The generalization capacity of Multi-Task Learning (MTL) becomes limited when
unrelated tasks negatively impact each other by updating shared parameters with
conflicting gradients, resulting in negative transfer and a reduction in MTL
accuracy compared to single-task learning (STL). Recently, there has been an
increasing focus on the fairness of MTL models, necessitating the optimization
of both accuracy and fairness for individual tasks. Similarly to how negative
transfer affects accuracy, task-specific fairness considerations can adversely
influence the fairness of other tasks when there is a conflict of fairness loss
gradients among jointly learned tasks, termed bias transfer. To address both
negative and bias transfer in MTL, we introduce a novel method called
FairBranch. FairBranch branches the MTL model by assessing the similarity of
learned parameters, grouping related tasks to mitigate negative transfer.
Additionally, it incorporates fairness loss gradient conflict correction
between adjoining task-group branches to address bias transfer within these
task groups. Our experiments in tabular and visual MTL problems demonstrate
that FairBranch surpasses state-of-the-art MTL methods in terms of both
fairness and accuracy.

通过评估学习参数的相似性，将相关任务分组以减轻负迁移，并在这些任务组内纳入公平性损失梯度冲突校正，从而解决多任务学习中的负迁移和偏见传递问题。实验证明，FairBranch 在公平性和准确性方面优于最先进的多任务学习方法。

FairBranch：在任务组分支上对公平多任务学习的公平冲突纠正

FairBranch: Fairness Conflict Correction on Task-group Branches for Fair  Multi-Task Learning

In AI-assisted decision-making, effective hybrid (human-AI) teamwork is not
solely dependent on AI performance alone, but also on its impact on human
decision-making. While prior work studies the effects of model accuracy on
humans, we endeavour here to investigate the complex dynamics of how both a
model's predictive performance and bias may transfer to humans in a
recommendation-aided decision task. We consider the domain of ML-assisted
hiring, where humans -- operating in a constrained selection setting -- can
choose whether they wish to utilize a trained model's inferences to help select
candidates from written biographies. We conduct a large-scale user study
leveraging a re-created dataset of real bios from prior work, where humans
predict the ground truth occupation of given candidates with and without the
help of three different NLP classifiers (random, bag-of-words, and deep neural
network). Our results demonstrate that while high-performance models
significantly improve human performance in a hybrid setting, some models
mitigate hybrid bias while others accentuate it. We examine these findings
through the lens of decision conformity and observe that our model architecture
choices have an impact on human-AI conformity and bias, motivating the explicit
need to assess these complex dynamics prior to deployment.

探讨了使用 AI 辅助决策时，人工智能的预测性能与偏差如何对人类决策产生影响的复杂动态，并通过大规模用户研究发现，高性能的模型显著提高了人类在混合环境中的表现，但有些模型削弱了混合偏见，有些则加重了混合偏见。这些研究结果表明，预先评估这些复杂动态对于部署人工智能具有明确的必要性。