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

Fair machine learning aims to prevent discrimination against individuals or
sub-populations based on sensitive attributes such as gender and race. In
recent years, causal inference methods have been increasingly used in fair
machine learning to measure unfairness by causal effects. However, current
methods assume that the true causal graph is given, which is often not true in
real-world applications. To address this limitation, this paper proposes a
framework for achieving causal fairness based on the notion of interventions
when the true causal graph is partially known. The proposed approach involves
modeling fair prediction using a Partially Directed Acyclic Graph (PDAG),
specifically, a class of causal DAGs that can be learned from observational
data combined with domain knowledge. The PDAG is used to measure causal
fairness, and a constrained optimization problem is formulated to balance
between fairness and accuracy. Results on both simulated and real-world
datasets demonstrate the effectiveness of this method.

本文提出了一种基于干预的因果公平性框架，用于在部分已知因果图的情况下实现公平预测，该方法使用部分有向无环图（PDAG）来度量因果公平性，并制定一个受限制的优化问题来平衡公平性和准确性。模拟和真实数据集的结果证明了该方法的有效性。

部分已知因果图的干预公平性：一种约束优化方法

Interventional Fairness on Partially Known Causal Graphs: A Constrained  Optimization Approach

Fairness for machine learning predictions is widely required in practice for
legal, ethical, and societal reasons. Existing work typically focuses on
settings without unobserved confounding, even though unobserved confounding can
lead to severe violations of causal fairness and, thus, unfair predictions. In
this work, we analyze the sensitivity of causal fairness to unobserved
confounding. Our contributions are three-fold. First, we derive bounds for
causal fairness metrics under different sources of unobserved confounding. This
enables practitioners to examine the sensitivity of their machine learning
models to unobserved confounding in fairness-critical applications. Second, we
propose a novel neural framework for learning fair predictions, which allows us
to offer worst-case guarantees of the extent to which causal fairness can be
violated due to unobserved confounding. Third, we demonstrate the effectiveness
of our framework in a series of experiments, including a real-world case study
about predicting prison sentences. To the best of our knowledge, ours is the
first work to study causal fairness under unobserved confounding. To this end,
our work is of direct practical value as a refutation strategy to ensure the
fairness of predictions in high-stakes applications.

现实中，由于法律、伦理和社会原因，对机器学习预测的公平性要求越来越高。尽管现有工作通常关注没有未观察到混淆的情况，但未观察到的混淆可能导致严重的因果公平性违规和不公平的预测。本研究分析了未观察到的混淆对因果公平性的敏感性，并提出了一个新的神经框架用于学习公平的预测，同时在一系列实验中验证了该框架的有效性。