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

Faculty/Professorship: Cognitive Systems  
Author(s): Schwalbe, Gesina ; Schels, Martin
Title of the compilation: Proceeding of the 10th European Congress on Embedded Real Time Software and Systems
Corporate Body: European Congress on Embedded Real Time Software and Systems (ERTS),10, 2020, Toulouse
Publisher Information: Toulouse
Year of publication: 2020
Pages: 11
Language(s): English
URL: https://hal.archives-ouvertes.fr/hal-02442819v1
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.
GND 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   
Type: Conferenceobject
URI: https://fis.uni-bamberg.de/handle/uniba/47326
Release Date: 11. March 2020