Challenges in Using Neural Networks in Safety-Critical Applications

In this paper, we discuss challenges when using neural networks (NNs) in safety-critical applications. We address the challenges one by one, with aviation safety in mind. We then introduce a possible implementation to overcome the challenges. Only a small portion of the solution has been implemented...

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Bibliographic Details
Published in:	2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC) pp. 1 - 7
Main Authors:	Forsberg, Hakan, Linden, Joakim, Hjorth, Johan, Manefjord, Torbjorn, Daneshtalab, Masoud
Format:	Conference Proceeding
Language:	English
Published:	IEEE 11-10-2020
Subjects:	Artificial neural networks avionics Biological neural networks deep neural networks machine learning Monitoring Safety safety-critical Sensors Software Uncertainty
Online Access:	Get full text
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Summary:	In this paper, we discuss challenges when using neural networks (NNs) in safety-critical applications. We address the challenges one by one, with aviation safety in mind. We then introduce a possible implementation to overcome the challenges. Only a small portion of the solution has been implemented physically and much work is considered as future work. Our current understanding is that a real implementation in a safety-critical system would be extremely difficult. Firstly, to design the intended function of the NN, and secondly, designing monitors needed to achieve a deterministic and fail-safe behavior of the system. We conclude that only the most valuable implementations of NNs should be considered as meaningful to implement in safety-critical systems.
ISSN:	2155-7209
DOI:	10.1109/DASC50938.2020.9256519