Fairness: A Formal-Methods Perspective

  • Aws AlbarghouthiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11002)


Sensitive decisions of large-scale societal impact are increasingly being delegated to opaque software—a trend that is unlikely to slow down in the near future. The issue of fairness and bias of decision-making algorithms has thus become a multifaceted, interdisciplinary concern, attracting the attention of computer scientists, law scholars, policy makers, journalists, and many others. In this expository paper, I will outline some of the research questions we have been studying about fairness through the lens of formal methods.



The work described in this talk is in collaboration with a fantastic group of students and researchers: Samuel Drews, Aditya Nori, Loris D’Antoni, Justin Hsu, Calvin Smith, and David Merrell. The work described is generously supported by the National Science Foundation (NSF) grant #1704117.


  1. 1.
    Albarghouthi, A., D’Antoni, L., Drews, S., Nori, A.V.: Fairsquare: probabilistic verification of program fairness. In: Proceedings of the ACM on Programming Languages (OOPSLA), vol. 1, pp. 80:1–80:30, October 2017.
  2. 2.
    Albarghouthi, A., D’Antoni, L., Drews, S.: Repairing decision-making programs under uncertainty. In: Majumdar, R., Kunčak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 181–200. Springer, Cham (2017). Scholar
  3. 3.
    Albarghouthi, A., Hsu, J.: Synthesizing coupling proofs of differential privacy. In: Proceedings of the ACM on Programming Languages (POPL), vol. 2, pp. 58:1–58:30 (2018).
  4. 4.
    Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemel, R.S.: Fairness through awareness. In: Innovations in Theoretical Computer Science 2012, Cambridge, MA, USA, 8–10 January 2012, pp. 214–226 (2012)Google Scholar
  5. 5.
    Dwork, C., Roth, A.: The Algorithmic Foundations of Differential Privacy, vol. 9. Now Publishers, Inc., Hanover (2014)zbMATHGoogle Scholar
  6. 6.
    Feldman, M., Friedler, S.A., Moeller, J., Scheidegger, C., Venkatasubramanian, S.: Certifying and removing disparate impact. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 10–13 August 2015, pp. 259–268 (2015).
  7. 7.
    Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. CoRR abs/1610.02413 (2016).
  8. 8.
    Kearns, M., Roth, A., Wu, Z.S.: Meritocratic fairness for cross-population selection. In: International Conference on Machine Learning, pp. 1828–1836 (2017)Google Scholar
  9. 9.
    Kleinberg, J., Mullainathan, S., Raghavan, M.: Inherent trade-offs in the fair determination of risk scores. In: ITCS (2017)Google Scholar
  10. 10.
    Lyu, M., Su, D., Li, N.: Understanding the Sparse Vector Technique for differential privacy. In: Appeared at the International Conference on Very Large Data Bases (VLDB), Munich, Germany, vol. 10, pp. 637–648 (2017).
  11. 11.
    Merrell, D., Albarghouthi, A., DAntoni, L.: Weighted model integration with orthogonal transformations. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 4610–4616. AAAI Press (2017)Google Scholar
  12. 12.
    Pedreshi, D., Ruggieri, S., Turini, F.: Discrimination-aware data mining. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 560–568. ACM (2008)Google Scholar
  13. 13.
    Zemel, R.S., Wu, Y., Swersky, K., Pitassi, T., Dwork, C.: Learning fair representations. In: Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16–21 June 2013, pp. 325–333 (2013).

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of Wisconsin–MadisonMadisonUSA

Personalised recommendations