skip to main content
research-article
Free access

The (Im)possibility of fairness: different value systems require different mechanisms for fair decision making

Published: 22 March 2021 Publication History

Abstract

What does it mean to be fair?

References

[1]
Agarwal, A., Beygelzimer, A., Dudik, M., Langford, J., and Wallach, H. A reductions approach to fair classification. In Proceedings of the 35th Intern. Conf. on Machine Learning 80. J. Dy and A. Krause, (Eds.). PMLR, (Stockholmsmässan, Stockholm Sweden, 2018), 60--69; http://proceedings.mlr.press/v80/agarwal18a.html
[2]
Alexander, M. The New Jim Crow: Mass Incarceration in the Age of Colorblindness. The New Press, 2012.
[3]
Almlund, M., Duckworth, A., Heckman, J., and Kautz, T. Personality psychology and economics. Technical Report w16822. NBER Working Paper Series. National Bureau of Economic Research, Cambridge, MA, 2011.
[4]
Angwin, J., Larson, J., Mattu, S., and Kirchner, L. Machine bias. ProPublica (May 23, 2016).
[5]
Barocas, S. and Selbst, A. Big data's disparate impact. California Law Review 104, 671, (2016).
[6]
Calders, T. and Verwer, S. Three naïve Bayes approaches for discrimination-free classification. Data Min Knowl Disc 21 (2010), 277--292.
[7]
Chouldechova, A. Fair prediction with disparate impact: A study of bias in recidivism prediction instruments. Big Data 5, 2 (2017), 153--163.
[8]
Datta, A., Tschantz, M., and Datta, A. Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination. In Proceedings on Privacy Enhancing Technologies 1 (2015), 92 -- 112.
[9]
Duckworth, A., Peterson, C., Matthews, M., and Kelly, D. Grit: Perseverance and passion for long-term goals. J. Personality and Social Psychology 92, 6 (2007), 1087--1101.
[10]
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., and Zemel. R. Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conf. (2012), 214--226.
[11]
Feldman, M., Friedler, S., Moeller, J., Scheidegger, C., and Venkatasubramanian, S. Certifying and removing disparate impact. In Proceedings of the 21st ACM SIGKDD Intern. Conf. on Knowledge Discovery and Data Mining, (2015), 259--268.
[12]
Friedler, S., Scheidegger, C., and Venkatasubramanian, S. On the (im)possibility of fairness; arXiv:1609.07236 (2016).
[13]
Hardt, M., Price, E., and Srebro, N. Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems, 2016, 3315--3323.
[14]
Kamiran, F. and Calders, T. Classifying without discriminating. In Proceedings of the 2nd Intern. Conf. Computer, Control and Communication. IEEE, (2009), 1--6.
[15]
Kamiran, F., Karim, A., and Zhang, X. Decision theory for discrimination-aware classification. ICDM, (2012), 924--929.
[16]
Kamishima, T., Akaho, S., Asoh, H., and Sakuma, J. Fairness-aware classifier with prejudice remover regularizer. Machine Learning and Knowledge Discovery in Databases (2012), 35--50.
[17]
Kleinberg, J., Mullainathan, S., and Raghavan, M. Inherent trade-offs in the fair determination of risk scores. In Proceedings of Innovations in Theoretical Computer Science, (2017), 43:1--43:23.
[18]
Kozol, J. The Shame of the Nation: The Restoration of Apartheid Schooling in America. Broadway Books, 2006.
[19]
Madras, D., Creager, E., Pitassi, T., and Zemel, R. Learning adversarially fair and transferable representations. In Proceedings of the 35th Intern. Conf. on Machine Learning 80. J. Dy and A. Krause (Eds.), PMLR, (2018), 3384--3393; http://proceedings.mlr.press/v80/madras18a.html
[20]
Roemer, J. Equality of Opportunity. Harvard University Press, 1998.
[21]
Romei, A. and Ruggieri, S. A Multidisciplinary survey on discrimination analysis. The Knowledge Engineering Review (Apr. 3, 2013), 1--57.
[22]
Ruggieri, S. Using t-closeness anonymity to control for nondiscrimination. Transactions on Data Privacy 7 (2014), 99--129.
[23]
Yeom, S. and Tschantz, M. Discriminative but Not Discriminatory: A Comparison of Fairness Definitions under Different Worldviews. CoRR abs/1808.08619 (2018). arXiv:1808.08619 http://arxiv.org/abs/1808.08619
[24]
Zafar, M., Valera, I., Rodriguez, M., and Gummadi, K. Fairness beyond disparate treatment & disparate impact: Learning classification without disparate mistreatment. In Proceedings of WWW, (2017), 1171--1180.
[25]
Zafar, M., Valera, I., Rogriguez, M., and Gummadi, K. 2017. Fairness constraints: Mechanisms for fair classification. Artificial Intelligence and Statistics, (2017), 962--970.
[26]
Zemel, R., Wu, Y., Swersky, K., Pitassi, T., and Dwork, C. Learning fair representations. In Proceedings of ICML, (2013), 325--333.
[27]
Zliobaite, I. Measuring discrimination in algorithmic decision making. Data Mining and Knowledge Discovery 31, 4 (2017), 1060--1089.

Cited By

View all
  • (2025)Properties of Group Fairness Measures for RankingsACM Transactions on Social Computing10.1145/36748838:1-2(1-45)Online publication date: 17-Jan-2025
  • (2025)Triangular Trade-off between Robustness, Accuracy, and Fairness in Deep Neural Networks: A SurveyACM Computing Surveys10.1145/364508857:6(1-40)Online publication date: 10-Feb-2025
  • (2025)A Systematic Review of Fairness, Accountability, Transparency, and Ethics in Information RetrievalACM Computing Surveys10.1145/363721157:6(1-29)Online publication date: 10-Feb-2025
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image Communications of the ACM
Communications of the ACM  Volume 64, Issue 4
April 2021
164 pages
ISSN:0001-0782
EISSN:1557-7317
DOI:10.1145/3458337
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 March 2021
Published in CACM Volume 64, Issue 4

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Popular
  • Refereed

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)3,480
  • Downloads (Last 6 weeks)572
Reflects downloads up to 14 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2025)Properties of Group Fairness Measures for RankingsACM Transactions on Social Computing10.1145/36748838:1-2(1-45)Online publication date: 17-Jan-2025
  • (2025)Triangular Trade-off between Robustness, Accuracy, and Fairness in Deep Neural Networks: A SurveyACM Computing Surveys10.1145/364508857:6(1-40)Online publication date: 10-Feb-2025
  • (2025)A Systematic Review of Fairness, Accountability, Transparency, and Ethics in Information RetrievalACM Computing Surveys10.1145/363721157:6(1-29)Online publication date: 10-Feb-2025
  • (2025)Bias Amplification to Facilitate the Systematic Evaluation of Bias Mitigation MethodsIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2024.349194629:2(1444-1454)Online publication date: Feb-2025
  • (2025)Preference eigensystems for fair rankingExpert Systems with Applications10.1016/j.eswa.2024.126324269(126324)Online publication date: Apr-2025
  • (2025)Beyond incompatibility: Trade-offs between mutually exclusive fairness criteria in machine learning and lawArtificial Intelligence10.1016/j.artint.2024.104280340(104280)Online publication date: Mar-2025
  • (2025)Enhancing Fairness, Justice and Accuracy of Hybrid Human-AI Decisions by Shifting Epistemological StancesMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-74627-7_25(323-331)Online publication date: 1-Jan-2025
  • (2024)Investigating and Mitigating the Performance–Fairness Tradeoff via Protected-Category SamplingElectronics10.3390/electronics1315302413:15(3024)Online publication date: 31-Jul-2024
  • (2024)Sustainable artificial intelligence-driven classroom assessment in higher institutions: Lessons from Estonia, China, the USA, and Australia for NigeriaEuropean Journal of Interactive Multimedia and Education10.30935/ejimed/152655:2(e02403)Online publication date: 2024
  • (2024)AI and discriminative decisions in recruitment: Challenging the core assumptionsBig Data & Society10.1177/2053951724123587211:1Online publication date: 30-Mar-2024
  • Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Digital Edition

View this article in digital edition.

Digital Edition

Magazine Site

View this article on the magazine site (external)

Magazine Site

Login options

Full Access

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media