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Found 4 papers, 1 papers with code
Learning Run-time Safety Monitors for Machine Learning Components
A critical part of this is to be able to monitor when the performance of the model at runtime (as a result of changes) poses a safety risk to the system.
Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning
We illustrate the application of the framework on an example excerpt concerned with the quality of the test data.
Creating a Safety Assurance Case for an ML Satellite-Based Wildfire Detection and Alert System
Wildfires are a common problem in many areas of the world with often catastrophic consequences.
Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)
Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance.
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