Statistical fairness stipulates equivalent outcomes for every protected
group, whereas causal fairness prescribes that a model makes the same
prediction for an individual regardless of their protected characteristics.
Counterfactual data augmentation (CDA) is effective for reducing bias in NLP
models, yet models trained with CDA are often evaluated only on metrics that
are closely tied to the causal fairness notion; similarly, sampling-based
methods designed to promote statistical fairness are rarely evaluated for
causal fairness. In this work, we evaluate both statistical and causal
debiasing methods for gender bias in NLP models, and find that while such
methods are effective at reducing bias as measured by the targeted metric, they
do not necessarily improve results on other bias metrics. We demonstrate that
combinations of statistical and causal debiasing techniques are able to reduce
bias measured through both types of metrics.

本研究评估了性别偏见的 NLP 模型的统计和因果去偏方法，发现这些方法在目标度量上有效减少了偏见，但不一定改善了其他偏见度量的结果。同时，我们展示了统计和因果去偏技术的组合能够减少通过两种类型的度量来衡量的偏见。

在 NLP 模型中同时解决统计性别公平性和因果性别公平性

Addressing Both Statistical and Causal Gender Fairness in NLP Models

Federated Learning (FL) has emerged as a result of data ownership and privacy
concerns to prevent data from being shared between multiple parties included in
a training procedure. Although issues, such as privacy, have gained significant
attention in this domain, not much attention has been given to satisfying
statistical fairness measures in the FL setting. With this goal in mind, we
conduct studies to show that FL is able to satisfy different fairness metrics
under different data regimes consisting of different types of clients. More
specifically, uncooperative or adversarial clients might contaminate the global
FL model by injecting biased or poisoned models due to existing biases in their
training datasets. Those biases might be a result of imbalanced training set
(Zhang and Zhou 2019), historical biases (Mehrabi et al. 2021a), or poisoned
data-points from data poisoning attacks against fairness (Mehrabi et al. 2021b;
Solans, Biggio, and Castillo 2020). Thus, we propose a new FL framework that is
able to satisfy multiple objectives including various statistical fairness
metrics. Through experimentation, we then show the effectiveness of this method
comparing it with various baselines, its ability in satisfying different
objectives collectively and individually, and its ability in identifying
uncooperative or adversarial clients and down-weighing their effect

本文针对联邦学习领域中的数据隐私和公平性问题，提出了一种能够满足不同统计公平度量的新方法，并进行实验验证其在满足多个目标方面的有效性和鲁棒性。