Distilling Expert Judgment at Scale

The paper proposes a method to capture expert human judgment and reasoning for automated evaluation systems that can scale across high‑stakes domains such as healthcare, defense, finance, governance, and politics. By assembling expert networks and encoding their reasoning into structured editorial standards, the authors demonstrate that automated judges trained on these standards outperform uncalibrated frontier models on source‑quality and neutrality metrics in AI news reporting. The study validates the approach with strong inter‑annotator agreement and high F1 scores, suggesting a viable framework for expert‑calibrated evaluation in any domain where expertise is scarce.

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