标题： 社会系统中预测与干预问题的桥梁 显示英文标题

标题： Bridging Prediction and Intervention Problems in Social Systems

摘要： 许多自动化决策系统（ADS）被设计用来解决预测问题——其目标是从人口样本中学习模式，并将其应用于同一人口中的个体。 在现实中，这些预测系统在部署时会通过决策者操作方式的有效政策变化来影响受影响的人口结果，同时又受到现有组织和社会基础设施及背景中利益相关者过去和现在的互动限制。 在本工作中，我们考虑了在考虑ADS在社会系统中的影响时，必须从以预测为中心的范式转向干预主义范式的各种方式。 我们认为，这需要为ADS建立一个超越预测的新默认问题设置，而是将预测视为决策支持、最终决策和结果。 我们强调这种观点如何统一现代统计框架和其他工具来研究ADS系统的设计、实施和评估，并指出为了实现这种范式转变所需的研究方向。 使用这些工具，我们描述了专注于孤立预测任务的局限性，并为开发和部署ADS的更干预导向的方法奠定了基础。 显示英文摘要

摘要： 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.