United States Department of Transportation
i
Assurance of Machine Learning-Based Aerospace Systems: Towards an Overarching Properties-Driven Approach
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2023-09-01
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[PDF-2.86 MB]
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Edition:Phase I Report
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Abstract:Traditional process-based approaches of certifying aerospace digital systems are not sufficient to address the challenges associated with using Artificial Intelligence (AI) or Machine Learning (ML) techniques. To address this, agencies are evaluating an alternative Means of Compliance (MoC) called the Overarching Properties (OP). The goals for this research are to develop recommendations and assurance criteria and to explore safety risk mitigation approaches for such AI/ML-based software systems. This document outlines a novel foundation for the application of OPs to support the assurance and certification of complex aerospace digital systems consisting of AI/ML-based components. To this end, we first select the use case of a Recorder Independent Power Supply (RIPS) system. We then perform a Functional Hazard Assessment (FHA) to identify a set of hazards associated with the RIPS and design a set of appropriate requirements to mitigate those hazards.
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