AI Capability vs Verification
Confine AI to bounded domains with deterministic safeguards and runtime monitoring, expanding authority only as empirical evidence accumulates.
CyberTRIZ analysis · Space contradiction TSI003 · one of 8,235 worked contradictions published by CyberTRIZ.AI
Regulations
Business Context
Artificial intelligence can improve autonomous navigation, anomaly detection, payload interpretation, resource optimization, robotic operations, and mission planning. More capable AI systems, however, may exhibit behavior that is difficult to characterize exhaustively across all possible operating conditions. Increasing AI sophistication can therefore improve performance while making verification and assurance more difficult.
Space TRIZ Resolution
AI should be introduced within bounded functional domains where inputs, outputs, authority, and failure consequences can be controlled. Deterministic safeguards can supervise AI-generated actions, while simulation, scenario testing, runtime monitoring, and independent validation provide complementary assurance. AI capability can expand progressively as evidence accumulates.
Applicable TRIZ Principles
Principle 1 – Segmentation confines AI functions to defined operational domains.
Principle 11 – Beforehand Cushioning establishes deterministic safeguards against unacceptable AI actions.
Principle 23 – Feedback monitors AI behavior during operation and uses evidence to refine allowable authority.
Expected Outcome
Greater usable AI capability
Stronger verification confidence
Reduced risk from unexpected behavior
Controlled expansion of autonomous intelligence
Decision Indicators
Early indicators include:
AI functionality cannot be connected to clear verification criteria.
Increasing model capability expands behavior beyond validated conditions.
AI outputs directly control critical systems without independent safeguards.
Verification relies primarily on nominal test cases.
Useful AI functions are rejected because assurance boundaries are undefined.