aiqbench analysis
FairMon addresses critical gaps in existing runtime monitoring tools by introducing a specialized framework for auditing algorithmic fairness in high-stakes autonomous systems like credit lending and criminal justice algorithms. By extending the RTLola specification language with conditional probability operators, the tool enables rigorous, real-time verification of fairness properties alongside dynamic visualization of system intermediates. This advancement directly supports compliance frameworks such as the EU AI Act by providing the technical mechanisms necessary for effective human oversight of decision-making systems.
FairMon: A Tool for Monitoring and Visualizing Algorithmic Fairness
arXiv:2609.26123v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
arXiv:2609.26123 [cs.AI]
(or arXiv:2609.26123v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.26123 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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