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 intervention-oriented 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——其目标是从总体样本中学习模式，并将其应用于同一总体中的个体。实际上，这些预测系统在部署中执行的是整体性政策干预。一旦部署，ADS可以通过改变决策者的操作方式实现有效的政策变革，从而影响目标群体的结果；同时，ADS本身也由利益相关者之间过去和现在的互动、以及现有组织与社会基础设施及背景的局限性所定义。在本研究中，我们探讨了在考虑ADS对社会系统的影响时，必须如何从以预测为中心的范式转向以干预为导向的范式。我们认为，这需要为ADS建立超越预测的新默认问题设置，转而将预测视为决策支持、最终决策和结果。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，并指出实现这一范式转变所需的研究方向。借助这些工具，我们阐明了聚焦于孤立预测任务的局限性，并为开发与部署ADS建立更具干预导向性的方法奠定了基础。