalho, A. A. Freitas, and D. D. Ruiz, “Legal-tree: a lexicographic multi-objective genetic algorithm for decision tree induction,” in Proceedings of the 2009 ACM symposium on Applied Computing, 2009, pp. 1085–1090.
  21. S. Corbett-Davies and S. Goel, “The measure and mismeasure of fairness: A critical review of fair machine learning,” arXiv preprint arXiv:1808.00023, 2018.
  22. M. Hardt, E. Price, and N. Srebro, “Equality of opportunity in supervised learning,” in Advances in neural information processing systems, 2016, pp. 3315–3323.
  23. M. J. Kusner, J. Loftus, C. Russell, and R. Silva, “Counterfactual fairness,” in Advances in neural information processing systems, 2017, pp. 4066–4076.
  24. D. Dua and C. Graff, “UCI machine learning repository,” 2017. [Online]. Available: http://archive.ics.uci.edu/ml
  25. L. Breiman, “Random forests,” Machine learning, vol. 45, pp. 5–32, 2001.
  26. M. Fernandez-Delgado, E. Cernadas, S. Barro, and D. Amorin, “Do we need hundreds of classifiers to solve real-world classification problems?” Journal of Machine Learning Research, vol. 15, pp. 3133–3181, 2014.
  27. C. Zhang, C. Liu, X. Zhang, and G. Almpanidis, “An up-to-date comparison of state-of-the-art classification algorithms,” Expert Systems with Applications, no. 82, pp. 128–150, 2017.
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Authors (2)
  1. James Brookhouse (1 paper)
  2. Alex Freitas (1 paper)
Citations (2)
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