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Integrating Artificial Intelligence to Enhance Risk Assessment in Occupational Health and Safety for Facility Management

Patrick Ohis Ailemen

Subject area: Science,Engineering and Technology  ·  Area of research: AI to improve risk assessment in OHS

Abstract

The integration of artificial intelligence (AI) into occupational health and safety (OHS) is transforming risk assessment practices, particularly within the complex domain of facility management. This paper examines how AI-driven tools such as machine learning algorithms and AI-powered cameras are revolutionizing workplace hazard identification, prediction, and mitigation. While effective, traditional OHS methods often fail to address emerging risks or adapt to dynamic work environments proactively. AI?s predictive capabilities enable organizations to analyze large datasets, detect patterns in workplace incidents, and provide actionable insights for enhanced safety protocols. This paper further explores the ethical and regulatory dimensions of AI adoption, addressing concerns such as biases in algorithms, data privacy, and the legal frameworks governing AI in workplace safety. Case studies and industry examples within the paper illustrate the tangible benefits of AI systems, including reduced workplace incidents, improved employee well-being, and the development of a culture centered on proactive safety management. Despite these advancements, challenges persist, such as ensuring equitable AI adoption and maintaining transparency in decision-making processes. Practical recommendations are provided for OHS professionals, facility managers, and policymakers, emphasizing the need for structured AI solutions, workforce training, and stringent regulatory guidelines. Ultimately, this paper calls for the responsible adoption of AI, advocating for a collaborative approach to unlock its full potential in reshaping safety practices across U.S. facility management and beyond.

Keywords

Artificial Intelligence, Occupational Health and Safety, Facility Management, Risk Assessment, Machine Learning, Workplace Safety, Ethical AI, Regulatory Frameworks, Data Privacy, Predictive Analytics.

References

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How to cite this paper

Patrick Ohis Ailemen "Integrating Artificial Intelligence to Enhance Risk Assessment in Occupational Health and Safety for Facility Management" Iconic Research And Engineering Journals Volume 8 Issue 6 2024 Page 597-607
Patrick Ohis Ailemen "Integrating Artificial Intelligence to Enhance Risk Assessment in Occupational Health and Safety for Facility Management" Iconic Research And Engineering Journals, vol. 8, no. 6, Dec. 2024
Patrick Ohis Ailemen (2024). Integrating Artificial Intelligence to Enhance Risk Assessment in Occupational Health and Safety for Facility Management. Iconic Research And Engineering Journals, 8(6).
Patrick Ohis Ailemen "Integrating Artificial Intelligence to Enhance Risk Assessment in Occupational Health and Safety for Facility Management" Iconic Research And Engineering Journals, vol. 8, no. 6, Dec. 2024.
@article{1706644,
      author = {Patrick Ohis Ailemen},
      title = {Integrating Artificial Intelligence to Enhance Risk Assessment in Occupational Health and Safety for Facility Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {6},
      pages = {597-607},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706644.pdf},
      abstract = {The integration of artificial intelligence (AI) into occupational health and safety (OHS) is transforming risk assessment practices, particularly within the complex domain of facility management. This paper examines how AI-driven tools such as machine learning algorithms and AI-powered cameras are revolutionizing workplace hazard identification, prediction, and mitigation. While effective, traditional OHS methods often fail to address emerging risks or adapt to dynamic work environments proactively. AI?s predictive capabilities enable organizations to analyze large datasets, detect patterns in workplace incidents, and provide actionable insights for enhanced safety protocols. This paper further explores the ethical and regulatory dimensions of AI adoption, addressing concerns such as biases in algorithms, data privacy, and the legal frameworks governing AI in workplace safety. Case studies and industry examples within the paper illustrate the tangible benefits of AI systems, including reduced workplace incidents, improved employee well-being, and the development of a culture centered on proactive safety management. Despite these advancements, challenges persist, such as ensuring equitable AI adoption and maintaining transparency in decision-making processes. Practical recommendations are provided for OHS professionals, facility managers, and policymakers, emphasizing the need for structured AI solutions, workforce training, and stringent regulatory guidelines. Ultimately, this paper calls for the responsible adoption of AI, advocating for a collaborative approach to unlock its full potential in reshaping safety practices across U.S. facility management and beyond.},
      keywords = {Artificial Intelligence, Occupational Health and Safety, Facility Management, Risk Assessment, Machine Learning, Workplace Safety, Ethical AI, Regulatory Frameworks, Data Privacy, Predictive Analytics.},
      month = {December},
  }