<ul><li>AI systems in high-consequence domains need to detect rare, high-impact events while operating under tight constraints.</li><li>Traditional annotation strategies may introduce noise, limiting model generalization.</li><li>Smart-sizing training data strategy introduced, emphasizing label diversity and model-guided selection.</li><li>Experiments show that models trained on 20 to 40 percent of curated data can match or exceed full-data baselines, especially in rare-class recall and edge-case generalization.</li></ul>

The Achilles Heel of AI: Fundamentals of Risk-Aware Training Data for High-Consequence Models

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