Full Report
An evidence-led approach is key to navigating the rapid spread of digital technologies–including AI–in relation to occupational safety and health (OSH).
Analysis Summary
# Best Practices: Integrating Emerging Technology Safely into Occupational Safety and Health (OSH)
## Overview
These practices address the need for an evidence-led, coordinated approach to managing the occupational safety and health (OSH) risks—and realizing the safety benefits—associated with the rapid deployment of emerging digital technologies, including AI, wearable devices, collaborative robots (cobots), and AR/VR training systems. The primary focus is mitigating potential new risks while leveraging technology for safety improvements.
## Key Recommendations
### Immediate Actions
1. **Initiate Stakeholder Collaboration:** Establish a cross-functional working group involving OSH professionals, technology developers, and worker representatives to "ask the right questions" regarding the safety implications of any new technology *before* procurement or large-scale deployment.
2. **Conduct Initial Risk Scoping for High-Risk Tech:** Immediately map potential risks associated with currently deployed or planned technologies, particularly **Algorithmic Management/Emotional AI** (assessing potential impacts on worker privacy and trust) and **Wearable Devices** (identifying compliance requirements and potential for increased stress/complacency).
3. **Prioritize Evidence Review:** Mandate that all proposed technology implementations affecting OSH must be preceded by a review of available safety evidence, acknowledging where evidence is currently patchy or non-existent.
### Short-term Improvements (1-3 months)
1. **Develop Collaborative Implementation Protocols for Cobots:** For organizations integrating Collaborative Robots (cobots), develop specific protocols ensuring safe integration into existing assembly lines, focusing on mitigating collision risks and defining clear human-robot interaction boundaries.
2. **Review Wearable Device Usage Policies:** Analyze current usage of health/environmental tracking wearables. If data collection is extensive, revise policies to actively mitigate worker stress or complacency by ensuring clear communication on data utility and worker control over personal monitoring.
3. **Pilot AR/VR Training Modules with Measurable Outcomes:** If deploying AR/VR for safety training, design the pilot program to specifically measure not just knowledge retention but also the translation of learned behavior into real-world safety performance.
### Long-term Strategy (3+ months)
1. **Establish Continuous Evidence Mapping and Research Agenda:** Build a systematic process (potentially leveraging resources like the Lloyd’s Register Foundation Global Safety Evidence Centre) to track the long-term safety impacts of adopted technologies and actively identify and fill evidence gaps specific to your industry and worker demographics.
2. **Implement Trust-Building Measures for Algorithmic Management:** If utilizing AI for management tasks, develop design improvements and governance frameworks to ensure transparency, fairness, and collaboration. Actively work to counter the perception that AI signals a lack of trust from management.
3. **Future-Proof Planning:** Begin investigations into the potential OSH impacts of next-generation technologies, such as Generative AI, Autonomous Vehicles, and Digital Twins, integrating these considerations into organizational technology roadmaps.
## Implementation Guidance
### For Small Organizations
- **Focus on User Need:** Before purchasing OSH smartphone apps, clearly define the primary safety goal (e.g., habit tracking vs. incident reporting) and select tools with demonstrable, though potentially mixed, alignment with desired wellness outcomes.
- **Manual Check-in for AI:** If using basic automated scheduling or management tools, incorporate mandatory human oversight checkpoints to prevent adverse emotional or privacy impacts associated with overly automated oversight.
### For Medium Organizations
- **Standardized Vetting Pipeline:** Create a formal OSH Technology Review Board (TRB) to vet new deployments, ensuring collected data from wearables or environmental sensors directly informs risk reduction, rather than just tracking.
- **Blended Training Approach:** Use AR/VR for high-risk, low-frequency training scenarios (e.g., emergency response), but ensure foundational safety knowledge transfer is verified using traditional methods until AR/VR efficacy improves.
### For Large Enterprises
- **Governance Framework:** Establish a formal governance and regulatory strategy for emerging technologies that mandates risk anticipation and mitigation, ensuring compliance is integrated from the design phase (Security and Safety by Design).
- **Differential Impact Studies:** Actively investigate how different worker groups (e.g., age, tenure, department) are uniquely affected by new technologies, tailoring deployment and mitigation strategies accordingly.
- **Data Broker Transparency:** If utilizing complex data streams (e.g., biometric data from wearables, performance metrics from algorithmic management), establish strict internal data stewardship policies ensuring the data collected serves safety improvement, not punitive monitoring.
## Configuration Examples
*Note: Specific configuration parameters were not provided in the source document. The following are conceptual best practices based on the identified risks.*
1. **Cobot Safety Configuration:** Implement "Speed and Separation Monitoring" (SSM) as the primary control strategy, ensuring robot speed reduction is directly proportional to the proximity of human workers, ideally using LiDAR or vision systems rather than hard-coded safety zones.
2. **Emotional AI Mitigation Setting:** Configure any algorithmic feedback mechanism to prioritize descriptive, actionable safety insights over comparative or prescriptive performance ratings that could induce emotional suppression or anxiety in workers.
3. **Wearable Data Policy Configuration:** Develop a configuration file dictating that environmental exposure data (noise, heat) is anonymized and aggregated at the team level before being presented to managers, while individual health data remains accessible only to the worker and occupational health services.
## Compliance Alignment
- **NIST:** Alignment with concepts in NIST Cybersecurity Framework (Identify, Protect functions) concerning the management of technology supply chain risks and data privacy/integrity related to worker monitoring.
- **ISO 45001 (Occupational Health and Safety):** Direct alignment with requirements for hazard identification, risk assessment, and control of monitoring and measurement for performance evaluation.
- **CIS Benchmarks:** Concepts should be integrated into the management of endpoints (smart devices, wearables) and infrastructure supporting these new technologies, focusing on secure configuration and access control.
## Common Pitfalls to Avoid
- **Blind Faith in Technology:** Do not assume that because a technology is new or highly technical, it inherently improves safety; evaluate efficacy via evidence-based metrics.
- **Ignoring Worker Perception:** Deploying algorithmic systems without addressing worker concerns about trust, privacy, or fairness, leading to resistance or intentional circumvention of safety protocols.
- **Patchwork Implementation:** Adopting technologies without coordinated research or clear governance, leading to fragmented data, conflicting safety requirements, and unknown cumulative risks.
- **Complacency from Monitoring:** Over-relying on data from wearables or sensors to the detriment of traditional safety procedures, potentially causing situational awareness degradation among workers.
## Resources
- **Core Evidence Report:** The impact of emerging technology on safety at work (Lloyd’s Register Foundation / RAND Europe).
- **Safety Evidence Hub:** Lloyd’s Register Foundation Global Safety Evidence Centre (gsec.lrfoundation.org.uk) for ongoing research and evidence gaps.
- **Framework Guidance:** Utilize ISO 45001 for structuring the required OSH management systems around technological change.