Guidance on the Assurance of Machine Learning in Autonomous Systems (AMLAS)

Abstract

Machine Learning (ML) is now used in a range of systems with results that are reported to exceed, under certain conditions, human performance. Many of these systems, in domains such as healthcare , automotive and manufacturing, exhibit high degrees of autonomy and are safety critical. Establishing justified confidence in ML forms a core part of the safety case for these systems. In this document we introduce a methodology for the Assurance of Machine Learning for use in Autonomous Systems (AMLAS). AMLAS comprises a set of safety case patterns and a process for (1) systematically integrating safety assurance into the development of ML components and (2) for generating the evidence base for explicitly justifying the acceptable safety of these components when integrated into autonomous system applications.

References in corpus (2)

• Towards A Rigorous Science of Interpretable Machine Learning
• Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

Cited by in corpus (6)

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• A Trustworthiness Score to Evaluate DNN Predictions
• Model predictivity assessment: incremental test-set selection and accuracy evaluation
• An NCAP-like Safety Indicator for Self-Driving Cars
• Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning
• Sample selection from a given dataset to validate machine learning models