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AI-assisted measurement research tackles disagreement in administrative text analysis

By PulseAugur Editorial · [1 sources] ·

A new research paper explores methods for accurately measuring monetary totals from administrative texts when AI readers disagree. The study, using US tax filings, proposes dollar-weighted measurement, selective machine labeling, and budgeted human review to improve accuracy. Results indicate that a two-phase review policy can significantly reduce the root mean square error of the total compared to proportional sampling, though error control in deployment is not yet established. AI

IMPACT Introduces new techniques for improving the reliability of AI-driven data analysis in administrative contexts.

RANK_REASON Academic paper on a novel methodology for AI-assisted measurement. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI-assisted measurement research tackles disagreement in administrative text analysis

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Academic paper on a novel methodology for AI-assisted measurement. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · George Koomullil (Ascendr, PBC) ·

    Counting Under Disagreement: Dollar-Weighted Measurement, Selective Machine Labelling and Budgeted Human Review from Disagreeing AI Readers of Administrative Text

    arXiv:2611.13215v1 Announce Type: new Abstract: We study AI-assisted measurement when one text label applies to many administrative records, the target is a monetary total, and the human readings that anchor the machines disagree. The testbed is 121,070 grant-purpose texts from U…

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