A long-form article introducing the Twin Test: a practical standard for high-stakes machine learning where models must show nearest “twin” examples, neighborhood tightness, mixed-vs-homogeneous evidence, and “no reliable twins” abstention. Argues similarity and evidence packets beat probability scores for trust and safety.
product-design machine-learning risk uncertainty calibration nearest-neighbors knn governance human-in-the-loop interpretability similarity-search case-based-reasoning explainable-ai ai-ethics mlops out-of-distribution responsible-ai data-drift high-stakes decision-systems
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Updated
Dec 26, 2025