An Efficient Framework for Monitoring Subgroup Performance of Machine Learning Systems
This addresses the challenge of costly performance monitoring for subgroups in deployed ML systems, which is incremental as it builds on existing optimization methods.
The paper tackles the problem of efficiently monitoring the performance of machine learning systems across data subgroups post-deployment, proposing a framework that uses Bayesian optimization to identify the worst-performing subgroup with limited labeled data, showing effectiveness in experiments on real-world datasets.
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.