Coding, Proofreading and Related Clerks

← The GenAI exposure gradient

On the International Labour Organization's 2025 global study, the 5 task statements that define Coding, Proofreading and Related Clerks (ISCO-08 4413) score an average of 0.51 on a 0–1 exposure scale — more exposed than about 89% of the 427 placed occupations. Roughly 100% of its tasks fall somewhere on the exposed part of the gradient, and the typical task lands in the Gradient 3 band.

Exposure is task overlap, not a verdict. A high score means a generative-AI model can do part of the content of these tasks — it says nothing about whether the work is automated, whether anyone uses AI for it today, or whether jobs are lost. The gradient is scored on the international ISCO-08 system; the rest of Singulariki is U.S. O*NET/SOC, bridged below by an approximate, many-to-many crosswalk.

How its tasks split across the gradient

Each of the 5 scored tasks for this occupation, sorted into the six exposure bands — cool (human ground) to hot (almost fully assistable).

The most-exposed task

“Converting information into codes and classifying information by codes for data-processing purposes;”

Scores 0.70 on the 2025 scale. The task of converting information into codes and classifying information by codes for data-processing purposes is highly compatible with automation through Generative AI, as it involves repetitive and structured data management activities. This is similar to tasks such as "Coding information in accordance with the designed data processing program" (score: 0.78), which demonstrates high potential for automation due to AI's capability in handling structured coding and data classification. Other related tasks, like "Transferring data from a microcomputer to a bilingual system" (score: 0.625), further support this by indicating AI's strength in data transfer and processing operations. However, these tasks still require some human oversight to ensure accuracy and deal with exceptions, as seen in tasks like "Operating computerized passport record systems" (score: 0.715), which involve routine data operations but include elements that benefit from human judgment. Given Poland's robust digital infrastructure and the nature of the task being structured and primarily rule-based, an adjusted score of 0.615 reflects AI's significant capability to automate the majority of the task while acknowledging the need for human intervention in complex or ambiguous cases.

Moving fastest, 2023 → 2025

“Sorting documents for filing or to collate sets of pages;”

Model capability on this task changed by +0.25 in two years — the gradient is not static, it is filling in.

U.S. occupations this maps to

The American O*NET/SOC roles that crosswalk to ISCO-08 4413, biggest by employment first, via the published (approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 correspondence. These are the closest U.S. matches — not an asserted one-to-one identity.

• Proofreaders and Copy Markers

In context

Part of the 4 - Clerical support workers major group. Return to the full gradient to see how the whole group sits.

Coding, Proofreading and Related Clerks sit at the 89th percentile of the global GenAI exposure gradient

• Across 427 international occupations scored by the ILO, Coding, Proofreading and Related Clerks rank in the 89th percentile for GenAI task exposure — overlap with what generative AI can attempt, not a projection of displacement. (ILO / Gmyrek et al. (2025) GenAI exposure gradient)
• About 100% of this occupation's tasks fall into an exposed gradient band. (ILO / Gmyrek et al. (2025))
• Mean task exposure fell by 0.14 between the 2023 and 2025 model-capability snapshots. (ILO / Gmyrek et al. (2025), 2023→2025)
• Its most-exposed task: "Converting information into codes and classifying information by codes for data-processing purposes;". (ILO / Gmyrek et al. (2025))

Source: Singulariki — "Coding, Proofreading and Related Clerks". https://singulariki.com/gradient/4413-coding-proofreading-and-related-clerks.html
Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.