Abstract
Dilemma between model accuracy and fairness in machine learning models has been shown theoretically and empirically. So far, dozens of fairness measures have been proposed, among which incompatibility and complementarity exist. However, no fairness measure has been universally accepted as the single fairest measure. No one has considered multiple fairness measures simultaneously. In this paper, we propose a multi-objective evolutionary learning framework for mitigating unfairness caused by considering a single measure only, in which a multi-objective evolutionary algorithm is used during training to balance accuracy and multiple fairness measures simultaneously. In our case study, besides the model accuracy, two fairness measures that are conflicting to each other are selected. Empirical results show that our proposed multi-objective evolutionary learning framework is able to find Pareto-front models efficiently and provide fairer machine learning models that consider multiple fairness measures.
This work was supported by the Research Institute of Trustworthy Autonomous Systems, the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (Grant No. 2017ZT07X386), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515011830), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531), the Shenzhen Fundamental Research Program (Grant Nos. JCYJ20180504165652917, JCYJ20190809121403553) and Huawei project on “Fundamental Theory and Key Technologies of Trustworthy Systems”.
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Caton, S., Haas, C.: Fairness in machine learning: a survey. arXiv preprint arXiv:2010.04053 (2020)
Chandra, A., Yao, X.: Ensemble learning using multi-objective evolutionary algorithms. J. Math. Model. Algor. 5(4), 417–445 (2006)
Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data 5(2), 153–163 (2017)
Corbett-Davies, S., Goel, S.: The measure and mismeasure of fairness: a critical review of fair machine learning. arXiv preprint arXiv:1808.00023 (2018)
Cowell, F.A., Kuga, K.: Additivity and the entropy concept: an axiomatic approach to inequality measurement. J. Econ. Theory 25(1), 131–143 (1981)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214–226 (2012)
Friedler, S.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P., Roth, D.: A comparative study of fairness-enhancing interventions in machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 329–338 (2019)
Fujiyoshi, H., Hirakawa, T., Yamashita, T.: Deep learning-based image recognition for autonomous driving. IATSS Res. 43(4), 244–252 (2019)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256. JMLR Workshop and Conference Proceedings (2010)
Gong, Z., Chen, H., Yuan, B., Yao, X.: Multiobjective learning in the model space for time series classification. IEEE Trans. Cybern. 49(3), 918–932 (2018)
Heidari, H., Ferrari, C., Gummadi, K.P., Krause, A.: Fairness behind a veil of ignorance: a welfare analysis for automated decision making. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, pp. 1273–1283. Curran Associates Inc., Red Hook (2018)
Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods, vol. 751. John Wiley & Sons, Hoboken (2013)
Hutchinson, B., Mitchell, M.: 50 years of test (un) fairness: lessons for machine learning. In: Proceedings of the Conference on Fairness, Accountability, and Transparency, pp. 49–58 (2019)
Kamiran, F., Calders, T.: Classifying without discriminating. In: 2nd International Conference on Computer, Control and Communication, pp. 1–6. IEEE (2009)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic gradient descent. In: ICLR: International Conference on Learning Representations, pp. 1–15 (2015)
Kohavi, R., Becker, B.: UCI machine learning repository: The adult income data set (1998). https://archive.ics.uci.edu/ml/datasets/adult
Larson, J., Mattu, S., Kirchner, L., Angwin, J.: Data and analysis for “how we analyzed the compas recidivism algorithm” (2016). https://github.com/propublica/compas-analysis
Li, B., Li, J., Tang, K., Yao, X.: Many-objective evolutionary algorithms: a survey. ACM Comput. Surv. 48(1) (2015)
Li, M., Yao, X.: Quality evaluation of solution sets in multiobjective optimisation: a survey. ACM Comput. Surv. 52(2) (2019)
Liem, C.C.S., et al.: Psychology meets machine learning: interdisciplinary perspectives on algorithmic job candidate screening. In: Escalante, H.J., Escalera, S., Guyon, I., Baró, X., Güçlütürk, Y., Güçlü, U., van Gerven, M. (eds.) Explainable and Interpretable Models in Computer Vision and Machine Learning. TSSCML, pp. 197–253. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98131-4_9
Minku, L.L., Yao, X.: Software effort estimation as a multiobjective learning problem. ACM Trans. Softw. Eng. Methodol. 22(4) (2013)
Perrone, V., Donini, M., Zafar, M.B., Schmucker, R., Kenthapadi, K., Archambeau, C.: Fair bayesian optimization. In: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (2021)
Schott, J.R.: Fault tolerant design using single and multicriteria genetic algorithm optimization. Ph.D. thesis, Massachusetts Institute of Technology (1995)
Solow, A., Polasky, S., Broadus, J.: On the measurement of biological diversity. J. Environ. Econ. Manag. 24(1), 60–68 (1993)
Speicher, T., et al.: A unified approach to quantifying algorithmic unfairness: Measuring individual & group unfairness via inequality indices. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2239–2248 (2018)
Verma, S., Rubin, J.: Fairness definitions explained. In: 2018 IEEE/ACM International Workshop on Software Fairness (FairWare), pp. 1–7. IEEE (2018)
Wang, H., Jin, Y., Yao, X.: Diversity assessment in many-objective optimization. IEEE Trans. Cybern. 47(6), 1510–1522 (2017)
Wei, S., Niethammer, M.: The fairness-accuracy Pareto front. arXiv preprint arXiv:2008.10797 (2020)
Yao, X.: Evolving artificial neural networks. Proc. IEEE 87(9), 1423–1447 (1999)
Yao, X., Liu, Y.: A new evolutionary system for evolving artificial neural networks. IEEE Trans. Neural Netw. 8(3), 694–713 (1997)
Zhang, Q., Wu, F., Tao, Y., Pei, J., Liu, J., Yao, X.: D-MAENS2: a self-adaptive D-MAENS algorithm with better decision diversity. In: Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence, pp. 2754–2761. IEEE (2020)
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength pareto evolutionary algorithm. TIK-report 103 (2001)
Žliobaitė, I.: Measuring discrimination in algorithmic decision making. Data Mining Knowl. Disc. 31(4), 1060–1089 (2017). https://doi.org/10.1007/s10618-017-0506-1
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Zhang, Q., Liu, J., Zhang, Z., Wen, J., Mao, B., Yao, X. (2021). Fairer Machine Learning Through Multi-objective Evolutionary Learning. In: Farkaš, I., Masulli, P., Otte, S., Wermter, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2021. ICANN 2021. Lecture Notes in Computer Science(), vol 12894. Springer, Cham. https://doi.org/10.1007/978-3-030-86380-7_10
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