The Effects of Artificial Intelligence on the Future of Employment: Looking for a Trend from a Literature Review
Bibliographic record
Abstract
Each new wave of technological progress sparks debates about the effects of automation on the future of employment. Current debates on artificial intelligence (AI) and employment are reminiscent of those raised by mechanization in the 19th century, the generalization of electricity, and the introduction of computers in the 20th century: some consider new technologies as a way to relieve workers of the most challenging tasks, and others are alarmed by the imminent threat to employment. This article aims to contribute to the ongoing debate on the potential changes that may arise from the recent emergence of Generative AI in job markets. It is based on a historical analysis of technological revolutions and a literature review of technology’s impact on employment. The purpose of this study is not to gather general statistics but rather to analyze potential changes and help design suitable policy responses. This analysis will also consider the possible impact on job quality. The study emphasizes the potential implications for various professional categories but does not predetermine the outcomes of technological transition. The decision to incorporate such technologies is driven by humans, and it is their responsibility to guide the transition process.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".