A Survey on Methods for the Safety Assurance of Machine Learning Based Systems
Faculty/Professorship: | Cognitive Systems |
Author(s): | Schwalbe, Gesina ![]() |
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 |
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. |
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 |

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University of Bamberg