Explainable AI and Real-Time Decisioning for Automated Government Case Management in Saudi Arabia

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@article{1724296,
      author = {Arshad Pasha Mohammed},
      title = {Explainable AI and Real-Time Decisioning for Automated Government Case Management in Saudi Arabia},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {3308-3321},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724296.pdf},
      abstract = {Automated government case management is moving from digitised workflow toward intelligent triage, eligibility assessment, risk prioritisation and service routing. In Saudi Arabia, this transition is especially consequential because digital government programmes increasingly connect high-volume public services with data-intensive decision processes. The central challenge is not simply whether artificial intelligence can accelerate case handling, but whether decisions can remain timely, reviewable, understandable and contestable when models operate at operational speed. This review examines how explainable artificial intelligence and real-time decisioning can be combined for automated government case management in Saudi Arabia. It synthesises thirty DOI-verified studies published from 2020 to 2025 across explainability, human-AI decision support, public-sector algorithmic governance, Saudi digital government and streaming analytics. The review develops a socio-technical framework in which case intake, data quality, machine reasoning, rule validation, explanation generation, human oversight, notification, appeal and continuous monitoring form one auditable decision loop. The analysis finds that explainability is valuable only when it is matched to the decision recipient and linked to operational controls; generic feature-attribution displays alone do not guarantee justified or trustworthy outcomes. Real-time systems also require explicit latency budgets, uncertainty handling, concept-drift monitoring and escalation rules so that speed does not suppress due process. For Saudi public agencies, successful adoption depends on combining interoperable data, secure governance, accountable ownership and Arabic-capable explanation interfaces with measurable human override and appeal mechanisms. The study proposes evaluation criteria spanning predictive validity, explanation fidelity, decision latency, fairness, reviewability and institutional accountability. It concludes that explainable real-time case management should be designed as governed decision infrastructure rather than as an isolated prediction model.},
      keywords = {explainable AI; real-time decisioning; automated case management; digital government; public-sector AI; Saudi Arabia; algorithmic accountability; human oversight},
      month = {October},
      doi = {https://doi.org/10.64388/IREV10I4-1724296}
  }