FROM DEVELOPMENT TO DEPLOYMENT: FOR A COMPREHENSIVE APPROACH TO ETHICSOF AI AND LABOUR
Bibliographic record
Abstract
In recent years, government and policy organizations, private companies, and research agencies have been discussing the potential disruption caused by the deployment of AI systems in working environments. This paper traces contemporary discourse on the relationship between artificial intelligence and labour and discusses how these principles must be comprehensive in their approach to labour and AI. First, the paper asserts that ethical frameworks in AI alone are not enough to guarantee the rights of workers since they lack enforcement mechanisms and the participation of worker organizations. Secondly, it argues that current discussions on AI and labour focus on the deployment of these technologies in the workplace but ignore the essential role of human labour in their development, particularly in the different cases of outsourced labour around the world. Finally, the paper recommends the use of already existing human rights frameworks on working conditions – notably the International Labour Organization conventions on the right of collective bargaining, the abolition of discrimination at work, and the right to equal remuneration – as a basis for a more comprehensive ethical framework on AI labour. It concludes by arguing that the central question regarding the future of work will not be whether intelligent machines will replace humans, but who will own and have a say on the systems that will ultimately work alongside humans.
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.
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 | 0.048 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.104 |
| Scholarly communication | 0.018 | 0.036 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.020 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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".