Human‑Centered Governance for AI‑Augmented Decision Support in Public‑Sector Logistics
DOI:
https://doi.org/10.47941/ijscl.3372Keywords:
Artificial Intelligence Governance, Public-Sector Logistics, Decision Support Systems (DSS), Human–AI Collaboration, Algorithmic AccountabilityAbstract
Purpose: The purpose of this study was to investigate how artificial intelligence (AI)–augmented decision support systems (DSS) in public-sector logistics (PSL) can be designed to balance operational efficiency with democratic accountability, fairness, and human-centered governance.
Methodology: The study employed a qualitative multi-method design combining comparative-historical analysis (2015–2025), explanatory multiple-case studies, and scenario-based policy analysis. Data sources included peer-reviewed journal articles, official reports, and regulatory frameworks related to AI governance in logistics and public administration. The data was analyzed through thematic coding and cross-case pattern analysis using both manual and software-assisted approaches. Triangulation was applied to ensure validity and reliability of findings.
Findings: The results indicated that AI-based DSS consistently enhanced operational performance, achieving an average of 20% improvement in routing and resource allocation efficiency across cases. However, these aggregate gains often masked inequities in service distribution and raised questions about legitimacy in automated decision-making. Human-in-the-loop (HITL) and human-on-the-loop (HOTL) hybrid models were found to reduce system errors by nearly one-third and to increase user confidence, particularly under uncertain or high-stakes conditions.
Unique Contribution to Theory, Policy and Practice: This study contributes to the theoretical understanding of sociotechnical systems by framing a governance model that integrates human judgment with algorithmic intelligence. It provides practical policy recommendations for institutionalizing explainability, ethical safeguards, and longitudinal equity assessments. The findings advocate that sustainable and accountable public-sector logistics can be achieved not through full automation but through deliberately engineered human–AI collaboration grounded in transparency and oversight.
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Copyright (c) 2025 Ravindra Kumar Patro, Susruta Satapathy, Vikas Gupta
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