  As multi-task models gain popularity in a wider range of machine learning
applications, it is becoming increasingly important for practitioners to
understand the fairness implications associated with those models. Most
existing fairness literature focuses on learning a single task more fairly,
while how ML fairness interacts with multiple tasks in the joint learning
setting is largely under-explored. In this paper, we are concerned with how
group fairness (e.g., equal opportunity, equalized odds) as an ML fairness
concept plays out in the multi-task scenario. In multi-task learning, several
tasks are learned jointly to exploit task correlations for a more efficient
inductive transfer. This presents a multi-dimensional Pareto frontier on (1)
the trade-off between group fairness and accuracy with respect to each task, as
well as (2) the trade-offs across multiple tasks. We aim to provide a deeper
understanding on how group fairness interacts with accuracy in multi-task
learning, and we show that traditional approaches that mainly focus on
optimizing the Pareto frontier of multi-task accuracy might not perform well on
fairness goals. We propose a new set of metrics to better capture the
multi-dimensional Pareto frontier of fairness-accuracy trade-offs uniquely
presented in a multi-task learning setting. We further propose a
Multi-Task-Aware Fairness (MTA-F) approach to improve fairness in multi-task
learning. Experiments on several real-world datasets demonstrate the
effectiveness of our proposed approach.
