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Naoki Egami

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#artificial intelligence Preprint Aug 2026

Debiased Inference for AI-Generated Data without Gold-Standard Labels: Identification via Multiple Imperfect Measurements

This paper proposes debiased inference with multiple imperfect measurements (DMM), a framework that combines multiple error-prone AI measurements to enable valid downstream inference without gold-standard labels and proves that the DMM estimator is consistent and asymptotically normal.

Naoki Egami, Sooahn Shin · 0 citations