Assurance Guidance for Machine Learning in a Safety-Critical System

被引：2

作者：

Feather, Martin S. ^{[1
]}

Slingerland, Philip C. ^{[2
]}

Guerrini, Steven ^{[1
]}

Spolaor, Max ^{[2
]}

机构：

[1] CALTECH, Jet Prop Lab, Pasadena, CA 91125 USA

[2] Aerosp Corp, El Segundo, CA 90245 USA

来源：

2022 IEEE INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING WORKSHOPS (ISSREW 2022) | 2022年

基金：

美国国家航空航天局;

关键词：

assurance; guidance; machine learning; safety;

D O I：

10.1109/ISSREW55968.2022.00098

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

We are developing guidance for space domain assurance personnel on how to assure Artificial intelligence (AI) and Machine Learning (ML) systems. Key to such guidance will be an assurance process for these personnel, who may be unfamiliar with such systems, to follow. We are investigating one such process, the "Assurance of Machine Learning in Autonomous Systems (AMLAS)" from the University of York, UK. To gauge its suitability, we are (retrospectively) applying it to a safety critical AI/ML system in the space domain. We report here on our experience so far in applying this process.

引用

页码：394 / 401

页数：8

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