Whatever domain you work in, your autonomous system must be safe. Those who use it, design new features for it, regulate it or might be impacted by it need confidence that the system will behave safely and as expected.
On this site, you will find expert, impartial guidance to help you to create a credible and compelling assurance case for your autonomous system.
The guidance is written by the Assuring Autonomy International Programme at the University of York and is peer-reviewed by industry experts. The guidance will cover the core technical issues that must be considered for the safe development and introduction of an autonomous system.
Our methodology for the Assurance of Machine Learning for use in Autonomous Systems (AMLAS) is the first of its kind and can help you to justify the safety of your machine learnt components.
We have created a series of processes which enables the creation of a safety assurance case for both autonomous systems, and machine learning autonomous systems. These are AMLAS (Assurance of Machine Learning for us in Autonomous Systems) and SACE (Safety Assurance of autonomous systems in Complex Environments).
If you are new to these processes, download our AMLAS and SACE factsheets to learn how and why your organisation can benefit from our guidance.
We have developed the first methodology for the Assurance of Machine Learning for use in Autonomous Systems (AMLAS).
AMLAS has six stages, which complement the machine learning (ML) development process. It incorporates a set of safety case patterns and a process for systematically integrating safety assurance into your development of ML components.Browse AMLAS
Our guidance provides you with proven, accessible methodologies and processes for assuring the safety of your autonomous system.
We are writing five essential pieces of guidance that will be published on this website and free to use. These will help you to create a credible and compelling assurance case for your autonomous system.Future guidance