AI Distributional Shift

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  "title": "AI Distributional Shift",
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  "dateCreated": "2026-02-15",
  "summary": "Comprehensive analysis of distributional shift showing 40-45% accuracy drops when models encounter novel distributions (ObjectNet vs ImageNet), with 5,202 autonomous vehicle accidents and 15-30% medical AI degradation across hospitals documented through 2025. Current OOD detection achieves 60-92% accuracy depending on method, with benchmark gaps persisting despite significant research investment (\\$50-100M annually). Fundamental uncertainties remain about whether scale solves robustness, with MIT 2024 research showing fairness debiasing fails to transfer across institutions.",
  "description": "When AI systems fail due to differences between training and deployment contexts. Research shows 40-45% accuracy drops when models encounter novel distributions (ObjectNet vs ImageNet), with failures affecting autonomous vehicles, medical AI, and deployed ML systems at scale.",
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