Helping you assure the safety of your autonomous system
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 covers the core technical issues that must be considered for the safe development and introduction of an autonomous system.
Despite the interest from government and industry, there is currently no established process for certifying and assuring an AI component. AMLAS fills this gap by providing us with a framework to integrate safety assurance into our development process and build a compelling argument for our safety case. We are very much looking forward to trialling the AMLAS process in our upcoming flight controller development project.
Co-Founder and Chief Executive Officer, Luffy AI
AMLAS has made an important contribution to safety case patterns and provided valuable guidance on how to proceed to justify the safety of machine learning components.Research Engineer, Robert Bosch GmbH
The AMLAS methodology really helped to shape our thinking and approach to the assurance of ML/AI. The lifecycle structure mirrors our development phases and it is well presented and easy to read. AMLAS helped me to structure my thinking about strategies for verification and validation and the examples given are useful.Systems/Software Engineer, Critical Systems Labs Inc
Safe autonomous systems
Our latest guidance supports system developers, safety engineers, and assessors to design and introduce safe autonomous systems.
The Safety Assurance of autonomous systems in Complex Environments (SACE) methodology defines a detailed process for creating a safety case for autonomous systems. It considers overall system-level assurance activities, defining a safety process and corresponding safety case patterns.
Browse SACE
Safe machine learning
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
