当预测塑造现实：机器学习中表现性预测的社会技术综合
When Predictions Shape Reality: A Socio-Technical Synthesis of Performative Predictions in Machine Learning
AI总结：
本文提出“表现性强度与影响矩阵”框架，用于评估机器学习模型中表现性预测的影响和严重性，指导算法或人工干预的选择。
摘要：
机器学习模型越来越多地应用于高风险领域，其预测能够主动塑造其运行的环境，这种现象被称为表现性预测。这种动态中，模型的部署会影响其试图预测的成果，可能导致意外后果，包括反馈循环、性能问题和社会风险。尽管该领域的文献近年来发展迅速，但缺乏系统整合该现象概念并提供实际指导的社会技术综合。本系统化知识（SoK）通过全面回顾表现性预测的文献来填补这一空白。我们概述了表现性如何体现的主导机制，提出了相关风险的分类，并调查了文献中提出的解决方案。我们的主要贡献是“表现性强度与影响矩阵”评估框架。这个实用工具旨在帮助从业者评估表现性对部署预测模型的潜在影响和严重性，并选择适当的算法或人工干预级别。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
