A Survey on Methods for the Safety Assurance of Machine Learning Based Systems





Faculty/Professorship: Cognitive Systems  
Author(s): Schwalbe, Gesina  ; Schels, Martin
Corporate Body: European Congress on Embedded Real Time Software and Systems (ERTS), 10, 2020, Toulouse
Publisher Information: Bamberg : Otto-Friedrich-Universität
Year of publication: 2020
Pages: 11
Language(s): English
DOI: 10.20378/irb-47275
Licence: Creative Commons - CC BY-NC-SA - Attribution - NonCommercial - ShareAlike 4.0 International 
URL: https://fis.uni-bamberg.de/handle/uniba/47326
URN: urn:nbn:de:bvb:473-irb-472752
Abstract: 
Methods for safety assurance suggested by the ISO 26262 automotive functional safety standard are not sufficient for applications based on machine learning (ML). We provide a structured, certification oriented overview on available methods supporting the safety argumen-tation of a ML based system. It is sorted into life-cycle phases, and maturity of the approach as well as applicability to different ML types are collected. From this we deduce current open challenges: powerful solvers, inclusion of expert knowledge, validation of data representativity and model diversity, and model introspection with provable guarantees.
SWD Keywords: ISO/DIS 26262 ; Funktionssicherheit ; Maschinelles Lernen ; Künstliche Intelligenz
Keywords: functional safety, life-cycle, ISO 26262, machine learning, explainable AI
DDC Classification: 004 Computer science  
RVK Classification: ST 300   
Peer Reviewed: Ja
International Distribution: Ja
Open Access Journal: Ja
Document Type: Conferenceobject
URI: https://fis.uni-bamberg.de/handle/uniba/47275
Release Date: 3. July 2020

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