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Moderate evidence

GainPublished recordLaborGood Space

TaskNet: Building Occupational Task Inventories from Worker Interviews at Scale

Truvace record
TRV-2026-2484raw data (JSON)
Version
v1Published
First published
Evidence type
Controlled or validated comparisonPreprint, not yet peer reviewed
Sources
1 sourcePeer-reviewed
sha256
1d59610159f2…check it

Understanding how AI is reshaping work requires an accurate account of the tasks workers perform. Workers are often the first to see how their jobs are changing, as new responsibilities and tools often appear in everyday work before these changes reach formal descriptions. Interviews with workers can help capture these emerging changes in job tasks. Yet existing methods support standardized ratings across workers, while open-ended conversations with workers remain costly to collect and synthesize at scale. We in…

The gain

In a study of 132 workers in six occupations, an AI interviewer produced 202 task statements that all passed O*NET analyst review and covered 89% of tasks reported by held-out workers, versus 79% for O*NET.

What this doesn’t fix

Workers were recruited through a single AI data platform, about twenty per occupation and not matched to the U.S. workforce, and the design cannot separate why TaskNet's exposure estimates differ from O*NET's.

Sources

1 on file
  1. NBER Working Paperdoi:10.3386/w35987
    Peer-reviewed2026-11-01

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v1

Truvace Impact Record TRV-2026-2484, v1: “TaskNet: Building Occupational Task Inventories from Worker Interviews at Scale.” Truvace, 2026-11-12. /record/TRV-2026-2484 (accessed on the day you copy it). sha256 1d59610159f287ca…

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