The use of data-driven decision support by public agencies is becoming more widespread and already influences the allocation of public resources. This raises ethical concerns, as it has adversely affected minorities and historically discriminated groups. In this paper, we use an approach that combines statistics and data-driven approaches with dynamical modeling to assess long-term fairness effects of labor market interventions. Specifically, we develop and use a model to investigate the impact of decisions caused by a public employment authority that selectively supports job-seekers through targeted help. The selection of who receives what help is based on a data-driven intervention model that estimates an individual's chances of finding a job in a timely manner and rests upon data that describes a population in which skills relevant to the labor market are unevenly distributed between two groups (e.g., males and females). The intervention model has incomplete access to the individual's actual skills and can augment this with knowledge of the individual's group affiliation, thus using a protected attribute to increase predictive accuracy. We assess this intervention model's dynamics -- especially fairness-related issues and trade-offs between different fairness goals -- over time and compare it to an intervention model that does not use group affiliation as a predictive feature. We conclude that in order to quantify the trade-off correctly and to assess the long-term fairness effects of such a system in the real-world, careful modeling of the surrounding labor market is indispensable.

翻译：公共机构采用数据驱动型决策支持的做法日益普及，并已影响公共资源的分配。这引发了伦理关切，因为此类做法曾对少数群体及历史上受歧视群体造成不利影响。本文采用统计方法、数据驱动方法与动态建模相结合的路径，评估劳动力市场干预措施的长期公平性效应。具体而言，我们构建并运用一个模型，研究公共就业部门通过选择性支持求职者的定向帮扶所引发的决策影响。帮扶对象及内容的筛选基于一个数据驱动的干预模型，该模型估算个体及时找到工作的概率，并基于描述人群特征的数据（其中与劳动力市场相关的技能在两个群体间（如男性和女性）呈不均衡分布）。干预模型无法完全获取个体的实际技能，但可通过知晓个体的群体归属信息来补充预测，即利用受保护属性提升预测准确性。我们评估该干预模型随时间演化的动态特性——尤其是公平性相关问题及不同公平目标间的权衡——并与未将群体归属作为预测特征的干预模型进行对比。研究结论表明：为准确量化权衡关系并评估此类系统在现实中的长期公平性效应，必须对周边劳动力市场进行审慎建模。