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Artificial Intelligence in Aeronautical Systems: Statement of Concerns
- Aerospace Standard
- AIR6988
- Issued
Downloadable datasets available
Annotation ability available
Sector:
Issuing Committee:
Language:
English
Scope
This document reviews current aerospace software, hardware, and system development standards used in the certification/approval process of safety-critical airborne and ground-based systems, and assesses whether these standards are compatible with a typical Artificial Intelligence (AI) and Machine Learning (ML) development approach. The document then outlines what is required to produce a standard that provides the necessary accommodation to support integration of ML-enabled sub-systems into safety-critical airborne and ground-based systems, and details next steps in the production of such a standard.
Rationale
In order to provide a means of compliance for the certification of Artificial Intelligence (AI) within safety critical aeronautical systems, the committee must first review existing standards and perform a gap analysis to understand how and why existing standards cannot be reliably used. This document serves as that gap analysis and provides a list of concerns that need to be addressed in order to produce a future means of compliance.
Topic
Data Sets - Support Documents
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| Table 1 | Example use cases for aircraft systems | |
| Table 2 | Air space traffic management use-cases | |
| Table 3 | Ground traffic management use cases | |
| Table 4 | Airworthiness use cases | |
| Table 5 | Air traffic communications use cases | |
| Table 6 | Remote ID from NAS infrastructure use cases | |
| Table 7 | Mixed man and unmanned operations use case | |
| Table 8 | Predictive maintenance use cases |
Issuing Committee
G-34 Artificial Intelligence in Aviation
The G-34 Artifical Intelligence in Aviation, Committee is responsible for creating and maintaining SAE Technical Reports (i.e. Aerospace Information Reports, Aerospace Recommended Practices, and Aerospace Standards) on the implementation and certification aspects relate to AI technologies inclusive of any on or off-board system for the safe operation of aerospace systems and aerospace vehicles.
Reference
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