Background

Plainworkforce is a data journalism portal that analyzes occupations in the AI workforce using public government data. Content is compiled by our editorial team from BLS Occupational Employment Statistics, O*NET, and BEA source files and reviewed against the original records before publication. The team does not accept payment from entities it covers to ensure unbiased reporting. As part of Kiznis Studio, the portal provides neutral insights into workforce trends and their implications.

Editorial approach

The PlainWorkforce Editorial editorial process follows plainworkforce's Editorial Standards for Data Journalism. Every dataset we cover is traced back to its originating public source — federal agencies, state registries, or authoritative third parties — and we publish the provenance of each statistic so readers can verify it independently. We do not accept compensation, sponsorship, or influence from entities we cover.

When we present derived numbers (rankings, ratios, comparisons), the methodology page documents exactly how the figure was computed. If a number cannot be computed consistently across every entity in the dataset, we either disclose the gap or omit the comparison rather than present misleading data.

How content is produced

Data ingestion and computation are handled by deterministic pipelines — every numeric value, ranking, and ratio shown on a data page comes directly from the upstream source agency, unmodified. Numbers are never estimated or interpolated. Where we draw data from a primary public dataset, the figure is reproduced exactly as the source published it, and the originating dataset is named and dated on the page. Plain-language summaries, explanations, and editorial framing are drafted from the source data by our editorial team and reviewed against the official record for accuracy before publication.

Source attribution is shown on every page; the methodology underlying any derived figure is documented at the link above. We follow the guidance of Google Search Central's Helpful Content principles — write for readers, document sources, and disclose the editorial process — and welcome any factual flag at the contact link below.

Corrections and feedback

If you find an error, stale figure, or missing context on any page we publish, please use the contact page or write to hello@plainworkforce.com with the URL and the issue. We aim to respond within 72 hours and to publish corrections with a visible revision note.

Areas of focus

• ai workforce
• occupation data
• public dataset analysis

Contact and identity

• Email: hello@plainworkforce.com
• Publisher: https://plainworkforce.com