Understanding Instance-Level Impact of Fairness Constraints
This work addresses the need for instance-level fairness analysis in machine learning, which is incremental as it builds on existing influence function methods to explore fairness impacts.
The paper tackles the problem of understanding how fairness constraints affect individual training instances, rather than just groups, by developing a fairness influence function that decomposes into a kernelized combination of examples. The result shows that training on a subset of influential examples reduces fairness violations with a trade-off in accuracy, as demonstrated in experiments.
A variety of fairness constraints have been proposed in the literature to mitigate group-level statistical bias. Their impacts have been largely evaluated for different groups of populations corresponding to a set of sensitive attributes, such as race or gender. Nonetheless, the community has not observed sufficient explorations for how imposing fairness constraints fare at an instance level. Building on the concept of influence function, a measure that characterizes the impact of a training example on the target model and its predictive performance, this work studies the influence of training examples when fairness constraints are imposed. We find out that under certain assumptions, the influence function with respect to fairness constraints can be decomposed into a kernelized combination of training examples. One promising application of the proposed fairness influence function is to identify suspicious training examples that may cause model discrimination by ranking their influence scores. We demonstrate with extensive experiments that training on a subset of weighty data examples leads to lower fairness violations with a trade-off of accuracy.