A Survey on Methods for the Safety Assurance of Machine Learning Based Systems
Gesina Schwalbe, Martin Schels · 2020
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 argumentation 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.