Safety through Machine Learning Applications

A Safety Case Analysis

Master thesis (2018)

Authors

F.A. Jacobs Technology, Policy and Management

Contributors

A. Verbraeck (supervisor 1)
S. Cunningham (supervisor 1)
P.H.A.J.M. van Gelder (supervisor 1)
Faculty
Technology, Policy and Management
Copyright: © 2018 Freek Jacobs
Safety Machine Learning Uncertainty Risk Safety assessment
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Published Date

26-10-2018

Language

English

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Faculty

Technology, Policy and Management

Abstract

Machine learning applications are increasingly being implemented in socio-technical safety-critical systems, but the safety of these applications is not well understood. This thesis used a mixed-method design and applied four methods to explore the field of machine learning and safety. With the results of these four methods, a framework was constructed for the risk analysis of machine learning applications. All findings were synthesised in two ways: by drawing conclusions about machine learning capabilities, and by drawing conclusions about organisational capabilities. This approach provided a way to think conceptually about how far we can take machine learning to increase safety in socio-technical safety-critical systems.