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Fair Supervised Learning with A Simple Random Sampler of Sensitive Attributes (2311.05866v2)

Published 10 Nov 2023 in stat.ML and cs.LG

Abstract: As the data-driven decision process becomes dominating for industrial applications, fairness-aware machine learning arouses great attention in various areas. This work proposes fairness penalties learned by neural networks with a simple random sampler of sensitive attributes for non-discriminatory supervised learning. In contrast to many existing works that critically rely on the discreteness of sensitive attributes and response variables, the proposed penalty is able to handle versatile formats of the sensitive attributes, so it is more extensively applicable in practice than many existing algorithms. This penalty enables us to build a computationally efficient group-level in-processing fairness-aware training framework. Empirical evidence shows that our framework enjoys better utility and fairness measures on popular benchmark data sets than competing methods. We also theoretically characterize estimation errors and loss of utility of the proposed neural-penalized risk minimization problem.

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References (57)
  1. One-network adversarial fairness. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pages 2412–2420. AAAI Press.
  2. A reductions approach to fair classification. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, pages 60–69. PMLR.
  3. Fair regression: Quantitative definitions and reduction-based algorithms. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 120–129. PMLR.
  4. Wasserstein gan.
  5. Do gans learn the distribution? some theory and empirics. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net.
  6. Fairness in machine learning. Nips tutorial, 1:2017.
  7. Data decisions and theoretical implications when adversarially learning fair representations. CoRR, abs/1707.00075.
  8. Fairness in machine learning: A survey. CoRR, abs/2010.04053.
  9. A fair classifier using kernel density estimation. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual.
  10. Chouldechova, A. (2017). Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2):153–163.
  11. Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals. J. Mach. Learn. Res., 20:172:1–172:59.
  12. Fairness measures for machine learning in finance. The Journal of Financial Data Science.
  13. Algorithmic fairness in business analytics: Directions for research and practice. CoRR, abs/2207.10991.
  14. Reinforcement learning with stepwise fairness constraints. In International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain, volume 206 of Proceedings of Machine Learning Research, pages 10594–10618. PMLR.
  15. Fairness via representation neutralization. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 12091–12103.
  16. Censoring representations with an adversary. In 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings.
  17. A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Nat. Comput., 17(3):585–609.
  18. Generative adversarial networks.
  19. Fairness-aware neural rényi minimization for continuous features. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pages 2262–2268. ijcai.org.
  20. Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics. J. Mach. Learn. Res., 13:307–361.
  21. Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 3315–3323.
  22. Understanding estimation and generalization error of generative adversarial networks. IEEE Trans. Inf. Theory, 67(5):3114–3129.
  23. Re-weighting based group fairness regularization via classwise robust optimization. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net.
  24. Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst., 33(1):1–33.
  25. Nonconvex optimization for regression with fairness constraints. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, pages 2742–2751. PMLR.
  26. Tabddpm: Modelling tabular data with diffusion models. In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, volume 202 of Proceedings of Machine Learning Research, pages 17564–17579. PMLR.
  27. Fairness in credit scoring: Assessment, implementation and profit implications. Eur. J. Oper. Res., 297(3):1083–1094.
  28. A maximal correlation approach to imposing fairness in machine learning. In IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Virtual and Singapore, 23-27 May 2022, pages 3523–3527. IEEE.
  29. Algorithmic fairness in mortgage lending: from absolute conditions to relational trade-offs. Minds Mach., 31(1):165–191.
  30. Estimating and improving fairness with adversarial learning. CoRR, abs/2103.04243.
  31. The many dimensions of algorithmic fairness in educational applications. In Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications, BEA@ACL 2019, Florence, Italy, August 2, 2019, pages 1–10. Association for Computational Linguistics.
  32. Fair conformal predictors for applications in medical imaging. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pages 12008–12016. AAAI Press.
  33. Fairness-aware learning for continuous attributes and treatments. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 4382–4391. PMLR.
  34. Machine learning and algorithmic fairness in public and population health. Nat. Mach. Intell., 3(8):659–666.
  35. Conditional generative adversarial nets. CoRR, abs/1411.1784.
  36. Spectral normalization for generative adversarial networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net.
  37. f-gan: Training generative neural samplers using variational divergence minimization. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, pages 271–279.
  38. Bias and unfairness in machine learning models: A systematic review on datasets, tools, fairness metrics, and identification and mitigation methods. Big data and cognitive computing, 7(1):15.
  39. On fairness and calibration. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 5680–5689.
  40. Recycling privileged learning and distribution matching for fairness. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 677–688.
  41. Fast feature selection with fairness constraints. In International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain, volume 206 of Proceedings of Machine Learning Research, pages 7800–7823. PMLR.
  42. Achieving equalized odds by resampling sensitive attributes. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual.
  43. Fairness GAN: generating datasets with fairness properties using a generative adversarial network. IBM J. Res. Dev., 63(4/5):3:1–3:9.
  44. Achieving fairness with a simple ridge penalty. Stat. Comput., 32(5):77.
  45. DECAF: generating fair synthetic data using causally-aware generative networks. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 22221–22233.
  46. Scalable spectral clustering with group fairness constraints. In International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain, volume 206 of Proceedings of Machine Learning Research, pages 6613–6629. PMLR.
  47. Fair and optimal classification via post-processing. In International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, volume 202 of Proceedings of Machine Learning Research, pages 37977–38012. PMLR.
  48. Controllable invariance through adversarial feature learning. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 585–596.
  49. Achieving causal fairness through generative adversarial networks. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 1452–1458. ijcai.org.
  50. Fairgan: Fairness-aware generative adversarial networks. In IEEE International Conference on Big Data (IEEE BigData 2018), Seattle, WA, USA, December 10-13, 2018, pages 570–575. IEEE.
  51. Algorithmic fairness in computational medicine. EBioMedicine, 84:104250.
  52. Modeling tabular data using conditional GAN. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, pages 7333–7343.
  53. Fairness constraints: A flexible approach for fair classification. J. Mach. Learn. Res., 20:75:1–75:42.
  54. Bayes-optimal classifiers under group fairness. CoRR, abs/2202.09724.
  55. Mitigating unwanted biases with adversarial learning. In Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, AIES 2018, New Orleans, LA, USA, February 02-03, 2018, pages 335–340. ACM.
  56. Conditional learning of fair representations. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net.
  57. Lipschitz generative adversarial nets. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 7584–7593. PMLR.
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