Assurance of AI/ML-Based Aerospace Systems Using Overarching Properties

Artificial Intelligence/Machine Learning (AI/ML) is a growing field that has potential for widespread usage in the aerospace industry. However, the traditional process-based approaches for aerospace systems certification fall short of addressing the uncertainties and complexities associated with AI/...

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Bibliographic Details
Main Authors: Paul, Saswata, Iyer, Naresh, Prince, Daniel, Tang, Liang, Durling, Michael, Meiners, Mike, Meng, Baoluo, Visnevski, Nikita, Mandal, Udayan
Format: Conference Proceeding
Language:English
Subjects:
Aerospace industry
Artificial Intelligence
Assurance
Assurance Case
Certification
Digital Avionics
FAA
Government
GSN
Learning (artificial intelligence)
Machine Learning
NASA
Power supplies
Terminology
Transportation
Uncertainty
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Summary:Artificial Intelligence/Machine Learning (AI/ML) is a growing field that has potential for widespread usage in the aerospace industry. However, the traditional process-based approaches for aerospace systems certification fall short of addressing the uncertainties and complexities associated with AI/ML technologies. This paper presents the results of applying a novel Overarching Properties (OP)-based approach for the assurance of complex digital aerospace systems that contain AI/ML-based subcomponents. The OPs are being evaluated by the FAA and NASA as a foundation for developing an alternate means of compliance (MOC) for the certification of aerospace systems. The OP-based approach evaluated in this paper uses structured premise-based arguments, where the premises are designed to address the different aspects of AI/ML technologies with respect to system-level safety and design needs. The structured arguments align the low-level properties of AI/ML components to system-level properties by using the three OPs labeled - Intent, Correctness, and Innocuity - making it easy to logically establish the safety and correctness of AI/ML-based digital aerospace systems. We use a Recorder Independent Power Supply (RIPS) example, that contains AI/ML-based components, to evaluate the OP-based assurance approach. We describe in detail the process of generating verification and validation evidence to support the arguments and presenting the evidence using assurance cases.
ISSN:2155-7209
DOI:10.1109/DASC62030.2024.10749102