Many automated decision systems (ADS) are designed to solve prediction problems -- where the goal is to learn patterns from a sample of the population and apply them to individuals from the same population. In reality, these prediction systems operationalize holistic policy interventions in deployment. Once deployed, ADS can shape impacted population outcomes through an effective policy change in how decision-makers operate, while also being defined by past and present interactions between stakeholders and the limitations of existing organizational, as well as societal, infrastructure and context. In this work, we consider the ways in which we must shift from a prediction-focused paradigm to an interventionist paradigm when considering the impact of ADS within social systems. We argue this requires a new default problem setup for ADS beyond prediction, to instead consider predictions as decision support, final decisions, and outcomes. We highlight how this perspective unifies modern statistical frameworks and other tools to study the design, implementation, and evaluation of ADS systems, and point to the research directions necessary to operationalize this paradigm shift. Using these tools, we characterize the limitations of focusing on isolated prediction tasks, and lay the foundation for a more intervention-oriented approach to developing and deploying ADS.

翻译：许多自动化决策系统（ADS）旨在解决预测问题——其目标是从群体样本中学习规律，并将其应用于同一群体的个体。然而在实际部署中，这些预测系统往往通过整体性政策干预得以实施。一旦部署，ADS既可通过改变决策者的操作方式引发有效的政策变革，从而塑造受影响群体的结果，同时也受到利益相关者之间历史及当前互动、现有组织与社会基础设施及情境限制的制约。本研究探讨了在社会系统中评估ADS影响时，必须从以预测为中心的范式转向干预主义范式的方式。我们认为这需要为ADS建立超越预测的新默认问题框架，将预测视为决策支持、最终决策与结果的一环。我们强调这一视角如何统一现代统计框架及其他工具，以研究ADS系统的设计、实施与评估，并指出实现此范式转变所需的研究方向。借助这些工具，我们揭示了孤立预测任务的局限性，并为开发与部署ADS奠定了更具干预导向的方法基础。