Learning how to approve updates to machine learning algorithms in non-stationary settings

TL;DR

This paper proposes a learning-to-approve (L2A) framework that autonomously learns to approve or abstain from deploying updates to machine learning models in healthcare, adapting to dataset shifts and ensuring safety.

Contribution

It introduces a family of approval strategies optimized via an exponentially weighted average forecaster, with theoretical risk bounds and empirical validation in non-stationary settings.

Findings

L2A adapts approval speed based on data stability.

L2A effectively balances approval and abstention to maintain safety.

Theoretical bounds support the approach's reliability.

Abstract

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.