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Individual Fairness in Pipelines

Authors Cynthia Dwork, Christina Ilvento, Meena Jagadeesan

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Author Details

Cynthia Dwork
  • Harvard John A Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA
  • Radcliffe Institute for Advanced Study, Cambridge, MA, USA
  • Microsoft Research, Mountain View, CA, USA
Christina Ilvento
  • Harvard John A Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA
Meena Jagadeesan
  • Harvard University, Cambridge, MA, USA

Funding

  • Dwork, Cynthia: Research supported in part by NSF grant CCF-1763665 and Microsoft Research.
  • Ilvento, Christina: This work was funded by the Sloan Foundation.

Cite AsGet BibTex

Cynthia Dwork, Christina Ilvento, and Meena Jagadeesan. Individual Fairness in Pipelines. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 7:1-7:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.FORC.2020.7

Abstract

It is well understood that a system built from individually fair components may not itself be individually fair. In this work, we investigate individual fairness under pipeline composition. Pipelines differ from ordinary sequential or repeated composition in that individuals may drop out at any stage, and classification in subsequent stages may depend on the remaining "cohort" of individuals. As an example, a company might hire a team for a new project and at a later point promote the highest performer on the team. Unlike other repeated classification settings, where the degree of unfairness degrades gracefully over multiple fair steps, the degree of unfairness in pipelines can be arbitrary, even in a pipeline with just two stages. Guided by a panoply of real-world examples, we provide a rigorous framework for evaluating different types of fairness guarantees for pipelines. We show that naïve auditing is unable to uncover systematic unfairness and that, in order to ensure fairness, some form of dependence must exist between the design of algorithms at different stages in the pipeline. Finally, we provide constructions that permit flexibility at later stages, meaning that there is no need to lock in the entire pipeline at the time that the early stage is constructed.

Subject Classification

ACM Subject Classification
  • Theory of computation → Machine learning theory
Keywords
  • algorithmic fairness
  • fairness under composition
  • pipelines

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References

  1. Amanda Bower, Sarah N. Kitchen, Laura Niss, Martin J. Strauss, Alexander Vargas, and Suresh Venkatasubramanian. Fair pipelines. CoRR, abs/1707.00391, 2017. URL: http://arxiv.org/abs/1707.00391.
  2. L. Elisa Celis, Anay Mehrotra, and Nisheeth K. Vishnoi. Toward controlling discrimination in online ad auctions. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, pages 4456-4465, 2019. Google Scholar
  3. Alexandra Chouldechova. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data, 5(2):153-163, 2017. Google Scholar
  4. Amit Datta, Michael Carl Tschantz, and Anupam Datta. Automated experiments on ad privacy settings. Proceedings on Privacy Enhancing Technologies, 2015(1):92-112, 2015. Google Scholar
  5. Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard S. Zemel. Fairness through awareness. In Innovations in Theoretical Computer Science 2012, Cambridge, MA, USA, January 8-10, 2012, pages 214-226, 2012. Google Scholar
  6. Cynthia Dwork and Christina Ilvento. Fairness under composition. In 10th Innovations in Theoretical Computer Science Conference, ITCS 2019, January 10-12, 2019, San Diego, California, USA, pages 33:1-33:20, 2019. Google Scholar
  7. Cynthia Dwork, Michael Kim, Omer Reingold, Guy Rothblum, and Gal Yona. Learning from outcomes: Evidence-consistent rankings. In 60th Annual IEEE Symposium on Foundations of Computer Science November 9-12, 2019, Baltimore, Maryland, pages 106-125, 2019. Google Scholar
  8. Danielle Ensign, Sorelle A. Friedler, Scott Neville, Carlos Scheidegger, and Suresh Venkatasubramanian. Runaway feedback loops in predictive policing. In Conference on Fairness, Accountability and Transparency, FAT 2018, 23-24 February 2018, New York, NY, USA, pages 160-171, 2018. Google Scholar
  9. Stephen Gillen, Christopher Jung, Michael J. Kearns, and Aaron Roth. Online learning with an unknown fairness metric. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada, pages 2605-2614, 2018. Google Scholar
  10. Moritz Hardt, Eric Price, Nati Srebro, et al. Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems, pages 3315-3323, 2016. Google Scholar
  11. Úrsula Hébert-Johnson, Michael P. Kim, Omer Reingold, and Guy N. Rothblum. Multicalibration: Calibration for the (computationally-identifiable) masses. In Jennifer G. Dy and Andreas Krause, editors, 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 1944-1953. PMLR, 2018. Google Scholar
  12. Lily Hu and Yiling Chen. Fairness at equilibrium in the labor market. CoRR, abs/1707.01590, 2017. URL: http://arxiv.org/abs/1707.01590.
  13. Christina Ilvento, Meena Jagadeesan, and Shuchi Chawla. Multi-category fairness in sponsored search auctions. In FAT* '20: Conference on Fairness, Accountability, and Transparency, Barcelona, Spain, January 27-30, 2020, pages 348-358, 2020. Google Scholar
  14. Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Roth, Logan Stapleton, and Zhiwei Steven Wu. Eliciting and enforcing subjective individual fairness. CoRR, abs/1905.10660, 2019. URL: http://arxiv.org/abs/1905.10660.
  15. Michael J. Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. Preventing fairness gerrymandering: Auditing and learning for subgroup fairness. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, pages 2569-2577, 2018. Google Scholar
  16. Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. Avoiding discrimination through causal reasoning. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017, Long Beach, CA, USA, pages 656-666, 2017. Google Scholar
  17. Michael P. Kim, Omer Reingold, and Guy N. Rothblum. Fairness through computationally-bounded awareness. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada, pages 4847-4857, 2018. Google Scholar
  18. Jon M. Kleinberg, Sendhil Mullainathan, and Manish Raghavan. Inherent trade-offs in the fair determination of risk scores. In 8th Innovations in Theoretical Computer Science Conference, ITCS 2017, January 9-11, 2017, Berkeley, CA, USA, pages 43:1-43:23, 2017. Google Scholar
  19. Anja Lambrecht and Catherine Tucker. Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of STEM career ads. Management Science, 65(7):2966-2981, 2019. Google Scholar
  20. Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt. Delayed impact of fair machine learning. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 6196-6200, 2019. Google Scholar
  21. Kristian Lum and William Isaac. To predict and serve? Significance, 13(5):14-19, 2016. Google Scholar
  22. David Madras, Elliot Creager, Toniann Pitassi, and Richard S. Zemel. Learning adversarially fair and transferable representations. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, pages 3381-3390, 2018. Google Scholar
  23. Ya'acov Ritov, Yuekai Sun, and Ruofei Zhao. On conditional parity as a notion of non-discrimination in machine learning. arXiv preprint, 2017. URL: http://arxiv.org/abs/1706.08519.
  24. Gal Yona and Guy N. Rothblum. Probably approximately metric-fair learning. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, pages 5666-5674, 2018. Google Scholar
  25. Richard S. Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork. Learning fair representations. In Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013, pages 325-333, 2013. Google Scholar
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