European Agency for Safety and Health at Work

 

Artificial intelligence for worker management: implications for occupational safety and health
Report

 

Table of contents
List of figures and tables
1 Introduction
1.1 Rationale and objectives
1.2 Scope
1.3 Research methods
Literature review
In-depth expert interviews
Statistical data analysis of ESENER-3
1.4 Structure of the report
2 AIWM and OSH
2.1 Risks of AIWM for workers’ safety and health
2.2 Opportunities of AI-based management approaches for OSH
2.3 AIWM and OSH: evidence from ESENER-3
3 Prevention measures
4 Conclusions and recommendations
References
Annex I – Analysis of Third European Survey of Enterprises on New and Emerging Risks 2019 (ESENER-3) data
Annex II – Experts interview questionnaire
List of figures and tables
Figure 2-1: Establishments using digital technologies that discuss possible health and safety impacts of technologies, by type of technology (EU-27, %)
Figure 2-2: Establishments that discussed the impact of digital technologies by type of impacts discussed (EU-27, %)
Figure I-1: Workplaces reporting discussions on the possible health and safety impacts of digital technologies by size (EU-27, %)
Figure I-2: Workplaces reporting discussions on the possible health and safety impacts of digital technologies by main activity of the establishment (EU-27, %)
Table 2-1: Establishments by specific OSH risks (based on ESENER-3 Q200 and Q201) and the usage of digital technologies (based on ESENER-3 Q310) (EU-27, % - 2019)
Table I-1: Binomial logit regression models analysing factors correlated with the usage of robots that interact with workers (use of robots = 1)
Table I-2: Binomial logit regression models analysing factors correlated with the usage of machines, systems or computers determining the content or pace of work (machines determining the content or pace of work = 1)
Table I-3: Binomial logit regression models analysing factors correlated with the usage of monitoring technologies (monitoring = 1)

Table I-4: Binomial logit regression models analysing factors correlated with the usage of wearable devices, such as smart watches, data glasses or other (embedded) sensors (wearables = 1)
Table I-10: Workplaces by type of measures undertaken to prevent psychosocial risks in the last three years, by type of digital technology used (EU-27, %)
 


fonte: osha.europa.eu

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© European Agency for Safety and Health at Work, 2022