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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.
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.
Detection and Mitigation of Rare Subclasses in Deep Neural Network Classifiers
Regions of high-dimensional input spaces that are underrepresented in training datasets reduce machine-learnt classifier performance, and may lead to corner cases and unwanted bias for classifiers used in decision making systems.
