Artificial Intelligence in Manufacturing Industry Worker Safety: A New Paradigm for Hazard Prevention and Mitigation
Bibliographic record
Abstract
The phenomenal rise of artificial intelligence (AI) in the last decade, and its evolution as a versatile addition to various fields, necessitates its usage for novel purposes in multidimensional fields like the manufacturing industry. Even though AI has been rigorously studied for process optimization, wastage reduction, and other quintessential aspects of the manufacturing industry, there has been limited focus on worker safety as a theme in the current literature. Safety standards contribute to worker safety, but there is no one-size-fits-all approach in these standards or policies, which warrants evaluation and integration of new ideas and technologies to reach the closest to ideal standards. This includes but is not limited to health, regulation of operations, predictive maintenance, and automation and control. The rise of Industry 4.0 and the migration towards Industry 5.0 facilitate easy integration of advanced technologies like AI into the manufacturing industry with real-time predictive capabilities, and this can help reduce human errors and mitigate hazards in processes where sensitivity is crucial or hazards are frequent. Keeping the future outlook in focus, AI can contribute to training workers in risk-free environments, promote engineering education for easy adaptation to new technology, and reduce resistance to changes in the industry. Furthermore, there is an urgent need for standards and regulations to govern and integrate AI technologies judiciously into the manufacturing industry, which holds AI models and their creators accountable for their decisions. This could further extend to preventing the adversarial use of new technology. This study exhaustively discusses the potential and ongoing contributions of this technology to the safety of workers in the manufacturing industry.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".