Safety through Machine Learning Applications

Safety through Machine Learning Applications: A Safety Case Analysis

Jacobs, Freek (TU Delft Technology, Policy and Management)

Verbraeck, Alexander (mentor)
Cunningham, Scott (mentor)
van Gelder, Pieter (mentor)

Delft University of Technology

2018-10-26

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.

Machine Learning
Safety
Risk
Uncertainty
safety assessment

http://resolver.tudelft.nl/uuid:ce5c73ef-8ad0-426f-926e-7d7ef3e197c3

Student theses

master thesis

© 2018 Freek Jacobs