Utility-Fairness Trade-Offs and How to Find Them
Sepehr Dehdashtian Bashir Sadeghi Vishnu Naresh Boddeti
When building classification systems with demographic fairness
considerations, there are two objectives to satisfy: 1) maximizing utility for
the specific task and 2) ensuring fairness w.r.t. a known demographic
attribute. These objectives often compete, so optimizing both can lead to a
trade-off between utility and fairness. While existing works acknowledge the
trade-offs and study their limits, two questions remain unanswered: 1) What are
the optimal trade-offs between utility and fairness? and 2) How can we
numerically quantify these trade-offs from data for a desired prediction task
and demographic attribute of interest? This paper addresses these questions. We
introduce two utility-fairness trade-offs: the Data-Space and Label-Space
Trade-off. The trade-offs reveal three regions within the utility-fairness
plane, delineating what is fully and partially possible and impossible. We
propose U-FaTE, a method to numerically quantify the trade-offs for a given
prediction task and group fairness definition from data samples. Based on the
trade-offs, we introduce a new scheme for evaluating representations. An
extensive evaluation of fair representation learning methods and
representations from over 1000 pre-trained models revealed that most current
approaches are far from the estimated and achievable fairness-utility
trade-offs across multiple datasets and prediction tasks.