发表机构
CISPA Helmholtz Center for Information Security; Technical University of Munich（CISPA亥姆霍兹信息安全中心; 慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FairMon是一种运行时监控工具，利用扩展了条件概率算子的RTLola语言，实时可视化中间值，以分析高风险决策系统的算法公平性，支持人类监督。
AI 中文摘要
运行时监控最近被提出作为一种严格的方法，用于分析在关键场景（如信贷借贷、求职申请和刑事司法系统）中使用的自主决策系统的算法公平性。先前的工作表明，原则上，运行时监控可以成为一种有效的技术，用于建立欧盟人工智能法案等立法所要求的人类监督。在实践中，现有的监控工具并非针对此应用而开发，在这些场景中表现出若干关键缺陷。在本文中，我们提出了FairMon，一种专为高风险决策系统的公平性分析而设计的运行时监控工具。FairMon使用RTLola作为监控器的灵活规范语言，我们对其进行了扩展，增加了条件概率算子，从而能够简洁地描述算法公平性属性。该工具还具备中间值的实时可视化功能，使人类能够洞察被监控系统的动态。
英文摘要
Runtime monitoring has recently been proposed as a rigorous method for analyzing algorithmic fairness of autonomous decision systems used in critical scenarios such as credit lending, job application, and the criminal justice system. Prior work has shown that runtime monitoring, in principle, can be an effective technique for establishing the kind of human oversight required by legislation such as the EU Artificial Intelligence Act. In practice, the available monitoring tools have not been developed with this application in mind and display several critical shortcomings in these scenarios. In this paper, we present FairMon, a runtime monitoring tool tailored to fairness analysis of high-stakes decision systems. FairMon uses RTLola as a flexible specification language for monitors, which we have extended with conditional probability operators that allow for concise descriptions of algorithmic fairness properties. The tool also features a real-time visualization of intermediary values, enabling human insight into the dynamics of the monitored system.
CommentsAccepted at RV 2026
