iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making

2019 IEEE 35th International Conference on Data Engineering (ICDE), Volume abs/1806.01059, 2019.

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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 alte...More

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