SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary
Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence. AI and Social Good. Pages 7138-7146.
https://doi.org/10.24963/ijcai.2026/794
Keywords
- Agent-based and Multi-agent Systems: Agent-based and Multi-agent Systems
- AI Ethics, Trust, Fairness: AI Ethics, Trust, Fairness
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 soft capacity constraints across all the resources. The scale of the real-world problem poses substantial challenges, since there are over 2000 mediators, and a multitude of combinations of geographic locations (87) and case types (12) that each mediator is qualified to work on. Together, these features—unknown quality of new resources (newly onboarded mediators), soft capacity constraints (due to the mandate to assign cases without delay), and high-dimensional state space—make existing scheduling and resource allocation algorithms either inapplicable or inefficient. We formalize the problem in a tractable manner, using a quadratic program formulation for assignment and a multi-agent bandit style framework for learning. We demonstrate the key properties and advantages of our new algorithm, SMaRT (Selecting Mediators that are Right for the Task), compared with baselines on some stylized instances of the mediator allocation problem. We then turn to considering its application to real-world data on cases and mediators from the Kenyan Judiciary. SMaRT outperforms baselines and allows for controlling the tradeoff between the strictness of the capacity constraints and overall case resolution rates, both in situations where mediator quality is known beforehand and when the problem is bandit-like in that learning is part of the problem definition. On the strength of these results, we plan to conduct a randomized controlled trial in which we deploy SMaRT in the Judiciary's mediation management system.
@inproceedings{ijcai2026p794,
title = {SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary},
author = {Farabi, Shafkat and Marti Pinto, Didac and LU, Wei and Ramos-Maqueda, Manuel and Das, Sanmay and Deeb, Antoine and Sautmann, Anja},
booktitle = {Proceedings of the Thirty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-26}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Diego Calvanese},
pages = {7138--7146},
year = {2026},
month = {8},
note = {AI and Social Good},
doi = {10.24963/ijcai.2026/794},
url = {https://doi.org/10.24963/ijcai.2026/794},
}
TY - CPAPER TI - SMaRT: Online Reusable Resource Assignment and an Application to Mediation in the Kenyan Judiciary AU - Farabi, Shafkat AU - Marti Pinto, Didac AU - LU, Wei AU - Ramos-Maqueda, Manuel AU - Das, Sanmay AU - Deeb, Antoine AU - Sautmann, Anja T2 - Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-26 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2026 SP - 7138 EP - 7146 DO - 10.24963/ijcai.2026/794 UR - https://doi.org/10.24963/ijcai.2026/794 ER -Copied
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