Towards the Certification of Neural Networks using Overarching Properties: An Avionics Case Study

The traditional process-based approaches to 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 like the National Aeronautics and Space Administration (NASA...

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Bibliographic Details
Main Authors: Paul, Saswata, Prince, Daniel, Iyer, Naresh, Durling, Michael, Visnevski, Nikita, Meng, Baoluo, Varanasi, Sarat Chandra, Siu, Kit, McMillan, Craig, Meiners, Mike
Format: Conference Proceeding
Language:English
Subjects:
Aerospace electronics
Assurance
autonomy
Digital systems
FAA
Machine learning
NASA
Neural networks
Power supplies
trustworthy AI
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Summary:The traditional process-based approaches to 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 like the National Aeronautics and Space Administration (NASA) and the Federal Aviation Administration (FAA) are evaluating an alternative Means of Compliance (MoC) called the Overarching Properties (OP). We propose a novel foundation for the application of OPs to support the certification of complex aerospace digital systems consisting of AI/ML-based components. Our approach utilizes well-defined argument structures, that are justified by premises specialized to the AI/ML domain, to logically claim that an AI/ML-based component will possess the OPs. To motivate our work, we execute the design process of a Recorder Independent Power Supply (RIPS) system that provides several minutes of backup power to the data recorder when an aircraft loses access to its standard power supply.
ISSN:2155-7209
DOI:10.1109/DASC58513.2023.10311280