Distilling Expert Judgment at Scale

arXiv:2610.33233v1 Announce Type: new Abstract: Frontier AI models are increasingly deployed on topics where the stakes are high and expert judgment is required, including healthcare, defense, finance, governance, and politics. We present a general methodology for distilling expert human judgment into automated evaluation systems that operate at scale. Our approach assembles networks of domain experts and develops techniques for extracting not just their conclusions but their reasoning processes, encoding those processes into structured editorial standards that automated judges can apply continuously. We ground our approach in a long tradition of research on the nature of expertise and the conditions under which expert judgment is reliable and more effective than other approaches, and argue that independent, expert-calibrated evaluation is necessary not only on epistemic grounds but as a matter of accountability and public trust. We validate the methodology on AI news reporting as a first application domain. On source quality, two independent domain experts achieve Krippendorff's $\alpha = 0.83/0.69$ on a held-out test set when applying the editorial standards, and our automated judge reaches $F1 = 0.81/0.97$ against expert consensus. On neutrality, experts achieve $\alpha = .72$ on the test set, and the automated judge reaches $F1 = 0.86$ against expert consensus. Additionally, a controlled comparison shows that expert-calibrated judges substantially outperform uncalibrated frontier models on every source-quality metric, demonstrating that encoding expert reasoning is the

The paper presents a methodology for distilling expert judgment into automated evaluation systems, which could lead to more accurate and reliable AI decision-making. However, the implications for human flourishing are uncertain and depend on the specific applications and domains where this technology is used.

The methodology improves AI evaluation by incorporating expert judgment and reasoning, enhancing accountability and trust. However, it depends on quality and availability of expert input and may not fully eliminate biases or errors. It offers incremental benefits rather than transformative changes.

This is a methodology paper proposing scalable expert-calibrated evaluation infrastructure, tested as proof of concept on AI news quality assessment. It offers a modest positive implication for AI accountability and trust tooling, but it is a narrow, unreplicated preprint with limited domain validation.

The paper presents a methodology for distilling expert human judgment into automated evaluation systems across high-stakes domains. While the stated goal is to ensure accountability and public trust through expert-calibrated evaluation, the development of AI systems capable of applying 'structured editorial standards' at scale, potentially replacing human experts, presents both opportunities and risks regarding human autonomy and expertise.

SOURCE     arXiv cs.AI (tier 1)
   ↓
DOCUMENT   92e802f4-2f23-492a-a929-acfe1719f0a7
           https://arxiv.org/abs/2610.33233
   ↓
EVIDENCE   2 extracted excerpts
   ↓
MODEL RUN  4 runs, methodology 1.0.1
   ↓
SCORE      -1  (Mixed or uncertain)
   ↓
CONFIDENCE 53%