This paper studies long-term fair machine learning which aims to mitigate
group disparity over the long term in sequential decision-making systems. To
define long-term fairness, we leverage the temporal causal graph and use the
1-Wasserstein distance between the interventional distributions of different
demographic groups at a sufficiently large time step as the quantitative
metric. Then, we propose a three-phase learning framework where the decision
model is trained on high-fidelity data generated by a deep generative model. We
formulate the optimization problem as a performative risk minimization and
adopt the repeated gradient descent algorithm for learning. The empirical
evaluation shows the efficacy of the proposed method using both synthetic and
semi-synthetic datasets.

研究论文通过使用时间因果图和不同人群干预分布之间的 1-Wasserstein 距离作为定量指标，以减少顺序决策系统中的群体差异，研究了长期公平的机器学习。然后，提出了一个三阶段的学习框架，其中决策模型在深度生成模型生成的高保真数据上进行训练，将优化问题制定为执行风险最小化，并采用了重复梯度下降算法进行学习。经验评估证明了所提出方法在合成和半合成数据集上的有效性。

基于深度生成模型的长期公平决策

Long-Term Fair Decision Making through Deep Generative Models

Pretrained multilingual language models can help bridge the digital language
divide, enabling high-quality NLP models for lower resourced languages. Studies
of multilingual models have so far focused on performance, consistency, and
cross-lingual generalisation. However, with their wide-spread application in
the wild and downstream societal impact, it is important to put multilingual
models under the same scrutiny as monolingual models. This work investigates
the group fairness of multilingual models, asking whether these models are
equally fair across languages. To this end, we create a new four-way
multilingual dataset of parallel cloze test examples (MozArt), equipped with
demographic information (balanced with regard to gender and native tongue)
about the test participants. We evaluate three multilingual models on MozArt --
mBERT, XLM-R, and mT5 -- and show that across the four target languages, the
three models exhibit different levels of group disparity, e.g., exhibiting
near-equal risk for Spanish, but high levels of disparity for German.

探讨预训练多语言语言模型的组公平性，通过创建一个新的平行洞察测试实例的多语言数据集（MozArt）及使用人口统计信息来评估三种多语言模型（mBERT，XLM-R 和 mT5），我们发现这三种模型在四种目标语言中表现出不同程度的组不公平性，例如在西班牙语中表现出接近相等的风险，但在德语中表现出高水平的不平等。