# FairMon: A Tool for Monitoring and Visualizing Algorithmic Fairness

arXiv: https://arxiv.org/abs/2609.26123 · HTML: https://mlsanity.com/paper/2609.26123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 17 of 17)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [FairVis: Visual Analytics for Discovering Intersectional Bias in Machine Learning](https://mlsanity.com/paper/1904.05419/) (2019)
- [OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning](https://mlsanity.com/paper/2103.09055/) (2021)
- [Runtime Monitoring of Dynamic Fairness Properties](https://mlsanity.com/paper/2305.04699/) (2023)
- [The DSA Transparency Database: Auditing Self-reported Moderation Actions by Social Media](https://mlsanity.com/paper/2312.10269/) (2023)
- [Monitoring Algorithmic Fairness](https://mlsanity.com/paper/2305.15979/) (2023)
- [Monitoring Algorithmic Fairness under Partial Observations](https://mlsanity.com/paper/2308.00341/) (2023)
- [Stream-Based Monitoring of Algorithmic Fairness](https://mlsanity.com/paper/2501.18331/) (2025)
- [LangBiTe: A Platform for Testing Bias in Large Language Models](https://mlsanity.com/paper/2404.18558/) (2024)
- [Leveraging Static Analysis: An IDE for RTLola](https://mlsanity.com/paper/2311.08096/) (2023)
- [Monitoring Unmanned Aircraft: Specification, Integration, and Lessons-learned](https://mlsanity.com/paper/2404.12035/) (2024)
- [An Intermediate Program Representation for Optimizing Stream-Based Languages](https://mlsanity.com/paper/2504.21458/) (2025)
- [Active Monitoring with RTLola: A Specification-Guided Scheduling Approach](https://mlsanity.com/paper/2507.20615/) (2025)
- [Differentially Private Runtime Monitoring](https://mlsanity.com/paper/2605.02391/) (2026)

## Abstract (arXiv)

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.