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

Mohammed Alammar1, Abdulrahman Al Ayidh2, Mohamed Abbas2

  • 1Electrical Engineering Department, College of Engineering, King Khalid University, 61421, Abha, Saudi Arabia. mmalamar@kku.edu.sa.

Scientific Reports
|March 20, 2026
PubMed
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This study introduces an explainable AI (XAI) framework for Public Safety Networks (PSNs) resource allocation. It enhances transparency and trust in emergency response decision-making.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • Public Safety Networks (PSNs) are vital for emergency response coordination.
  • Resource allocation in PSNs is complex due to dynamic demands and limited resources.
  • Traditional AI models lack transparency, hindering trust and potentially causing bias.

Purpose of the Study:

  • To develop an explainable AI (XAI) framework for resource allocation in PSNs.
  • To enhance transparency, fairness, and reliability in AI-driven allocation decisions.
  • To foster trust among stakeholders in emergency management.

Main Methods:

  • Integration of SHAP and LIME for global and local interpretability.
  • Incorporation of Bayesian uncertainty modeling for enhanced reliability.
Keywords:
Bayesian uncertaintyExplainable AIFairness in AILIMEPublic safety networksResource allocationSHAP

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  • Experimental evaluation of the XAI framework's performance.

    • Main Results:

    • The XAI framework optimizes resource distribution in PSNs.
    • Enhanced transparency and explainability in allocation decisions.
    • Improved decision reliability through uncertainty modeling.

    Conclusions:

    • The XAI framework bridges the gap between AI optimization and human interpretability.
    • Contributes to equitable, accountable, and transparent decision-making in PSNs.
    • Ultimately improves emergency response efficiency and public safety outcomes.