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<p>Public Safety Networks (PSNs) play a crucial role in enabling efficient communication and coordination among first responders and emergency management agencies during critical situations such as natural disasters, terrorist attacks, and public health crises. These networks must facilitate timely and reliable data transmission to ensure informed decision-making in dynamic and high-pressure environments. However, effective resource allocation within PSNs is a challenging task due to fluctuating demands, limited bandwidth, and the need for real-time adaptability. Traditional AI-based allocation models, while capable of optimizing resource distribution, often function as black-box systems, offering little transparency into the decision-making process. The lack of explainability in these models raises concerns among stakeholders, as opaque AI-driven decisions may result in inefficiencies, biases, or unintended disparities in resource allocation. To address these challenges, this study presents an XAI-driven framework for resource allocation that integrates SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) to enhance both global and local interpretability. By leveraging these techniques, the proposed model improves transparency, ensures fairness, and provides interpretable justifications for allocation decisions. Furthermore, Bayesian uncertainty modeling is incorporated to enhance decision reliability, particularly in rapidly evolving emergency scenarios. Experimental evaluations demonstrate that the framework optimizes resource distribution while maintaining explainability, fostering trust among emergency responders, policymakers, and stakeholders. By bridging the gap between AI-driven optimization and human interpretability, this research contributes to the advancement of equitable, accountable, and transparent decision-making processes in PSNs, ultimately improving emergency response efficiency and public safety outcomes.</p>

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Explainable AI (XAI) for transparent resource allocation in public safety communications networks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月19日

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  • Mohammed Alammar,
  • Abdulrahman Al Ayidh,
  • Mohamed Abbas,
  • Muneer Parayangat

摘要

Public Safety Networks (PSNs) play a crucial role in enabling efficient communication and coordination among first responders and emergency management agencies during critical situations such as natural disasters, terrorist attacks, and public health crises. These networks must facilitate timely and reliable data transmission to ensure informed decision-making in dynamic and high-pressure environments. However, effective resource allocation within PSNs is a challenging task due to fluctuating demands, limited bandwidth, and the need for real-time adaptability. Traditional AI-based allocation models, while capable of optimizing resource distribution, often function as black-box systems, offering little transparency into the decision-making process. The lack of explainability in these models raises concerns among stakeholders, as opaque AI-driven decisions may result in inefficiencies, biases, or unintended disparities in resource allocation. To address these challenges, this study presents an XAI-driven framework for resource allocation that integrates SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) to enhance both global and local interpretability. By leveraging these techniques, the proposed model improves transparency, ensures fairness, and provides interpretable justifications for allocation decisions. Furthermore, Bayesian uncertainty modeling is incorporated to enhance decision reliability, particularly in rapidly evolving emergency scenarios. Experimental evaluations demonstrate that the framework optimizes resource distribution while maintaining explainability, fostering trust among emergency responders, policymakers, and stakeholders. By bridging the gap between AI-driven optimization and human interpretability, this research contributes to the advancement of equitable, accountable, and transparent decision-making processes in PSNs, ultimately improving emergency response efficiency and public safety outcomes.