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 common setting of fine-tuning a pre-trained
model, where, following recent work (Gururangan et al., 2020; Dery et al.,
2023), 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 always, achieves better worst-group
accuracy than Just-Train-Twice (JTT; Liu et al. (2021)) -- 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 and find that our
regularized MTL approach consistently outperforms JTT on both worst and average
group outcomes. Our official code can be found here:
this https URL

为了创建能为多样用户提供服务的机器学习系统，需要不仅实现高的平均性能，还要确保在不同群组中公平的结果。本文研究了多任务学习在最差群组准确性和群组公平性挑战中的潜力，并提出了通过对多任务表示空间进行正则化改进的方法。实验结果表明，我们的正则化多任务学习方法在最差和平均群组结果上都优于现有方法。