With the ever growing involvement of data-driven AI-based decision making technologies in our daily social lives, the fairness of these systems is becoming a crucial phenomenon. However, an important and often challenging aspect in utilizing such systems is to distinguish validity for the range of their application especially under distribution shifts, i.e., when a model is deployed on data with different distribution than the training set. In this paper, we present a case study on the newly released American Census datasets, a reconstruction of the popular Adult dataset, to illustrate the importance of context for fairness and show how remarkably can spatial distribution shifts affect predictive- and fairness-related performance of a model. The problem persists for fairness-aware learning models with the effects of context-specific fairness interventions differing across the states and different population groups. Our study suggests that robustness to distribution shifts is necessary before deploying a model to another context.

翻译：随着以数据驱动的基于AI的决策技术越来越多地参与我们的日常生活,这些系统的公平性正在成为一种关键现象,然而,在利用这些系统时,一个重要而且往往具有挑战性的方面是,要区别其应用范围的有效性,特别是在分布变化的情况下,即,在数据分布与培训组不同的数据上部署一个模型时,这种模型就不同分布。在本文件中,我们介绍了关于新发布的美国人口普查数据集的案例研究,即对受欢迎的成人数据集的重建,以说明公平环境的重要性,并表明空间分布变化如何能显著地影响模型的预测和公平相关业绩。公平意识学习模式仍然存在问题,其影响是各州和不同人口群体之间因地不同而异的公平干预。我们的研究表明,在将模型运用到另一个环境之前,必须稳健地进行分配转移。