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Distribution-Specific Auditing for Subgroup Fairness

Authors Daniel Hsu , Jizhou Huang , Brendan Juba

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LIPIcs.FORC.2024.5.pdf
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Author Details

Daniel Hsu
  • Columbia University, New York, NY, USA
Jizhou Huang
  • Washington University in St. Louis, MO, USA
Brendan Juba
  • Washington University in St. Louis, MO, USA

Funding

This work was partially supported by NSF awards IIS-2040971, IIS-1908287, and IIS-1942336, and NSF-Amazon award IIS-1939677.

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Daniel Hsu, Jizhou Huang, and Brendan Juba. Distribution-Specific Auditing for Subgroup Fairness. In 5th Symposium on Foundations of Responsible Computing (FORC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 295, pp. 5:1-5:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.FORC.2024.5

Abstract

We study the problem of auditing classifiers for statistical subgroup fairness. Kearns et al. [Kearns et al., 2018] showed that the problem of auditing combinatorial subgroups fairness is as hard as agnostic learning. Essentially all work on remedying statistical measures of discrimination against subgroups assumes access to an oracle for this problem, despite the fact that no efficient algorithms are known for it. If we assume the data distribution is Gaussian, or even merely log-concave, then a recent line of work has discovered efficient agnostic learning algorithms for halfspaces. Unfortunately, the reduction of Kearns et al. was formulated in terms of weak, "distribution-free" learning, and thus did not establish a connection for families such as log-concave distributions. In this work, we give positive and negative results on auditing for Gaussian distributions: On the positive side, we present an alternative approach to leverage these advances in agnostic learning and thereby obtain the first polynomial-time approximation scheme (PTAS) for auditing nontrivial combinatorial subgroup fairness: we show how to audit statistical notions of fairness over homogeneous halfspace subgroups when the features are Gaussian. On the negative side, we find that under cryptographic assumptions, no polynomial-time algorithm can guarantee any nontrivial auditing, even under Gaussian feature distributions, for general halfspace subgroups.

Subject Classification

ACM Subject Classification
  • Theory of computation → Machine learning theory
Keywords
  • Fairness auditing
  • agnostic learning
  • intractability

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References

  1. Alekh Agarwal, Alina Beygelzimer, Miroslav Dudík, John Langford, and Hanna Wallach. A reductions approach to fair classification. In International conference on machine learning, pages 60-69. PMLR, 2018. Google Scholar
  2. Özgür Akgün, Ian P Gent, Christopher Jefferson, Ian Miguel, and Peter Nightingale. Metamorphic testing of constraint solvers. In Principles and Practice of Constraint Programming: 24th International Conference, CP 2018, Lille, France, August 27-31, 2018, Proceedings 24, pages 727-736. Springer, 2018. Google Scholar
  3. Solon Barocas, Moritz Hardt, and Arvind Narayanan. Fairness and Machine Learning: Limitations and Opportunities. MIT Press, 2023. Google Scholar
  4. Bart Bogaerts, Stephan Gocht, Ciaran McCreesh, and Jakob Nordström. Certified dominance and symmetry breaking for combinatorial optimisation. Journal of Artificial Intelligence Research, 77:1539-1589, 2023. Google Scholar
  5. William Cook, Thorsten Koch, Daniel E Steffy, and Kati Wolter. A hybrid branch-and-bound approach for exact rational mixed-integer programming. Mathematical Programming Computation, 5(3):305-344, 2013. Google Scholar
  6. Kimberlé Crenshaw. Demarginalizing the intersection of race and sex: A black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. In Feminist legal theories, pages 23-51. Routledge, 2013. Google Scholar
  7. Ilias Diakonikolas, Daniel Kane, and Lisheng Ren. Near-optimal cryptographic hardness of agnostically learning halfspaces and relu regression under gaussian marginals. In International Conference on Machine Learning, pages 7922-7938. PMLR, 2023. Google Scholar
  8. Ilias Diakonikolas, Daniel M Kane, Vasilis Kontonis, Christos Tzamos, and Nikos Zarifis. Agnostic proper learning of halfspaces under gaussian marginals. In Conference on Learning Theory, pages 1522-1551. PMLR, 2021. Google Scholar
  9. Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, and Nikos Zarifis. Non-convex sgd learns halfspaces with adversarial label noise. Advances in Neural Information Processing Systems, 33:18540-18549, 2020. Google Scholar
  10. Ilias Diakonikolas, Vasilis Kontonis, Christos Tzamos, and Nikos Zarifis. Learning general halfspaces with adversarial label noise via online gradient descent. In International Conference on Machine Learning, pages 5118-5141. PMLR, 2022. Google Scholar
  11. Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. Fairness through awareness. In Proceedings of the 3rd innovations in theoretical computer science conference, pages 214-226, 2012. Google Scholar
  12. Vitaly Feldman, Parikshit Gopalan, Subhash Khot, and Ashok Kumar Ponnuswami. On agnostic learning of parities, monomials, and halfspaces. SIAM Journal on Computing, 39(2):606-645, 2009. Google Scholar
  13. Spencer Frei, Yuan Cao, and Quanquan Gu. Agnostic learning of halfspaces with gradient descent via soft margins. In International Conference on Machine Learning, pages 3417-3426. PMLR, 2021. Google Scholar
  14. Xavier Gillard, Pierre Schaus, and Yves Deville. Solvercheck: Declarative testing of constraints. In Principles and Practice of Constraint Programming: 25th International Conference, CP 2019, Stamford, CT, USA, September 30-October 4, 2019, Proceedings 25, pages 565-582. Springer, 2019. Google Scholar
  15. Aravind Gollakota, Adam R Klivans, and Pravesh K Kothari. A moment-matching approach to testable learning and a new characterization of rademacher complexity. In Proceedings of the 55th Annual ACM Symposium on Theory of Computing, pages 1657-1670, 2023. Google Scholar
  16. Aparna Gupte, Neekon Vafa, and Vinod Vaikuntanathan. Continuous lwe is as hard as lwe & applications to learning gaussian mixtures. In 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), pages 1162-1173. IEEE, 2022. Google Scholar
  17. David Haussler. Decision theoretic generalizations of the pac model for neural net and other learning applications. Information and computation, 100(1):78-150, 1992. Google Scholar
  18. Ursula Hébert-Johnson, Michael Kim, Omer Reingold, and Guy Rothblum. Multicalibration: Calibration for the (computationally-identifiable) masses. In International Conference on Machine Learning, pages 1939-1948. PMLR, 2018. Google Scholar
  19. Adam Tauman Kalai, Adam R Klivans, Yishay Mansour, and Rocco A Servedio. Agnostically learning halfspaces. SIAM Journal on Computing, 37(6):1777-1805, 2008. Google Scholar
  20. Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In International Conference on Machine Learning, pages 2564-2572, 2018. Google Scholar
  21. Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. An empirical study of rich subgroup fairness for machine learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency, pages 100-109, 2019. Google Scholar
  22. Michael J. Kearns, Robert E. Schapire, and Linda M. Sellie. Toward efficient agnostic learning. Machine Learning, 17:115-141, 1994. Google Scholar
  23. Michael Kim, Omer Reingold, and Guy Rothblum. Fairness through computationally-bounded awareness. Advances in Neural Information Processing Systems, 31, 2018. Google Scholar
  24. Michael P Kim, Amirata Ghorbani, and James Zou. Multiaccuracy: Black-box post-processing for fairness in classification. In Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, pages 247-254, 2019. Google Scholar
  25. Matt J Kusner, Joshua Loftus, Chris Russell, and Ricardo Silva. Counterfactual fairness. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. URL: https://proceedings.neurips.cc/paper_files/paper/2017/file/a486cd07e4ac3d270571622f4f316ec5-Paper.pdf.
  26. Oded Regev. On lattices, learning with errors, random linear codes, and cryptography. Journal of the ACM (JACM), 56(6):1-40, 2009. Google Scholar
  27. Ronitt Rubinfeld and Arsen Vasilyan. Testing distributional assumptions of learning algorithms. In Proceedings of the 55th Annual ACM Symposium on Theory of Computing, pages 1643-1656, 2023. Google Scholar
  28. Robert E Schapire. The strength of weak learnability. Machine learning, 5:197-227, 1990. Google Scholar
  29. Ji Wang, Ding Lu, Ian Davidson, and Zhaojun Bai. Scalable spectral clustering with group fairness constraints. In International Conference on Artificial Intelligence and Statistics, pages 6613-6629. PMLR, 2023. Google Scholar
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