Skip to main content
SpringerLink
Log in

Fairness: A Formal-Methods Perspective

  • Conference paper
  • First Online:
Static Analysis (SAS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11002))

Included in the following conference series:

  • 1058 Accesses

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    World events at the time of writing strongly influenced my choice of example.

References

  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. http://doi.acm.org/10.1145/3133904

  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). https://doi.org/10.1007/978-3-319-63387-9_9

    Chapter  Google Scholar 

  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). http://doi.acm.org/10.1145/3158146

  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. Dwork, C., Roth, A.: The Algorithmic Foundations of Differential Privacy, vol. 9. Now Publishers, Inc., Hanover (2014)

    MATH  Google Scholar 

  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). http://doi.acm.org/10.1145/2783258.2783311

  7. Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. CoRR abs/1610.02413 (2016). http://arxiv.org/abs/1610.02413

  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. Kleinberg, J., Mullainathan, S., Raghavan, M.: Inherent trade-offs in the fair determination of risk scores. In: ITCS (2017)

    Google Scholar 

  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). http://arxiv.org/abs/1603.01699

  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. 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. 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). http://jmlr.org/proceedings/papers/v28/zemel13.html

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. University of Wisconsin–Madison, Madison, WI, USA

    Aws Albarghouthi

Authors
  1. Aws Albarghouthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aws Albarghouthi .

Editor information

Editors and Affiliations

  1. Universität Freiburg, Freiburg, Germany

    Andreas Podelski

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Albarghouthi, A. (2018). Fairness: A Formal-Methods Perspective. In: Podelski, A. (eds) Static Analysis. SAS 2018. Lecture Notes in Computer Science(), vol 11002. Springer, Cham. https://doi.org/10.1007/978-3-319-99725-4_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99725-4_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99724-7

  • Online ISBN: 978-3-319-99725-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation