Targeted Data for AI Safety and Verified Usecases
Working Model for AI Safety: Verified Usecases
Whitepaper Series – Applied Philosophy of Systems Engineering
Targeted Data for AI Safety and Verified Usecases
Targeted Data for AI Safety begins with a requirements-driven premise: data should not be collected first and justified later. In a safety-critical system, the requirement defines the intended function, the Usecase defines the question to be answered, and the operating envelope defines the conditions under which the answer is valid.
Safety-critical AI should not begin with “more data.”
It should begin with requirements.
This article continues Book III of my Applied Philosophy of Systems Engineering series by examining targeted data as a design constraint.
The central idea is simple:
Data is not discovered after the fact.
It is defined by the requirement, the Usecase, the operating envelope, and the knowledge needed for execution.
For AI-enabled safety systems, targeted data becomes the bridge between sensing and verified function.
Occupant Sensing is used as the demonstration platform, but the approach applies more broadly to passive safety, active safety, autonomy, software-defined vehicles, and other sensing-based vehicle functions.
Part of the Applied Philosophy of Systems Engineering whitepaper series.
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#SystemsEngineering #TargetedData #AISafety #Usecases #VehicleSafety #Verification #SafetyCriticalSystems
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