Machine learning and the labor market: A portrait of occupational and worker inequities in Canada
Bibliographic record
Abstract
INTRODUCTION: Machine learning (ML), an artificial intelligence (AI) subfield, is increasingly used by Canadian workplaces. Concerningly, the impact of ML may be inequitable and contribute to social and health inequities in the working population. The aim of this study is to estimate the number of workers in occupations with high, medium, and low ML exposure and describe differences in exposure according to occupational and worker sociodemographic factors. METHODS: Canadian occupations were scored according to the extent to which they were made up of job tasks that could be performed by ML. Eight years of data from Canada's Labour Force Survey were pooled and the number of Canadians in occupations with high, medium, or low exposure to ML were estimated. The relationship between hourly wages, educational attainment, and job skill, training and experience requirements, and ML exposure was examined using multinomial models that were stratified by gender. RESULTS: Approximately 5.7 million Canadians are working in occupations characterized by high ML exposure. Women workers and workers with a college or bachelor's degree and in occupations with lower job skills requirements made up a greater proportion of workers in occupations with high ML exposure. Multinomial models indicated gender differences in the relationship between independent variables and ML exposure. Among men, higher educational attainment and hourly wages were associated with high occupational ML exposure. However, among women, higher educational attainment and hourly wages were associated with low occupational ML exposure. CONCLUSION: ML exposure is segmented according to occupational and worker sociodemographic characteristics and has the potential to widen inequities in the working population. ML may have a gendered effect and disproportionately impact certain groups of women when compared to men. We provide a critical evidence base to inform strategic responses that ensure inclusion in a working world where ML is commonplace.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".