Reproducibility is not construct validity: LLM measurement of institutionally situated communication

arXiv cs.AI Papers

Summary

This paper tests the distinction between reproducibility and construct validity in LLM annotations, using data from the European Commission's AI Act consultation to demonstrate high reproducibility but limited convergence with survey measures, with systematic variations across stakeholder groups.

arXiv:2609.19866v1 Announce Type: new Abstract: High annotation reproducibility does not necessarily imply that an LLM-inferred measure captures the construct it is intended to measure. We test this distinction using a dataset from the European Commission's AI Act consultation, linking structured survey responses to free-text consultation submissions from the same stakeholders. LLM annotations of consultation submissions are highly reproducible (intraclass correlations > 0.99), yet show limited convergence with survey-reported measures of the nominal construct they were intended to approximate. Divergence between survey-and LLM-inferred text-based measures varies systematically across stakeholder groups: business associations express greater concern about AI risks in text-based consultations than in survey responses ({\=g} = +1.0), whereas public authorities and several nonbusiness groups show smaller or negative divergences. Divergences between scores suggest positive spatial autocorrelation across European countries (Moran's I = 0.347, p = 0.036), indicating that stakeholders from neighboring countries tend toward more similar text-based stances towards AI safety concerns. Despite divergence, survey-reported concerns remain strongly associated with support for explainability across all divergence levels. These results demonstrate that LLM annotation reproducibility can coexist with poor construct correspondence and motivate validation procedures that distinguish reproducibility, construct validity, and communication context variation when LLMs are used as measurement instruments.
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# Reproducibility is not construct validity: LLM measurement of institutionally situated communication
Source: [https://arxiv.org/abs/2609.19866](https://arxiv.org/abs/2609.19866)
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> Abstract:High annotation reproducibility does not necessarily imply that an LLM\-inferred measure captures the construct it is intended to measure\. We test this distinction using a dataset from the European Commission's AI Act consultation, linking structured survey responses to free\-text consultation submissions from the same stakeholders\. LLM annotations of consultation submissions are highly reproducible \(intraclass correlations \> 0\.99\), yet show limited convergence with survey\-reported measures of the nominal construct they were intended to approximate\. Divergence between survey\-and LLM\-inferred text\-based measures varies systematically across stakeholder groups: business associations express greater concern about AI risks in text\-based consultations than in survey responses \(\{g\} = \+1\.0\), whereas public authorities and several nonbusiness groups show smaller or negative divergences\. Divergences between scores suggest positive spatial autocorrelation across European countries \(Moran's I = 0\.347, p = 0\.036\), indicating that stakeholders from neighboring countries tend toward more similar text\-based stances towards AI safety concerns\. Despite divergence, survey\-reported concerns remain strongly associated with support for explainability across all divergence levels\. These results demonstrate that LLM annotation reproducibility can coexist with poor construct correspondence and motivate validation procedures that distinguish reproducibility, construct validity, and communication context variation when LLMs are used as measurement instruments\.

## Submission history

From: Carlo Romano Marcello Alessandro Santagiustina \[[view email](https://arxiv.org/show-email/a1ec6f39/2609.19866)\] \[via CCSD proxy\] **\[v1\]**Thu, 17 Sep 2026 08:19:58 UTC \(1,675 KB\)

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