Abstract
Many public-sector artificial intelligence systems fail not at the point of model development, but at the point of deployment. Systems that perform well in internal testing may still stall because the receiving institution lacks the approvals, data arrangements, human oversight, operational capacity, fiscal continuity, or legal clarity needed for broader rollout. Existing responsible AI and model evaluation frameworks are valuable, but they primarily assess models, datasets, and developer-side processes, not the readiness of the institution that must use the system in practice.
This paper introduces Institutional Alignment Readiness (IAR), a five-dimensional framework for assessing deployment readiness in public systems, designed for resource-constrained settings where the gap between technical viability and responsible deployment is most acute.
Framework
IAR assesses the receiving institution rather than the artifact alone, across five dimensions: institutional and operational compatibility, data ecosystem maturity, human oversight capacity, fiscal sustainability, and regulatory alignment readiness. It is meant to complement, not replace, established AI evaluation tools, and it supports staging decisions such as no-go, pilot-only, or readiness for broader deployment.
Grounding cases
The framework is grounded in two anonymized operational cases from a large public education system: an image-based anthropometric screening tool and a speech-analysis system for early learning-risk identification. Both reached technically viable stages but could not advance to broader rollout for institutional rather than technical reasons.
Relevance to ECAIR
IAR formalizes the logic behind ECAIR's build-and-prove-before-handover model: a system is only ready when the office that must run it is ready to own it. The framework offers a shared vocabulary for the staging decisions ECAIR already makes across its portfolio.
Citation
Legara, E. F., Jose, E. D., & Martinez, P. J. (2026). Beyond Model Readiness: Institutional Readiness for AI Deployment in Public Systems. 2nd Workshop on Technical AI Governance Research (TAIGR) at ICML 2026, Seoul, South Korea. SSRN: https://ssrn.com/abstract=6779119 · DOI: 10.2139/ssrn.6779119