Machine learning and the labour market: A portrait of occupational and worker inequities in Canada
Notice bibliographique
Résumé
ABSTRACT Introduction Machine learning (ML) is increasingly used by Canadian workplaces. Concerningly, the impact of ML may be inequitable and disrupt social determinants of health. The aim of this study is to estimate the number of workers in occupations highly exposed to ML and describe differences in ML exposure represents 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 or low exposed to machine learning were estimated. The relationship between gender, hourly wages, educational attainment and occupational job skills, experience and training requirements and ML exposure was examined using stratified logistic regression models. Results Approximately, 1.9 million Canadians are working in occupations with high ML exposure and 744,250 workers were employed in occupations with low ML exposure. Women were more likely to be employed in occupations with high ML exposure than men. Workers with greater educational attainment and in occupations with higher wages and greater job skills requirements were more likely to experience high ML exposure. Women, especially those with less educational attainment and in jobs with greater job skills, training and experience requirements, were disproportionately exposed to ML. Conclusion ML has the potential to widen inequities in the working population. Disadvantaged segments of the workforce may be most likely to be employed in occupations with high ML exposure. ML may have a gendered effect and disproportionately impact certain groups of women when compared to men. We provide a critical evidence base to develop strategic responses that ensure inclusion in a working world where ML is commonplace. KEY MESSAGES What is already known on this topic The Canadian labour market is undergoing an artificial intelligence (AI) revolution that has the potential to have widespread impact on a range of occupations and worker groups. It is unclear how which the adoption of machine learning (ML), an AI subfield, within the working world might contribute to inequities within the labour market. What this study adds Segments of the workforce which have been previously disadvantaged may be most likely to work in occupations most likely to be affected by ML. ML may have a gendered effect and disproportionately impact some groups of women when compared to men. How this study might affect research, practice or policy Findings can inform targeted policies and programs that optimize the economic benefits of ML while addressing disparities that can emerge because of the adoption of the technology on workers.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,010 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».