# An Efficient Framework for Monitoring Subgroup Performance of Machine Learning Systems

arXiv: https://arxiv.org/abs/2212.08312 · HTML: https://mlsanity.com/paper/2212.08312/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 16 Dec 2022.

## Builds on (most cited first, 10 of 10)

- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Scalable Bayesian Optimization Using Deep Neural Networks](https://mlsanity.com/paper/1502.05700/) (2015)
- [An Intersectional Definition of Fairness](https://mlsanity.com/paper/1807.08362/) (2018)
- [Active Testing: Sample-Efficient Model Evaluation](https://mlsanity.com/paper/2103.05331/) (2021)
- [Auditing and Achieving Intersectional Fairness in Classification Problems](https://mlsanity.com/paper/1911.01468/) (2019)
- [Operationalizing Machine Learning: An Interview Study](https://mlsanity.com/paper/2209.09125/) (2022)
- [Identifying Significant Predictive Bias in Classifiers](https://mlsanity.com/paper/1611.08292/) (2016)
- [MLDemon: Deployment Monitoring for Machine Learning Systems](https://mlsanity.com/paper/2104.13621/) (2021)
- [Identifying Biased Subgroups in Ranking and Classification](https://mlsanity.com/paper/2108.07450/) (2021)
- [ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms](https://mlsanity.com/paper/2104.04999/) (2021)

## Abstract (arXiv)

Monitoring machine learning systems post deployment is critical to ensure the reliability of the systems. Particularly importance is the problem of monitoring the performance of machine learning systems across all the data subgroups (subpopulations). In practice, this process could be prohibitively expensive as the number of data subgroups grows exponentially with the number of input features, and the process of labelling data to evaluate each subgroup's performance is costly. In this paper, we propose an efficient framework for monitoring subgroup performance of machine learning systems. Specifically, we aim to find the data subgroup with the worst performance using a limited number of labeled data. We mathematically formulate this problem as an optimization problem with an expensive black-box objective function, and then suggest to use Bayesian optimization to solve this problem. Our experimental results on various real-world datasets and machine learning systems show that our proposed framework can retrieve the worst-performing data subgroup effectively and efficiently.