# Runtime Monitoring of Dynamic Fairness Properties arXiv: https://arxiv.org/abs/2305.04699 ยท HTML: https://mlsanity.com/paper/2305.04699/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 41 references that are in the map. - First posted on arXiv 8 May 2023. ## Builds on (most cited first, 21 of 21) - [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016) - [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019) - [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014) - [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018) - [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016) - [Fairness Without Demographics in Repeated Loss Minimization](https://mlsanity.com/paper/1806.08010/) (2018) - [Delayed Impact of Fair Machine Learning](https://mlsanity.com/paper/1803.04383/) (2018) - [Estimating means of bounded random variables by betting](https://mlsanity.com/paper/2010.09686/) (2020) - [A Convex Framework for Fair Regression](https://mlsanity.com/paper/1706.02409/) (2017) - [How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility](https://mlsanity.com/paper/1710.11214/) (2017) - [Runaway Feedback Loops in Predictive Policing](https://mlsanity.com/paper/1706.09847/) (2017) - [On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning](https://mlsanity.com/paper/1903.01209/) (2019) - [Shield Synthesis: Runtime Enforcement for Reactive Systems](https://mlsanity.com/paper/1501.02573/) (2015) - [The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally](https://mlsanity.com/paper/1910.04123/) (2019) - [Fair Algorithms for Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2007.06699/) (2020) - [Tracking the risk of a deployed model and detecting harmful distribution shifts](https://mlsanity.com/paper/2110.06177/) (2021) - [Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness](https://mlsanity.com/paper/1905.00569/) (2019) - [From Fair Decision Making to Social Equality](https://mlsanity.com/paper/1812.02952/) (2018) - [Fairness in Learning-Based Sequential Decision Algorithms: A Survey](https://mlsanity.com/paper/2001.04861/) (2020) - [Fair Algorithms for Learning in Allocation Problems](https://mlsanity.com/paper/1808.10549/) (2018) - [Monitoring Event Frequencies](https://mlsanity.com/paper/1910.06097/) (2019) ## Built on by (most cited first, 17 of 17) - [Visibility into AI Agents](https://mlsanity.com/paper/2401.13138/) (2024) - [Fairness in Reinforcement Learning: A Survey](https://mlsanity.com/paper/2405.06909/) (2024) - [Monitoring Algorithmic Fairness](https://mlsanity.com/paper/2305.15979/) (2023) - [Monitoring Algorithmic Fairness under Partial Observations](https://mlsanity.com/paper/2308.00341/) (2023) - [Human oversight of agentic systems in practice: Examining the oversight work, challenges, and heuristics of developers using software agents](https://mlsanity.com/paper/2606.05391/) (2026) - [Stream-Based Monitoring of Algorithmic Fairness](https://mlsanity.com/paper/2501.18331/) (2025) - [Is it Still Fair? A Comparative Evaluation of Fairness Algorithms through the Lens of Covariate Drift](https://mlsanity.com/paper/2409.12428/) (2024) - [FairSense: Long-Term Fairness Analysis of ML-Enabled Systems](https://mlsanity.com/paper/2501.01665/) (2025) - [Monitoring Robustness and Individual Fairness](https://mlsanity.com/paper/2506.00496/) (2025) - [Lookahead Counterfactual Fairness](https://mlsanity.com/paper/2412.01065/) (2024) - [A Causal Lens for Learning Long-term Fair Policies](https://mlsanity.com/paper/2506.11242/) (2025) - [A Critical Review of Predominant Bias in Neural Networks](https://mlsanity.com/paper/2502.11031/) (2025) - [Monitoring of Static Fairness](https://mlsanity.com/paper/2507.03048/) (2025) - [Algorithmic Fairness: A Runtime Perspective](https://mlsanity.com/paper/2507.20711/) (2025) - [Energy Shields for Fairness](https://mlsanity.com/paper/2605.24926/) (2026) - [Evaluating Model Retraining under Drift: Paired Comparisons of Cumulative Subgroup Disparity](https://mlsanity.com/paper/2609.09788/) (2026) - [FairMon: A Tool for Monitoring and Visualizing Algorithmic Fairness](https://mlsanity.com/paper/2609.26123/) (2026) ## Abstract (arXiv) A machine-learned system that is fair in static decision-making tasks may have biased societal impacts in the long-run. This may happen when the system interacts with humans and feedback patterns emerge, reinforcing old biases in the system and creating new biases. While existing works try to identify and mitigate long-run biases through smart system design, we introduce techniques for monitoring fairness in real time. Our goal is to build and deploy a monitor that will continuously observe a long sequence of events generated by the system in the wild, and will output, with each event, a verdict on how fair the system is at the current point in time. The advantages of monitoring are two-fold. Firstly, fairness is evaluated at run-time, which is important because unfair behaviors may not be eliminated a priori, at design-time, due to partial knowledge about the system and the environment, as well as uncertainties and dynamic changes in the system and the environment, such as the unpredictability of human behavior. Secondly, monitors are by design oblivious to how the monitored system is constructed, which makes them suitable to be used as trusted third-party fairness watchdogs. They function as computationally lightweight statistical estimators, and their correctness proofs rely on the rigorous analysis of the stochastic process that models the assumptions about the underlying dynamics of the system. We show, both in theory and experiments, how monitors can warn us (1) if a bank's credit policy over time has created an unfair distribution of credit scores among the population, and (2) if a resource allocator's allocation policy over time has made unfair allocations. Our experiments demonstrate that the monitors introduce very low overhead. We believe that runtime monitoring is an important and mathematically rigorous new addition to the fairness toolbox.