AIORG-W014 Government & IGO research

Generative AI and Jobs: A Refined Global Index of Occupational Exposure

ILO Working Paper 140 · refined global index

intergovernmental working paper

Recorded claims
2
coded from the inspected source
Evidence classes
1
survey-or-synthesis
Recorded limitations
4
stated, never hidden

From the wave-0 evidence ledger

Recorded claims

Each claim states what the inspected source says, at the recorded location—bounded by its scope and graded by its confidence. Nothing here is a synthesis across works.

The refined index estimates that about one in four workers globally is in an occupation with some generative-AI exposure, while emphasizing that transformation is more likely than full redundancy because most jobs retain tasks requiring human input.

confidence: medium-high for model result; low for direct outcome inference survey-or-synthesis

Scope: Global occupational exposure under the study's task and capability assumptions; not realized employment effects.

The study explicitly separates task exposure from job outcomes and points to policy, worker voice, and social dialogue as determinants of whether exposure becomes augmentation, work degradation, or displacement.

confidence: high for stated boundary; medium for policy implications survey-or-synthesis

Scope: Transition framing across occupations and countries.

Boundaries

Limitations & independence

Recorded at coding time, carried with the work forever. A claim without its limits is not evidence.

Recorded limitations

  • Exposure index is not observed adoption or displacement
  • Occupational task bundles and model capabilities evolve
  • Country/occupation data quality varies
  • Organizational responses mediate outcomes

Source independence

ILO occupational/task analysis; independent of a single employer or vendor but model-based exposure coding.