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People are rated and ranked, towards algorithmic decision making in anincreasing number of applications, typically based on machine learning.Research on how to incorporate fairness into such tasks has prevalently pursuedthe paradigm of group fairness: giving adequate success rates to specificallyprotected groups. In contrast, the alternative paradigm of individual fairnesshas received relatively little attention, and this paper advances this lessexplored direction. The paper introduces a method for probabilistically mappinguser records into a low-rank representation that reconciles individual fairnessand the utility of classifiers and rankings in downstream applications. Ournotion of individual fairness requires that users who are similar in alltask-relevant attributes such as job qualification, and disregarding allpotentially discriminating attributes such as gender, should have similaroutcomes. We demonstrate the versatility of our method by applying it toclassification and learning-to-rank tasks on a variety of real-world datasets.Our experiments show substantial improvements over the best prior work for thissetting.

iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making