机器学习预测会改变现实，这篇论文给出了评估和应对的实用框架。
When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning
- 提出'表演性强度与影响矩阵'评估框架
- 系统梳理了预测影响现实的机制与风险类型
- 适合部署高风险预测系统的从业者参考
机器学习模型在高风险领域中的应用日益广泛，其预测可能主动改变所处环境，这种现象被称为表演性预测。当模型的部署影响其试图预测的结果时，可能引发反馈循环、性能下降及重大社会风险。尽管该领域研究迅速增长，但缺乏对现象概念的系统整合与实践指导。本文通过知识体系化（SoK）综述，全面梳理表演性预测的文献，阐述其主要表现机制，提出相关风险分类，并调研现有解决方案。核心贡献是构建‘表演性强度与影响矩阵’评估框架，帮助从业者判断模型预测对现实的影响程度与严重性，进而选择合适的算法或人工干预策略。
原文摘要 · Abstract (English)
Machine learning models are increasingly used in high-stakes domains where their predictions can actively shape the environments in which they operate, a phenomenon known as performative prediction. This dynamic, in which the deployment of the model influences the very outcome it seeks to predict, can lead to unintended consequences, including feedback loops, performance issues, and significant societal risks. While the literature in the field has grown rapidly in recent years, a socio-technical synthesis that systemises the phenomenon concepts and provides practical guidance has been lacking. This Systematisation of Knowledge (SoK) addresses this gap by providing a comprehensive review of the literature on performative predictions. We provide an overview of the primary mechanisms through which performativity manifests, present a typology of associated risks, and survey the proposed solutions offered in the literature. Our primary contribution is the ``Performative Strength vs. Impact Matrix" assessment framework. This practical tool is designed to help practitioners assess the potential influence and severity of performativity on their deployed predictive models and select the appropriate level of algorithmic or human intervention.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。