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Found 8 papers, 1 papers with code
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.
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.
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)
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.
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.
AERoS: Assurance of Emergent Behaviour in Autonomous Robotic Swarms
The behaviours of a swarm are not explicitly engineered.
Unravelling Responsibility for AI
As part of a conscious effort towards 'unravelling' the concept of responsibility to support practical reasoning about responsibility for AI, this paper takes the three-part formulation, 'Actor A is responsible for Occurrence O' and identifies valid combinations of subcategories of A, is responsible for, and O.
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.
