Positive-Sum Fairness: Leveraging Demographic Attributes to Achieve Fair AI Outcomes Without Sacrificing Group Gains

TL;DR

This paper introduces positive-sum fairness in medical AI, allowing performance improvements that increase group disparities without harming individual subgroup fairness, by leveraging demographic attributes.

Contribution

It proposes the concept of positive-sum fairness, enabling the use of sensitive attributes to improve performance without sacrificing fairness, demonstrated through CNN models with race attributes.

Findings

Using demographic data can increase overall performance.

Removing demographic encodings reduces subgroup disparities.

Leveraging race attributes improves performance but widens disparities.

Abstract

Fairness in medical AI is increasingly recognized as a crucial aspect of healthcare delivery. While most of the prior work done on fairness emphasizes the importance of equal performance, we argue that decreases in fairness can be either harmful or non-harmful, depending on the type of change and how sensitive attributes are used. To this end, we introduce the notion of positive-sum fairness, which states that an increase in performance that results in a larger group disparity is acceptable as long as it does not come at the cost of individual subgroup performance. This allows sensitive attributes correlated with the disease to be used to increase performance without compromising on fairness. We illustrate this idea by comparing four CNN models that make different use of the race attribute in the training phase. The results show that removing all demographic encodings from the images helps close the gap in performance between the different subgroups, whereas leveraging the race attribute as a model's input increases the overall performance while widening the disparities between subgroups. These larger gaps are then put in perspective of the collective benefit through our notion of positive-sum fairness to distinguish harmful from non harmful disparities.