🤖 AI Summary
This study addresses the persistent challenge of scaling artificial intelligence systems in the public sector, where institutional barriers—such as inadequate approval processes, weak data governance, insufficient oversight capacity, fiscal unsustainability, or regulatory ambiguity—often impede deployment despite technical feasibility. To bridge this gap, the authors propose the Institutional Alignment Readiness (IAR) framework, which shifts focus from the AI model itself to the receiving institution. The framework assesses readiness across five dimensions: institutional and operational compatibility, data ecosystem maturity, human oversight capacity, fiscal sustainability, and regulatory alignment, offering a practical evaluation tool tailored for resource-constrained settings. Through qualitative analysis of two anonymized public education system cases, the IAR framework effectively identifies institutional bottlenecks and informs phased deployment decisions—ranging from prohibition and piloting to full-scale implementation—thereby addressing a critical void in existing AI assessment methodologies at the institutional level.
📝 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. We introduce Institutional Alignment Readiness (IAR), a five-dimensional framework for assessing deployment readiness in public systems. The framework is designed for resource-constrained settings, where gaps between technical viability and responsible deployment are most acute. It 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. We use these cases to motivate a practical readiness framework covering institutional and operational compatibility, data ecosystem maturity, human oversight capacity, fiscal sustainability, and regulatory alignment readiness. IAR is designed to complement, not replace, established AI evaluation tools. It assesses the receiving institution rather than the artifact alone and supports staging decisions such as no-go, pilot-only, or readiness for broader deployment.