Information Clerks
United States · BLS Employment Projections 2024–34 (figures for SOC 43-4199 occupational group)
AI exposure in published research
Figures below are reproduced from external datasets without modification. Where a dataset does not cover this occupation, the value is shown as — rather than as zero.
Data provider: OpenAI · "GPTs are GPTs"
Time basis: 2023 baselineex-ante estimate
Scored against GPT-4-generation capability.
- Human rater basis
- —
- GPT-4 rater basis
- —
This occupation code is not included in this dataset.
This occupation code is not included in this dataset.
β = direct exposure (E1) + 0.5 × exposure when tools are available (E2), per the source repository's definition.
— This occupation code is not included in this dataset.
- License:
- MIT License · Copyright (c) 2024 OpenAI
Data provider: Anthropic Economic Index
Time basis: Varies by releasecomposite index
- Observed exposure
- —
This occupation code is not included in this dataset.
Theoretical exposure index weighted by measured Claude usage, per the original report's definition.
— This occupation code is not included in this dataset.
Data: Anthropic Economic Index — labor_market_impacts, CC-BY, https://huggingface.co/datasets/Anthropic/EconomicIndex
AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.
- License:
- CC BY 4.0
Theoretical estimate vs observed usage
One card (GPTs are GPTs) estimates what AI could theoretically do at the time it was scored; the other (Anthropic Economic Index) observes how AI was actually used. They measure different things, so the two figures cannot be added, averaged, or ranked against each other. The older figure is kept here as a baseline for comparison rather than removed.
Caution: the Anthropic figures — observed exposure and task penetration — take the Eloundou β as one of their inputs. The two sides resembling each other is therefore not evidence that the earlier prediction came true; reading it that way is circular reasoning.
The Anthropic figures are measured on Claude users, who are not the whole economy and not the whole workforce.
The mapping of O*NET tasks and occupation codes was performed by AI Changing Work. The source figures themselves were not modified.
These indices are not forecasts. Which point in time each one belongs to is stated on the badge on its card.
Task Breakdown
- Respond to inquiries via phone and email
- Maintain information databases
- Direct visitors and provide directions
About This Occupation
If you work as a Information Clerks, AI is transforming your role. Risk 48/100, exposure 58%.
ISCO-08 classification
Client Information Workers Not Elsewhere Classified
Indirect mapping — this occupation is linked to the ISCO unit group by a rule-based fallback.
Definition
ILO original text (English)
This unit group includes client information workers not included elsewhere in Minor Group 422: Client information workers. For instance, it includes workers who obtain and process information from clients needed to determine eligibility for services.
Definition & vocabulary source
Source: International Labour Organization (ILO) — ISCO-08 Structure
License: ILO CC BY 4.0
Official occupational information
- ONET43-4199.00
Information and Record Clerks, All Other
All information and record clerks not listed separately.
View original
Frequently Asked Questions
They come from two published datasets: the Anthropic Economic Index (labor_market_impacts, CC BY 4.0) and the OpenAI "GPTs are GPTs" exposure rubric (MIT License, Copyright (c) 2024 OpenAI). AI Changing Work maps them onto O*NET occupation and task codes and does not calculate exposure scores of its own. AI Changing Work uses Anthropic Economic Index data under CC-BY; Anthropic does not endorse or sponsor this site or its analyses.
No. They are a diagnosis of exposure as measured at the time each source dataset was published. AI Changing Work publishes no prediction of future automation or job displacement for this occupation.