Meridian's evaluation team struggles to find enough real historical examples of rare, severe ramp-safety incidents to fully test the computer-vision model against. How might generative AI be used, consistent with CPMAI's view of generative AI as a project accelerator, to strengthen the Model Evaluation phase here?

CPMAI treats generative AI as an accelerator woven across the six-phase lifecycle, and Model Evaluation is a legitimate place for it: when real historical examples of rare, severe events are scarce — as they typically are for safety incidents by their very nature — generative AI can help produce realistic synthetic test scenarios (varied lighting conditions, unusual debris shapes, uncommon ramp-activity patterns) that supplement limited real data and give the evaluation broader edge-case coverage than the historical record alone provides. Saying generative AI has no role in evaluation contradicts CPMAI's explicit framing of GenAI as a cross-phase accelerator, not a tool confined to early phases. Using generative AI to fabricate fake incident reports and present them as genuine historical events is a serious integrity violation — synthetic test data must be clearly labeled and used transparently to broaden test coverage, never disguised as real events to inflate perceived evaluation completeness. Replacing the entire evaluation and Go/No-Go process with an AI-written report abandons actual model testing altogether, which defeats the entire purpose of the Model Evaluation phase and would be reckless for a safety-relevant system. The exam point: generative AI's legitimate accelerator role in evaluation is generating supplementary synthetic test material, transparently labeled as such — not fabricating evidence or replacing genuine testing.