THE IMPACT OF EXPLAINABLE AI ON CROWD MANAGEMENT EFFICIENCY: A STAKEHOLDERPERSPECTIVE

This study examines how explainable artificial intelligence (XAI) improvesSaudi crowd management efficiency and transparency.Large-scale gatherings, especially culturally and religiously significant ones, require intelligent systems that stakeholders can trust and understand.Integration and stakeholder reliance on XAI solutions continue to hinder adoption.This research aims to bridge the gap between technological advancement and realworld crowd management by addressing transparency, reliability, and integration.The results showed that stakeholders valued XAI for real-time data analysis and decision-making, but technical challenges, infrastructure constraints, and a lack of understanding prevented its full integration.Stakeholders emphasized improved decision-making, enhanced operational efficiency, and positive stakeholder engagement as major benefits.However, technical barriers, lack of understanding, and infrastructure limitations were noted.Explainable AI improves risk prediction, and better resource allocation.Different stakeholders expressed varied needs for XAI explanations, requiring customization based on role clarity and transparency.XAI also improved interdepartmental collaboration, data sharing, and decision-making alignment, while concerns about data privacy, security risks, and regulatory compliance were highlighted.The study emphasizes the need for adaptable XAI systems for different user groups, contributing to contingency and stakeholder theory.