iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making

international conference on data engineering, pp. 1334-1345, 2019.

Cited by: 19|Bibtex|Views92|DOI:https://doi.org/10.1109/ICDE.2019.00121
Other Links: academic.microsoft.com|arxiv.org

Abstract:

People are rated and ranked, towards algorithmic decision making in an increasing number of applications, typically based on machine learning. Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: giving adequate success rates to specifically protected groups. In contrast, the al...More

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