Advertisement

The Invisible Power of Fairness. How Machine Learning Shapes Democracy

  • Elena BerettaEmail author
  • Antonio Santangelo
  • Bruno Lepri
  • Antonio Vetrò
  • Juan Carlos De Martin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11489)

Abstract

Many machine learning systems make extensive use of large amounts of data regarding human behaviors. Several researchers have found various discriminatory practices related to the use of human-related machine learning systems, for example in the field of criminal justice, credit scoring and advertising. Fair machine learning is therefore emerging as a new field of study to mitigate biases that are inadvertently incorporated into algorithms. Data scientists and computer engineers are making various efforts to provide definitions of fairness. In this paper, we provide an overview of the most widespread definitions of fairness in the field of machine learning, arguing that the ideas highlighting each formalization are closely related to different ideas of justice and to different interpretations of democracy embedded in our culture. This work intends to analyze the definitions of fairness that have been proposed to date to interpret the underlying criteria and to relate them to different ideas of democracy.

Keywords

Machine learning Fairness Equity Discrimination 

References

  1. 1.
    Barocas, S., Hardt, M., Narayanan, A.: Fairness and Machine Learning. fairmlbook.org (2018). http://www.fairmlbook.org
  2. 2.
    Berk, R., Heidari, H., Jabbari, S., Kearns, M., Roth, A.: Fairness in criminal justice risk assessments: the state of the art. Sociol. Methods Res. (2018)Google Scholar
  3. 3.
    Berliant, M., Thomson, W.: On the fair division of a heterogeneous commodity. J. Math. Econ. 21, 201–216 (1992)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Binns, R.: Fairness in machine learning: lessons from political philosophy. In: Friedler, S.A., Wilsonf, C. (eds.) Proceedings of Machine Learning Research, vol. 81, pp. 149–159 (2018)Google Scholar
  5. 5.
    Bobbio, N.: Eguaglianza e libertá. Einaudi, Torino, Italy (1995)Google Scholar
  6. 6.
    Bozdag, E., van den Hoven, J.: Breaking the filter bubble: democracy and design. Ethics Inf. Technol. 17, 249–265 (2015)CrossRefGoogle Scholar
  7. 7.
    Chouldechova, A.: Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data (2017)Google Scholar
  8. 8.
    Christiano, T.: Democracy. Stanford Encyclopedia of Philosophy (2006)Google Scholar
  9. 9.
    Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., Huq, A.: Algorithmic decision making and the cost of fairness. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 (2017)Google Scholar
  10. 10.
    Dieterich, W., Mendoza, C., Brennan, T.: Compas risk scales: demonstrating accuracy equity and predictive parity. Technical report, Northpointe Inc. (2016)Google Scholar
  11. 11.
    Dunn, J.: Western Political Theory in the Face of the Future, vol. 3. Cambridge University Press, Cambridge (1979)Google Scholar
  12. 12.
    Dwork, C., Hardt, M., Pitassi, T., Reingold, O., Zemeln, R.: Fairness through awareness. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 214–226. ACM (2012)Google Scholar
  13. 13.
    Gajane, P., Pechenizkiy, M.: On formalizing fairness in prediction with machine learning arXiv:1710.03184 (2018)
  14. 14.
    Habermas, J.: Between Facts and Norms: Contributions to a Discourse Theory of Law and Democracy. MIT Press, Cambridge (1998)Google Scholar
  15. 15.
    Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Advances in Neural Information Processing Systems (2016)Google Scholar
  16. 16.
    Held, D.: Models of Democracy. Stanford University Press, Palo Alto (2006)Google Scholar
  17. 17.
    Held, D.: The Democratic Paradox. Verso, London (2009)Google Scholar
  18. 18.
    Kleinberg, J., Mullainathan, S., Raghavan, M.: Inherent trade-offs in the fair determination of risk scores. In: Proceedings of Innovations in Theoretical Computer Science, ITCS 2017 (2017)Google Scholar
  19. 19.
    Kusner, M.J., Loftus, J.R., Russell, C., Silva, R.: Counterfactual fairness. In: Proceedings of 31st Neural Information Processing Systems, NIPS 2017 (2017)Google Scholar
  20. 20.
    O’Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown Publishing Group, New York (2016)zbMATHGoogle Scholar
  21. 21.
    Pasquale, F.: The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press, Cambridge (2015)CrossRefGoogle Scholar
  22. 22.
    Rawls, J.: A Theory of Justice. Harvard University Press, Harvard (1971)Google Scholar
  23. 23.
    Rawls, J.: The Idea of Public Reason. The MIT Press, Cambridge (1997)Google Scholar
  24. 24.
    Simoiu, C., Corbett-Davies, S., Goel, S.: The problem of infra-marginality in outcome tests for discrimination. Ann. Appl. Stat. 11, 1193–1216 (2017)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Sweeney, L.: Discrimination in online ad delivery. Queue Storage 11(3), pages 10 (2013)Google Scholar
  26. 26.
    Varian, H.: Equity, envy, and efficiency. J. Econ. Theory 9, 63–91 (1974)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Zafar, M.B., Valera, I., Rodriguez, M.G., Gummadi, K.P.: Fairness beyond disparate treatment and disparate impact: learning classification without disparate mistreatment. In: Proceedings of the 26th International Conference on World Wide Web, WWW 2017 (2017)Google Scholar
  28. 28.
    Zafar, M.B., Valera, I., Rodriguez, M.G., Gummadi, K.P., Weller, A.: From parity to preference-based notions of fairness in classification. In: Proceedings of the 31st Conference on Neural Information Processing Systems, NIPS 2017 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Nexa Center for Internet & Society, DAUINPolitecnico di TorinoTurinItaly
  2. 2.Future Urban Legacy LabPolitecnico di TorinoTurinItaly
  3. 3.Fondazione Bruno KesslerTrentoItaly

Personalised recommendations