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Found 9 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.
What's my role? Modelling responsibility for AI-based safety-critical systems
Many authors have commented on the "responsibility gap" where it is difficult for developers and manufacturers to be held responsible for harmful behaviour of an AI-SCS.
Unravelling Responsibility for AI
It is widely acknowledged that we need to establish where responsibility lies for the outputs and impacts of AI-enabled systems.
AERoS: Assurance of Emergent Behaviour in Autonomous Robotic Swarms
The behaviours of a swarm are not explicitly engineered.
Review of the AMLAS Methodology for Application in Healthcare
In recent years, the number of machine learning (ML) technologies gaining regulatory approval for healthcare has increased significantly allowing them to be placed on the market.
A Principles-based Ethics Assurance Argument Pattern for AI and Autonomous Systems
An assurance case is a structured argument, typically produced by safety engineers, to communicate confidence that a critical or complex system, such as an aircraft, will be acceptably safe within its intended context.
The Role of Explainability in Assuring Safety of Machine Learning in Healthcare
Established approaches to assuring safety-critical systems and software are difficult to apply to systems employing ML where there is no clear, pre-defined specification against which to assess validity.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
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.
A Framework for Assurance of Medication Safety using Machine Learning
As healthcare is now data rich, it is possible to augment safety analysis with machine learning to discover actual causes of medication error from the data, and to identify where they deviate from what was predicted in the safety analysis.
