Is It Legitimate Statistics or Is It Sexism: Why Discrimination Is Not Rational

  • Martha Osegueda Escobar
  • Vladik Kreinovich
  • Thach N. Nguyen
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 760)

Abstract

While in the ideal world, everyone should have the same chance to succeed in a given profession, in reality, often the probability of success is different for people of different gender and/or ethnicity. For example, in the US, the probability of a female undergraduate student in computer science to get a PhD is lower than a similar probability for a male student. At first glance, it may seem that in such a situation, if we try to maximize our gain and we have a limited amount of resources, it is reasonable to concentrate on students with the higher probability of success – i.e., on males, and only moral considerations prevent us from pursuing this seemingly economically optimal discriminatory strategy. In this paper, we show that this first impression is wrong: the discriminatory strategy is not only morally wrong, it is also not optimal – and the morally preferable inclusive strategy is actually also economically better.

Notes

Acknowledgments

This work was supported in part by the National Science Foundation grant HRD-1242122 (Cyber-ShARE Center of Excellence).

References

  1. 1.
    Carmichael, L., Stalla-Bourdillon, S., Taab, S.: Data mining and automated discrimination: a mixed legal/technical perspective. IEEE Intell. Syst. 31(6), 51–55 (2016)CrossRefGoogle Scholar
  2. 2.
    Saichev, A.I., Malevergne, Y., Sornette, D.: Theory of Zipf’s Law and Beyond. Springer, Heidelberg (2010)CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Martha Osegueda Escobar
    • 1
  • Vladik Kreinovich
    • 1
  • Thach N. Nguyen
    • 2
  1. 1.Department of Computer ScienceUniversity of Texas at El PasoEl PasoUSA
  2. 2.Banking University of Ho Chi Minh CityHo Chi Minh CityVietnam

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