Human-Centered AI for Industry 5.0 (HUMAI5.0)
Bibliographic record
Abstract
Integrating artificial intelligence (AI) in the workplace has created many challenges and opportunities for human work. Increased human–automation collaboration is expected on physical or cognitive tasks. Several disciplines have echoed the fact that these new technologies automate part of the work steps in collaboration with human operators rather than replacing entire professions, such as Information Technologies ( Seeber et al., 2020 ), economics ( Frey & Osborne, 2017 ), work psychology ( Parker & Grote, 2022 ) and human factors & ergonomics ( Mueller et al., 2021 ). The area of Industry 4.0 (I4.0) is at the forefront of the digitalization of human work wherein AI plays a central role. I4.0 intends to increase production system capabilities in terms of productivity, repeatability, flexibility, real-time monitoring, and process standardization ( Zheng et al., 2021 ). This is done by integrating a set of digital, robotic, and automated technologies into production ( Kadir et al., 2019 ) and combining different digital solutions together ( Zheng et al., 2021 ). The latest technological advances in I4.0 have increased the capabilities of machines in performing complex, cognitive tasks ( Xiong et al., 2022 ). However, the development of I4.0 technologies follows a technocentric approach ( Sony & Naik, 2020 ). Focusing on technology development first ( Carayannis et al., 2022 ). Bibliometric analyses quantified the technocentric directions of I4.0. A recent literature review noted that out of a sample of 4885 studies with a search strategy that included the terms Industry 4.0 and Human Factors , 4849 studies focused on technical factors and 36 on human factors ( Passalacqua et al., 2022 ). This top-down approach often neglects the contextual factors that govern work systems and their potential integration into situated operational practices ( Loup-Escande, 2022 ).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.012 |
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".