AIORG-W032 Government & IGO research

OECD Employment Outlook 2023: Artificial Intelligence, Job Quality and Inclusiveness

Artificial Intelligence, Job Quality and Inclusiveness · OECD Employment Outlook 2023 Chapter 4

intergovernmental synthesis chapter

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 chapter reports that AI can improve some dimensions of job quality and worker satisfaction, but workers subject to algorithmic management are among the least positive and report concerns about pace, intensity, privacy, and autonomy.

confidence: medium survey-or-synthesis

Scope: Broad AI and surveyed workplaces before widespread current GenAI; effect direction varies.

Worker consultation, training, and bargaining institutions appear repeatedly as transition conditions in the chapter, but the available evidence does not supply a universal causal effect size for any one participation mechanism.

confidence: medium survey-or-synthesis

Scope: Workplace transition and job quality across OECD settings.

Boundaries

Limitations & independence

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

Recorded limitations

  • Much underlying evidence is cross-sectional/self-reported
  • Broad AI rather than current GenAI only
  • Country, worker, and tool heterogeneity
  • Synthesis cannot convert associations into causal effects

Source independence

OECD synthesis of employer/worker surveys and literature; independent of a single vendor but dependent on heterogeneous underlying designs.