arXiv:2605.07409v2 Announce Type: replace Abstract: Natural Language Processing is rapidly evolving into a primary instrument for Computational Social Science, with researchers increasingly using embeddings to measure latent constructs such as novelty, creativity, and bias. However, this transition faces a fundamental validity challenge: the ''Proxy Presumption,'' or the reliance on geometric properties (e.g., cosine distance) as direct measures of social concepts. We argue that without explicit validation, unsupervised representations remain entangled mixtures of the target construct ($C$) an
The rapid adoption of NLP in computational social science makes addressing fundamental validity challenges critical before these tools are widely deployed and trusted.
A strategic reader should care because unchecked assumptions in AI-driven social analysis can lead to flawed policy decisions, misallocated resources, and a misunderstanding of societal dynamics.
This research calls into question the simplistic reliance on geometric properties of embeddings, encouraging more rigorous validation and a nuanced approach to using AI for social measurement.
- · Ethical AI developers
- · Social scientists with strong methodological backgrounds
- · Organizations relying on validated AI insights
- · Developers of unvalidated AI social measurement tools
- · Researchers employing AI without critical evaluation
- · Decision-makers relying on unscrutinized AI outputs
The paper highlights a crucial methodological flaw in the application of NLP to social science, specifically regarding the 'Proxy Presumption'.
This could lead to a re-evaluation of existing AI-driven social studies and a demand for more robust validation frameworks for generative AI applications.
Ultimately, this shift towards validated AI applications could foster greater trust in AI for critical social analysis, or conversely, lead to skepticism if the core issues remain unaddressed.
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Read at arXiv cs.CL