SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
arXiv:2602.18431v2 Announce Type: replace-cross Abstract: Motivated by the problem of assigning mediators to cases in the Kenyan judicial system, we study an online resource allocation problem where incoming tasks (cases) must be immediately assigned to available, capacity-constrained resources (mediators). The resources differ in their quality, which may need to be learned. In addition, resources can only be assigned to a subset of tasks that overlaps to varying degrees with the subset of tasks other resources can be assigned to. The objective is to maximize task completion while satisfying s
The paper tackles a real-world problem in the Kenyan judiciary, indicating a growing trend of AI applications moving into complex, real-time resource allocation challenges within public services.
This research demonstrates how AI can optimize critical public services by learning preferences and managing dynamic resource constraints, potentially setting a precedent for wider adoption in other sectors.
The adoption of such systems could significantly improve judicial efficiency and access to justice in jurisdictions willing to implement AI-driven mediation assignment, leading to better resource utilization.
- · AI/ML research community
- · Judicial systems adopting AI
- · Citizens in jurisdictions with optimized services
- · Inefficient manual resource allocation systems
- · Bureaucracies resistant to AI integration
Increased efficiency in judicial mediation processes reducing case backlogs.
Broader public and governmental acceptance of AI for optimizing complex, sensitive public services beyond simple task automation.
The development of specialized AI companies focused on public service optimization and resource management in developing economies.
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Read at arXiv cs.LG