Unifying Evaluation of Machine Learning Safety Monitors
This work addresses the need for standardized safety evaluation in critical autonomous systems, though it is incremental as it builds on existing monitor concepts without introducing new methods.
The paper tackled the problem of inconsistent evaluation of machine learning safety monitors by introducing three unified safety-oriented metrics—Safety Gain, Residual Hazard, and Availability Cost—to assess monitors across different applications, demonstrating their impact on perceived performance in use-cases like classification, drone landing, and autonomous driving.
With the increasing use of Machine Learning (ML) in critical autonomous systems, runtime monitors have been developed to detect prediction errors and keep the system in a safe state during operations. Monitors have been proposed for different applications involving diverse perception tasks and ML models, and specific evaluation procedures and metrics are used for different contexts. This paper introduces three unified safety-oriented metrics, representing the safety benefits of the monitor (Safety Gain), the remaining safety gaps after using it (Residual Hazard), and its negative impact on the system's performance (Availability Cost). To compute these metrics, one requires to define two return functions, representing how a given ML prediction will impact expected future rewards and hazards. Three use-cases (classification, drone landing, and autonomous driving) are used to demonstrate how metrics from the literature can be expressed in terms of the proposed metrics. Experimental results on these examples show how different evaluation choices impact the perceived performance of a monitor. As our formalism requires us to formulate explicit safety assumptions, it allows us to ensure that the evaluation conducted matches the high-level system requirements.