iFair: Learning Individually Fair Data Representations for Algorithmic Decision Making
This work addresses fairness for individuals in machine learning applications, advancing a less explored direction compared to group fairness, but it is incremental as it builds on existing individual fairness concepts.
The paper tackles the problem of ensuring individual fairness in algorithmic decision making by introducing a method to map user records into low-rank representations that balance fairness and utility. Experiments on real-world datasets show substantial improvements over prior work in classification and ranking tasks.
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 alternative paradigm of individual fairness has received relatively little attention, and this paper advances this less explored direction. The paper introduces a method for probabilistically mapping user records into a low-rank representation that reconciles individual fairness and the utility of classifiers and rankings in downstream applications. Our notion of individual fairness requires that users who are similar in all task-relevant attributes such as job qualification, and disregarding all potentially discriminating attributes such as gender, should have similar outcomes. We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on a variety of real-world datasets. Our experiments show substantial improvements over the best prior work for this setting.