Occupational Hazards Identification, Risk Evaluation and Mitigation in Contemporary Nigeria Society: The Application of Artificial Intelligence (AI)

Occupational Hazards Identification, Risk Evaluation and Mitigation in Contemporary Nigeria Society: The Application of Artificial Intelligence (AI)

Ukandu, Chidiebere Promise¹; Emoghene, O. Blessing²; & Boye, E. Thomas³

¹Department Health Promotion, Environmental and Safety Education, Faculty of Education, University of Port Harcourt

^2,3Department of Physical and Health Education Delta State College of Education, Mosogar

DOI: https://dx.doi.org/10.47772/IJRISS.2023.7011104

Received: 27 October 2023; Accepted: 08 November 2023; Published: 14 December 2023

ABSTRACT

This paper examines the current state and potential future developments of Artificial Intelligence (AI) applications in Occupational Health and Safety (OHS) in Nigeria. OHS is a critical concern in the world and particularly in Nigeria, as workplace hazards pose risks to the health, safety, and well-being of workers. AI offers promising solutions to enhance hazard identification, risk evaluation, and control measures in OHS practices. The paper begins by providing an overview of AI, its subfields, and techniques commonly used in OHS. It then explores the real-time applications of AI in hazard identification, risk evaluation, and control measures, highlighting the benefits it brings to OHS practices. Furthermore, the paper discusses the challenges and considerations in adopting AI in Nigeria, including infrastructure limitations, skill gaps, and ethical concerns. Based on the analysis, four suggestion were proposed for Nigeria’s OHS context. These suggestions include investment in AI infrastructure and research, capacity building programs to develop AI expertise; collaboration between stakeholders, and the establishment of regulatory frameworks to ensure responsible AI deployment. By embracing AI in OHS, Nigeria can improve workplace safety, mitigate risks, and protect the health and well-being of its workforce.

Keywords: hazard identification, Risk Evaluation, Risk Mitigation, AI

INTRODUCTION

Occupational hazards present significant risks to workers across various industries in contemporary society. Nigeria, as a rapidly developing country with diverse industries, faces various occupational hazards across sectors such as construction, manufacturing, oil and gas, and agriculture. According to the Nigerian Social Insurance Trust Fund (NSITF), workplace accidents and injuries are prevalent (NSITF, 2021), with a significant impact on workers and the national economy (Akande, 2018).

Identifying and mitigating these hazards is crucial for ensuring the safety and well-being of employees, reducing workplace accidents, and improving overall productivity (Achalu, 2019). Traditional methods of hazard identification and mitigation have relied on manual inspections, safety protocols, and training programs (Kontogiannis et al., 2017; Albeaino et al., 2022). However, with the rapid advancement of technology, there is an increasing recognition of the potential of artificial intelligence (AI) to enhance occupational safety practices. Numerous studies have highlighted the importance of addressing occupational hazards and their impact on worker health and safety. For example, a study by Bunn et al. (2017) examined the causes and consequences of workplace injuries and illnesses, emphasizing the need for effective hazard identification and mitigation strategies. The study found that occupational hazards result in significant economic costs, lost productivity, and long-term health consequences for workers.

In recent years, AI has emerged as a transformative technology with applications in various fields, including healthcare (Shaheen, 2021), finance (Cao, 2022), and transportation (Iyer, 2021) etc. The potential of AI to revolutionize occupational safety practices has gained attention from researchers and practitioners alike. According to Abioye et al. (2021), AI technologies, such as machine learning, natural language processing, and computer vision, offer new possibilities for hazard identification, risk assessment, and mitigation.

AI-based hazard identification involves the use of algorithms and computational models to analyze vast amounts of data, such as sensor readings, worker behavior patterns, and historical accident records. These systems can detect patterns, anomalies, and potential hazards that may go unnoticed by human observers. For example, Li et al. (2020) developed an AI-based system that utilized computer vision and deep learning techniques to identify safety hazards in construction sites. The system demonstrated high accuracy and efficiency in detecting hazards such as fall risks, improper use of equipment, and hazardous material handling.

The benefits of AI in occupational hazard mitigation are manifold. One key advantage is the ability to provide real-time monitoring (Li et al., 2020) and early warning systems (Cao et al., 2022). AI algorithms can continuously analyze sensor data and detect potential hazards as they occur, allowing for immediate intervention and prevention of accidents (Lemos et al., 2022). This proactive approach to safety can significantly reduce the likelihood of injuries and fatalities in the workplace.

Moreover, AI can facilitate data-driven decision-making and risk assessment. By analyzing historical data and identifying patterns, AI systems can assist safety professionals in assessing the probability and severity of potential hazards (Shaheen, 2021; Albeaino et al., 2022; Lemos et al., 2022). This enables organizations to prioritize preventive measures and allocate resources effectively. For instance, Chen et al. (2019) developed an AI-based risk assessment model for the manufacturing industry, which incorporated various factors such as work environment, equipment conditions, and employee behavior to identify high-risk areas and activities.

While the potential of AI in occupational hazard identification and mitigation is promising, it remains heavily underutilized in developing countries including Nigeria. The underutilization of AI in OHS in Nigeria could as a result several challenges and limitations. Ethical considerations surrounding AI implementation, such as privacy concerns and algorithmic bias, data quality and availability can also pose challenges, as AI systems heavily rely on accurate and comprehensive data for training and validation (Abioye et al., 2021). Integration challenges and workforce acceptance are additional hurdles to consider. According to Pan and Zhang (2021), implementing AI technologies in occupational safety practices may require significant changes in organizational processes, infrastructure, and worker training. Ensuring that workers trust and embrace these technologies is crucial for successful implementation.

Looking ahead, there are exciting future directions and emerging trends in the field of AI for occupational hazard identification and mitigation. Advancements in AI algorithms and machine learning techniques, such as deep reinforcement learning and explainable AI, hold promise for enhancing the accuracy and interpretability of hazard detection systems (Li et al., 2022). Integration with sensor technologies and the Internet of Things (IoT) can further expand the capabilities of AI systems by enabling real-time data collection and analysis from multiple sources (Cao et al., 2022). More so, the concept of human-AI collaboration, known as augmented intelligence, is gaining traction. This approach recognizes that AI systems are tools to support human decision-making rather than replacing human expertise entirely. Combining the cognitive abilities of AI with human judgment and domain knowledge can lead to more effective hazard identification and mitigation strategies.

The application of AI in occupational hazard identification and mitigation has the potential to significantly improve workplace safety. By leveraging AI technologies, organizations can enhance hazard detection accuracy, provide real-time monitoring, and make data-driven decisions to reduce the occurrence of workplace accidents. However, understanding the best areas AI can be effectively applied to enhancement of hazard detection and mitigation will be of great benefit to all the industries and would better OHS practice and improve workers safety. Thus, this paper is an extensive literature review on the role of AI in improving hazard detection and mitigation in the contemporary Nigeria society.

THE CONCEPT OF OCCUPATIONAL HAZARDS

There is no single universally accepted definition of Occupational hazard. However, there some renowned definitions that are well recognized globally. International Labour Organization [ILO] (2019) defines occupational hazards as factors arising from work activities that have the potential to cause injury, illness, or death. This definition emphasizes that hazards originate from the tasks, processes, and conditions present in the workplace. It acknowledges the wide range of potential harms that can result from occupational hazards, encompassing physical, chemical, and biological risks. The ILO’s focus on the potential for severe outcomes highlights the importance of identifying and mitigating these hazards to ensure worker safety and well-being

According to WHO (2020), occupational hazards are factors in the work environment that have the potential to cause harm. This definition encompasses a broad spectrum of hazards, including physical, chemical, biological, and psychosocial factors. By acknowledging the various dimensions of hazards, the WHO emphasizes the multifaceted nature of risks faced by workers. This definition underscores the need to consider not only physical dangers but also psychological and social aspects when identifying and addressing occupational hazards.

National Institute for Occupational Safety and Health [NIOSH] (2021) NIOSH defines occupational hazards as conditions, exposures, or practices that can cause injury, illness, or death in the workplace. This definition emphasizes the practical consequences of hazards and highlights the potential harm they can inflict on workers. By encompassing not only specific factors but also general practices and exposures, NIOSH acknowledges the wide-ranging nature of hazards that can be encountered in the workplace.

Occupational Safety and Health Administration [OSHA] (2017), OSHA defines occupational hazards as any conditions or practices in the workplace that could cause harm to workers. This definition emphasizes the broad scope of hazards and their potential presence in various aspects of the work environment. It highlights the importance of identifying and addressing hazards, regardless of their specific nature, to ensure worker safety. OSHA’s definition underscores the need for a comprehensive approach to hazard prevention and control.

European Agency for Safety and Health at Work [EU-OSHA] (2018) defines occupational hazards as risks originating from work activities or the work environment that may cause harm or adverse effects on workers’ health or safety. This definition emphasizes the origin of hazards from work-related sources and acknowledges their potential to negatively impact worker health and safety. By referring to risks, it recognizes the dynamic and evolving nature of hazards in the workplace. This definition underscores the importance of assessing and managing risks to protect workers from harm.

Overall, these definitions collectively highlight the diverse nature of occupational hazards and their potential to cause harm to workers. They emphasize the need for identification, assessment, and mitigation of hazards to ensure a safe and healthy work environment. The definitions also highlight the multidimensional nature of hazards, encompassing physical, chemical, biological, and psychosocial factors, and the importance of adopting a comprehensive approach to occupational safety and health..

Occupational hazards encompass a wide range of risks, and their identification and mitigation are crucial for preventing work-related injuries, illnesses, and fatalities. According to the study conducted by Bunn et al. (2017), occupational hazards result in significant economic costs, lost productivity, and long-term health consequences for workers. This highlights the importance of addressing occupational hazards through effective hazard identification and mitigation strategies.

Occupational hazards can be classified into several categories based on the nature of the risk involved. According to Achalu (2019), some common types of occupational hazards include:

Physical Hazards: These hazards arise from environmental conditions and physical factors in the workplace. Examples include noise, vibration, temperature extremes, radiation, and ergonomic risks. Physical hazards can cause injuries, musculoskeletal disorders, hearing loss, and other health problems. For instance, prolonged exposure to excessive noise levels in industrial settings can lead to hearing impairment (Kujawa & Liberman, 2015).

Chemical Hazards: Chemical hazards result from exposure to hazardous substances, such as toxic chemicals, gases, fumes, and dust. These substances can be inhaled, absorbed through the skin, or ingested. Exposure to chemicals can lead to acute or chronic health effects, including respiratory problems, dermatitis, poisoning, and long-term organ damage. For example, exposure to asbestos fibers is associated with the development of lung diseases, including asbestosis and mesothelioma (Sakellariou, 2019).

Biological Hazards: Biological hazards involve exposure to microorganisms, such as bacteria, viruses, fungi, and parasites, as well as biological substances like blood and bodily fluids. Workers in healthcare, laboratory, and agricultural settings are particularly at risk. Biological hazards can cause infectious diseases, allergic reactions, and other health problems. For instance, healthcare workers may be exposed to blood-borne pathogens, leading to the transmission of diseases such as hepatitis B, hepatitis C, and HIV (Centers for Disease Control and Prevention, 2021).

Psychosocial Hazards: Psychosocial hazards are related to the social and organizational aspects of work that can impact mental health and well-being. These hazards include factors such as high job demands, low control over work, long working hours, workplace violence, and bullying. Psychosocial hazards can contribute to stress, anxiety, depression, and other mental health disorders. For example, a study by Stansfeld and Candy (2006) found that high job demands and low job control were associated with increased risk of developing common mental disorders.

Safety Hazards: Safety hazards involve situations or conditions that have the potential to cause accidents and injuries. These hazards can include slips, trips, falls, electrical hazards, machinery-related risks, and inadequate safety procedures. Failure to address safety hazards can result in severe injuries, amputations, fractures, and even fatalities. For instance, inadequate machine guarding can lead to serious injuries, such as crush injuries and amputations (Occupational Safety and Health Administration, 2022).

Effectively managing occupational hazards requires a comprehensive approach that includes hazard identification, risk assessment, and implementation of appropriate control measures. This involves conducting regular workplace inspections, providing adequate training and protective equipment, implementing engineering controls, establishing safety protocols, and promoting a culture of safety within the organization.

HAZARD IDENTIFICATION

Traditional Occupational Hazards Identification refers to the conventional methods and approaches used to identify potential risks and dangers in the workplace. These methods rely on established practices, observations, and expert knowledge to recognize hazards and assess their impact on worker health and safety. While there may be variations in the specific techniques employed, traditional hazard identification typically involves the following key elements: workplace inspections, hazard checklists, incident analysis, and employee input.

Figure 1: Classical hazard identification processes

Source: Adapted from: https://www.indiamart.com/proddetail/hazid-hazard-identification-studies-12688470512.html)

Workplace inspections are a fundamental component of traditional hazard identification. These inspections involve a systematic examination of the work environment, equipment, and processes to identify any existing or potential hazards. Inspections can be conducted by safety professionals, supervisors, or trained personnel using standardized checklists or guidelines. The purpose is to visually identify hazards such as trip hazards, unguarded machinery, or improper storage of hazardous materials (Heinrichs, 2016).

Hazard checklists are another common tool used in traditional hazard identification. These checklists provide a structured approach to systematically review specific areas or tasks within the workplace. The checklists typically cover a range of hazards based on industry standards and regulations. For example, a construction hazard checklist may include items such as fall hazards, electrical hazards, or excavation risks (Wong, 2017). By following the checklist, hazards can be identified based on their presence or absence in the workplace.

Incident analysis is an important aspect of traditional hazard identification. It involves examining past incidents, accidents, near-misses, or injuries to identify the underlying hazards that contributed to these events. Incident reports and records are reviewed to identify patterns, root causes, and potential hazards that may have been overlooked. This analysis helps to identify common hazards and underlying systemic issues that need to be addressed (Stewart et al., 2018).

Employee input is a valuable source of information in traditional hazard identification. Workers who directly engage in the tasks and processes of the job often have first-hand knowledge of potential hazards. Their input can be obtained through surveys, interviews, or participation in safety committees. Involving employees in the hazard identification process not only enhances the accuracy and completeness of hazard identification but also promotes worker engagement and ownership of safety (Zohar, 2010).

Traditional Occupational Hazards Identification relies on established methods such as workplace inspections, hazard checklists, incident analysis, and employee input. These approaches provide a systematic way to recognize and assess workplace hazards, contributing to the development of effective risk management strategies. After successful hazard identification, the next actions are risk assessment and hazard mitigation.

HAZARD MITIGATION

Occupational Hazards Mitigation refers to the strategies and practices employed to reduce or eliminate identified risks and dangers in the workplace (Achalu, 2019). These mitigation efforts aim to protect workers from harm and create a safe working environment. Traditional mitigation approaches encompass various elements, including engineering controls, administrative controls, and personal protective equipment (PPE). These measures are traditionally known as hierarchy of hazard control.

Figure 2: Traditional Hazard Control (OHS Academy, 2023)

Engineering controls are considered the most effective means of hazard mitigation as they involve modifying the work environment or processes to eliminate or minimize hazards. Examples of engineering controls include installing machine guarding to prevent contact with hazardous machinery (OSHA, n.d.), implementing ventilation systems to remove harmful fumes or dust particles (CDC, 2021), or redesigning workstations to optimize ergonomics and reduce physical strain (NIOSH, 2019). By addressing hazards at their source, engineering controls can provide long-term and sustainable solutions.

Administrative controls focus on modifying work practices, policies, and procedures to reduce exposure to hazards. These controls often involve establishing guidelines, protocols, and training programs to ensure safe work practices are followed. For instance, implementing a permit-to-work system for high-risk activities (Harris et al., 2020), conducting regular safety meetings, or providing safety training and education to employees (Kines et al., 2019) are examples of administrative controls. While administrative controls may not eliminate hazards entirely, they aim to minimize risks by promoting safe behaviors and raising awareness.

Personal protective equipment (PPE) is a critical component of traditional hazard mitigation. PPE includes items such as protective clothing, gloves, goggles, helmets, and respiratory protective devices. It acts as a physical barrier between workers and hazards, providing protection against specific risks. PPE is typically used when engineering or administrative controls are not feasible or when additional protection is necessary. However, it is considered the least effective control measure as it relies on worker compliance and proper use (CDC, 2021). Therefore, its implementation should be complemented with other control measures.

It is important to note that the hierarchy of controls is widely recognized in occupational safety and health. This hierarchy emphasizes the importance of prioritizing engineering controls over administrative controls and PPE whenever feasible. By implementing control measures in this order, hazards can be effectively mitigated and the risk of workplace injuries and illnesses can be minimized (OSHA, 2022.).

In conclusion, traditional Occupational Hazards Mitigation involves a combination of engineering controls, administrative controls, and personal protective equipment. By employing these strategies, employers can reduce workplace hazards and create a safer working environment for employees.

THE CONCEPT AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Artificial Intelligence (AI) is a branch of computer science that focuses on the development of intelligent machines capable of performing tasks that typically require human intelligence. AI systems aim to simulate human cognitive processes, such as learning, reasoning, and problem-solving, to perform specific tasks efficiently and accurately.

AI encompasses various subfields and techniques, including machine learning, natural language processing, computer vision, and robotics. Machine learning algorithms enable AI systems to learn from and analyze large datasets, identify patterns, and make predictions or decisions based on the acquired knowledge (Russell & Norvig, 2016). Natural language processing enables AI systems to understand and generate human language, facilitating communication between humans and machines (Jurafsky & Martin, 2019). Computer vision enables machines to interpret and understand visual information, such as images or videos, enabling applications like object recognition or autonomous driving (Szeliski, 2010). Robotics combines AI with physical systems, allowing machines to interact with the physical world and perform tasks in real-world environments (Corke, 2017).

The applications of AI are wide-ranging and continue to expand across various industries. In healthcare, AI is used for diagnosis and treatment planning, drug discovery, and medical image analysis (Esteva et al., 2019). In finance, AI is employed for fraud detection, risk assessment, and algorithmic trading (Riley, 2019). In transportation, AI powers autonomous vehicles and traffic management systems (Kuutti et al., 2020). In manufacturing, AI is utilized for quality control, predictive maintenance, and supply chain optimization (Lee et al., 2018). These examples illustrate the transformative potential of AI in improving efficiency, accuracy, and decision-making across sectors.

However, the adoption of AI also raises ethical and societal considerations. AI systems must be designed to ensure transparency, accountability, and fairness. The potential biases and unintended consequences of AI algorithms require careful scrutiny and mitigation (Angwin et al., 2016). Privacy concerns arise as AI systems collect and analyze vast amounts of personal data, necessitating robust data protection measures (Floridi et al., 2018). Moreover, the impact of AI on employment and the workforce must be addressed, including the need for reskilling and job displacement mitigation strategies (Brynjolfsson & McAfee, 2014).

AI is a rapidly evolving field with diverse applications across industries. Its ability to replicate human cognitive abilities and automate complex tasks has the potential to revolutionize numerous sectors. However, ethical considerations and the responsible development and deployment of AI systems are crucial to ensure its benefits are harnessed while mitigating risks and addressing societal implications.

THE ROLE OF AI IN HAZARD IDENTIFICATION, RISK EVALUATION AND HAZARD MITIGATION

Artificial Intelligence (AI) has shown significant potential in various applications within Occupational Health and Safety (OHS) to enhance workplace safety, mitigate hazards, and improve overall risk management. The real-time applications of AI in OHS leverage its capabilities in data analysis, pattern recognition, and predictive modeling to identify and address potential risks promptly. This section discusses some key real-time applications of AI in OHS, supported by relevant literature.

Real-time hazard detection and monitoring: AI technologies, such as computer vision and sensor networks, enable real-time monitoring of the work environment to identify hazardous conditions or behaviors. For instance, Li et al. (2020) developed an AI-based computer vision system that can detect safety hazards in construction sites, including fall risks and improper use of equipment. The system continuously analyzes live video feeds to identify potential hazards and send immediate alerts to supervisors or workers.

Predictive analytics for risk assessment: AI algorithms can analyze historical data on accidents, incidents, and near-misses to identify patterns and predict potential risks in real-time. This enables proactive interventions and preventive measures. Rivas et al. (2020) utilized machine learning techniques to analyze historical accident data and identify risk factors associated with workplace injuries. Their study demonstrated the potential of AI in predicting and preventing injuries before they occur.

Intelligent personal protective equipment (PPE): AI can enhance the functionality of PPE by integrating sensor technologies and real-time monitoring capabilities. For example, an AI-enabled smart helmet can monitor environmental parameters such as temperature, humidity, and toxic gas levels to provide real-time feedback and alerts to workers (Trivedi et al., 2018). This allows workers to take necessary precautions and avoid potential hazards.

Human behaviour monitoring and intervention: AI systems can analyze worker behaviour patterns and provide real-time feedback or interventions to improve safety practices. For instance, wearable devices equipped with AI algorithms can monitor workers’ movements and provide real-time feedback on ergonomics and proper lifting techniques (Korhonen et al., 2019). This helps prevent musculoskeletal injuries and promotes safer work practices.

Intelligent decision support systems: AI-based decision support systems can assist safety professionals in real-time decision-making by analysing complex and dynamic data. These systems can provide recommendations on safety protocols, emergency response strategies, and resource allocation. Hämäläinen et al. (2020) developed an AI-based decision support system for chemical risk management that utilizes real-time data from sensors to assess risks, recommend control measures, and support decision-making in hazardous environments.

Training and education: AI can be utilized in virtual reality or augmented reality systems to create immersive and interactive training experiences for workers. These AI-powered training systems can simulate hazardous scenarios and provide real-time feedback, enabling workers to develop safety skills and knowledge in a controlled environment (Korhonen et al., 2019).

These real-time applications of AI in Occupational hazard identification and mitigation demonstrate the potential for AI technologies to improve workplace safety by enabling faster hazard detection, proactive risk management, and intelligent decision-making.

THE ADVANTAGES OF AI APPLICATION IN OHS OVER TRADITIONAL HUMAN INTELLIGENCE

The development of AI is not intended to replace humans in the workplace, rather it is a movement to enhance and smoothen the job process for humans which reduces workload and stress. According to Ferguson (2021), Man must learn and understand AI to table to use and ease up job process in order to live longer. He further argued that AI is not human replacer, but a human enhancer. As such, different authors have highlighted different benefits of AI in OHS. Some of the benefits of AI in Occupational Health and Safety (OHS) compared to traditional hazard identification, risk evaluation, and control measures which rely heavily on human intelligence include:

Enhanced Efficiency: AI technologies, such as machine learning algorithms and computer vision, can analyze large datasets quickly and accurately, enabling efficient hazard identification and risk evaluation (Li et al., 2020).

Real-time Monitoring and Early Warning: AI-based systems can provide real-time monitoring of workplaces, detecting potential hazards as they occur and issuing immediate alerts, allowing for timely intervention and prevention of accidents (Li et al., 2020).

Improved Accuracy: AI algorithms can analyze complex data patterns, enabling more accurate identification of potential hazards and risks, reducing false positives and false negatives (Chen et al., 2019).

Improved Worker Safety: AI-enabled technologies, such as intelligent personal protective equipment (PPE) or AI-based training systems, can enhance worker safety by providing real-time feedback, hazard detection, and immersive training experiences (Trivedi et al., 2018; Korhonen et al., 2019).

THE POTENTIAL FUTURE DEVELOPMENT IN AI APPLICATION IN OHS

Some potential future developments in the application of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) may include:

Advanced Predictive Analytics: AI algorithms can be further developed to improve the accuracy and predictive capabilities of hazard identification and risk assessment models. This can involve incorporating more diverse and real-time data sources, such as wearable sensors or Internet of Things (IoT) devices, to enhance predictive analytics (Yang et al., 2018).

Human-Machine Collaboration: The future of AI in OHS may involve closer collaboration between AI systems and human workers. AI technologies can assist workers in real-time decision-making, provide personalized safety recommendations, and enable adaptive safety protocols based on individual worker behavior and context (Nguyen et al., 2020).

Explainable AI and Trustworthiness: To address concerns about transparency and trust in AI systems, future developments can focus on developing explainable AI models that provide clear explanations for the decisions made. This can help OHS professionals and workers understand how AI algorithms arrive at specific hazard identifications or risk assessments (Rudin, 2019).

Autonomous Safety Systems: AI can be used to develop autonomous safety systems that can operate independently to identify and mitigate hazards in real-time. For example, autonomous robots equipped with AI technologies can navigate hazardous environments, perform inspections, and mitigate risks without direct human intervention (Yüceer et al., 2020).

Adaptive Training and Education: AI can play a crucial role in the development of adaptive training and education programs for OHS. Personalized and immersive training experiences using AI-based simulations and virtual reality can enhance worker knowledge, skills, and safety behavior (Zhao et al., 2021).

CONCLUSION

The application of Artificial Intelligence (AI) in Occupational Health and Safety (OHS) holds great potential to enhance workplace safety practices in Nigeria. While the application of AI in OHS in Nigeria is still at infant stage, the existing literature highlights various areas where AI can make a positive impact, such as real-time hazard detection, risk assessment, intelligent PPE, data-driven decision support, and training. Hence, it is high time Nigeria begin to embrace AI application in OHS to smoothen the processes of hazard identification, risk evaluation and hazard mitigation in order to achieve better occupational outcome though harnessed safety and healthy work environment.

SUGGESTIONS

To harness the benefits of AI in OHS in Nigeria, the following suggestions are proposed:

1. Nigeria government and large companies should invest in awareness and knowledge programme to increase the workers understanding of AI technologies and their potential applications in OHS among OHS professionals, industry stakeholders, and policymakers in Nigeria. This can be achieved through training programs, workshops, and educational initiatives.
2. All stakeholders should foster collaboration between academia, industry, and government agencies to facilitate research and development of AI-based solutions tailored to the specific needs and challenges of the Nigerian OHS landscape. Encourage partnerships with technology companies and startups to leverage their expertise and resources.
3. Effort should also be made to adapt AI solutions to the local context of Nigeria, taking into account regulatory frameworks, infrastructure limitations, and the unique characteristics of the Nigerian workforce. Ensure that AI systems are culturally sensitive, accessible, and usable by workers in different industries and sectors.

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