The impacts of artificial intelligence (AI) on jobs: an industry perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper aims to explore the impacts of artificial intelligence (AI) on jobs. Design/methodology/approach The authors followed rapid review guidelines. The authors collected industry and government reports published prior and up to August 2017 in Google and Google Scholar using combination of key words: “job automation” or “work automation” with technology keywords: “artificial intelligence,” “machine learning,” etc. In total, 11 were included in this research. Findings The use of AI technologies will impact jobs in the near future as some job tasks are automated. AI is likely to substitute both, routine and nonroutine tasks. It is expected that humans and robots would work together in ways never imaginable. Changes in employability skills are expected. Because of the magnitude of these impacts on jobs, consulted reports call for concerted solutions that go beyond organizations’ and industry’s boundaries to include other relevant stakeholders. Moreover, organizations will have to rethink their human resource (HR) function to realign its expertise to the reality of AI. Practical implications In this context, the HR function will have to understand the dynamics that generate the impacts of these technologies in a workplace, to anticipate changes and actively contribute to creating an organizational environment that will facilitate the collaboration between human workers and complex digital agents, while ensuring compliance with labor and employment laws and supporting strategic organizational objectives. Originality/value This paper contributes to the debate on ongoing concerns by providing a synthesis of relevant professional literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it