Learning how to approve updates to machine learning algorithms in non-stationary settings
This work is significant for regulatory bodies like the FDA, aiming to design policies for autonomous approval of ML algorithm modifications in healthcare, ensuring safety and effectiveness in non-stationary settings.
This paper addresses the challenge of autonomously approving updates to machine learning algorithms in healthcare, where data distributions can be non-stationary. The authors propose a learning-to-approve (L2A) approach that adapts its approval strategy based on accumulating monitoring data, learning to abstain when performance drops are common and approving beneficial modifications quickly when distributions are stable.
Machine learning algorithms in healthcare have the potential to continually learn from real-world data generated during healthcare delivery and adapt to dataset shifts. As such, the FDA is looking to design policies that can autonomously approve modifications to machine learning algorithms while maintaining or improving the safety and effectiveness of the deployed models. However, selecting a fixed approval strategy, a priori, can be difficult because its performance depends on the stationarity of the data and the quality of the proposed modifications. To this end, we investigate a learning-to-approve approach (L2A) that uses accumulating monitoring data to learn how to approve modifications. L2A defines a family of strategies that vary in their "optimism''---where more optimistic policies have faster approval rates---and searches over this family using an exponentially weighted average forecaster. To control the cumulative risk of the deployed model, we give L2A the option to abstain from making a prediction and incur some fixed abstention cost instead. We derive bounds on the average risk of the model deployed by L2A, assuming the distributional shifts are smooth. In simulation studies and empirical analyses, L2A tailors the level of optimism for each problem-setting: It learns to abstain when performance drops are common and approve beneficial modifications quickly when the distribution is stable.