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Accuracy and fairness trade-offs in machine learning: a stochastic multi-objective approach

Author

Listed:
  • Suyun Liu

    (Lehigh University)

  • Luis Nunes Vicente

    (Lehigh University)

Abstract

In the application of machine learning to real-life decision-making systems, e.g., credit scoring and criminal justice, the prediction outcomes might discriminate against people with sensitive attributes, leading to unfairness. The commonly used strategy in fair machine learning is to include fairness as a constraint or a penalization term in the minimization of the prediction loss, which ultimately limits the information given to decision-makers. In this paper, we introduce a new approach to handle fairness by formulating a stochastic multi-objective optimization problem for which the corresponding Pareto fronts uniquely and comprehensively define the accuracy-fairness trade-offs. We have then applied a stochastic approximation-type method to efficiently obtain well-spread and accurate Pareto fronts, and by doing so we can handle training data arriving in a streaming way.

Suggested Citation

  • Suyun Liu & Luis Nunes Vicente, 2022. "Accuracy and fairness trade-offs in machine learning: a stochastic multi-objective approach," Computational Management Science, Springer, vol. 19(3), pages 513-537, July.
    • Handle: RePEc:spr:comgts:v:19:y:2022:i:3:d:10.1007_s10287-022-00425-z
    DOI: 10.1007/s10287-022-00425-z
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    References listed on IDEAS

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    1. Stamatios-Aggelos N. Alexandropoulos & Christos K. Aridas & Sotiris B. Kotsiantis & Michael N. Vrahatis, 2019. "Multi-Objective Evolutionary Optimization Algorithms for Machine Learning: A Recent Survey," Springer Optimization and Its Applications, in: Ioannis C. Demetriou & Panos M. Pardalos (ed.), Approximation and Optimization, pages 35-55, Springer.
    2. Mercier, Quentin & Poirion, Fabrice & Désidéri, Jean-Antoine, 2018. "A stochastic multiple gradient descent algorithm," European Journal of Operational Research, Elsevier, vol. 271(3), pages 808-817.
    3. Jörg Fliege & Benar Fux Svaiter, 2000. "Steepest descent methods for multicriteria optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 51(3), pages 479-494, August.
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