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- W3097884524 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." @default.
- W3097884524 created "2020-11-09" @default.
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- W3097884524 date "2020-01-01" @default.
- W3097884524 modified "2023-10-16" @default.
- W3097884524 title "A Survey on Methods for the Safety Assurance of Machine Learning Based Systems" @default.
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- W3097884524 doi "https://doi.org/10.20378/irb-47275" @default.
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