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Safety Analysis of Autonomous Railway Systems: An Introduction to the SACRED Methodology
To combat these difficulties we introduce SACRED, a safety methodology for producing an initial safety case and determining important safety metrics for autonomous systems.
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.
Safety Assessment for Autonomous Systems' Perception Capabilities
The well-established safety-analysis methods developed for conventional SC systems are not well-matched to AS, ML, or the sensing systems used by AS.
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)
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.
