  In order to create machine learning systems that serve a variety of users
well, it is vital to not only achieve high average performance but also ensure
equitable outcomes across diverse groups. However, most machine learning
methods are designed to improve a model's average performance on a chosen end
task without consideration for their impact on worst group error. Multitask
learning (MTL) is one such widely used technique. In this paper, we seek not
only to understand the impact of MTL on worst-group accuracy but also to
explore its potential as a tool to address the challenge of group-wise
fairness. We primarily consider the standard setting of fine-tuning a
pre-trained model, where, following recent work \citep{gururangan2020don,
dery2023aang}, we multitask the end task with the pre-training objective
constructed from the end task data itself. In settings with few or no group
annotations, we find that multitasking often, but not consistently, achieves
better worst-group accuracy than Just-Train-Twice (JTT;
\citet{pmlr-v139-liu21f}) -- a representative distributionally robust
optimization (DRO) method. Leveraging insights from synthetic data experiments,
we propose to modify standard MTL by regularizing the joint multitask
representation space. We run a large number of fine-tuning experiments across
computer vision and natural language processing datasets and find that our
regularized MTL approach \emph{consistently} outperforms JTT on both average
and worst-group outcomes. Our official code can be found here:
\href{https://github.com/atharvajk98/MTL-group-robustness.git}{\url{https://github.com/atharvajk98/MTL-group-robustness}}.
