Model-based and actual independence for fairness-aware classification

@article{Kamishima2017ModelbasedAA,
  title={Model-based and actual independence for fairness-aware classification},
  author={Toshihiro Kamishima and Shotaro Akaho and Hideki Asoh and Jun Sakuma},
  journal={Data Mining and Knowledge Discovery},
  year={2017},
  volume={32},
  pages={258-286}
}
The goal of fairness-aware classification is to categorize data while taking into account potential issues of fairness, discrimination, neutrality, and/or independence. For example, when applying data mining technologies to university admissions, admission criteria must be non-discriminatory and fair with regard to sensitive features, such as gender or race. In this context, such fairness can be formalized as statistical independence between classification results and sensitive features. The… CONTINUE READING
Recent Discussions
This paper has been referenced on Twitter 5 times over the past 90 days. VIEW TWEETS

Citations

Publications citing this paper.

References

Publications referenced by this paper.
Showing 1-10 of 25 references

Fairness constraints: Amechanism for fair classification. In: ICML2015Workshop: Fairness, Accountability, and Transparency inMachine Learning

  • ZafarMB, IV Martinez, RodriguezMG, K Gummadi
  • 2015

Similar Papers

Loading similar papers…