Model Driven Engineering for Resilience of Systems with Black Box and AI-based Components

Nikolaos Papakonstantinou, Britta Hale, Joonas Linnosmaa, Jarno Salonen, Douglas L.Van Bossuyt

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

Abstract

Modern complex cyber-physical systems heavily rely on humans and AI for mission-critical operations and decision making. Unfortunately, these components are often 'black boxes' to the operator, either because the decision models are too complex for human comprehension (e.g. deep neural networks) or are intentionally hidden (e.g. proprietary intellectual property). In these cases, the decision logic cannot be validated and therefore trust is forced.

Original languageEnglish
Title of host publication68th Annual Reliability and Maintainability Symposium, RAMS 2022
PublisherIEEE Institute of Electrical and Electronic Engineers
Number of pages7
ISBN (Electronic)9781665424325
DOIs
Publication statusPublished - 20 Sep 2022
MoE publication typeA4 Article in a conference publication
Event68th Annual Reliability and Maintainability Symposium, RAMS 2022 - Tucson, United States
Duration: 24 Jan 202227 Jan 2022

Publication series

SeriesProceedings - Annual Reliability and Maintainability Symposium
Volume2022-January
ISSN0149-144X

Conference

Conference68th Annual Reliability and Maintainability Symposium, RAMS 2022
Abbreviated titleRAMS 2022
Country/TerritoryUnited States
CityTucson
Period24/01/2227/01/22

Keywords

  • AI
  • Black Box Components
  • Defense in Depth
  • Model Driven Engineering
  • Resilience
  • Safety
  • Security

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