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Caribbean governments are moving from experimenting with artificial intelligence toward using it in real public workflows. Caribbean News Global has described AI agents as a potential new operational layer for digital government, while its coverage of Trinidad and Tobago has focused on using AI to improve public-sector productivity and citizen outcomes. The Caribbean Telecommunications Union’s first regional AI Forum, held in July, also put governance, harmonisation, resilience, and digital skills at the center of the region’s AI agenda.
The next step should be operational accountability. Before governments scale AI-assisted decisions, they should require a short AI decision record for high-impact public workflows.
A decision record would travel with an AI-assisted action. It would identify the service involved, what the AI recommended or executed, any known data limitation or uncertainty, the official who approved or overrode the result when human authorisation is required, the reason for a material override, and the route for review or appeal. Personal or security-sensitive information would remain protected. The record would give agencies a usable audit trail instead of forcing them to reconstruct one after a complaint or failure.
This matters because public-sector AI can create a misleading sense of efficiency. A tool may process applications faster while shifting difficult cases to employees. An agent may reduce routine clicks while creating new verification work. A model may flag an unusual transaction correctly most of the time but still require judgment when the consequences for a citizen are substantial. Without a decision record, managers can see throughput while missing how much human work keeps the system accurate and fair.
The record should stay lightweight. Governments do not need a new form for every trivial automated task. They can reserve it for workflows where an AI output can materially affect access to a service, a permit, a payment, an enforcement action, a procurement choice, or another consequential government decision.
Caribbean institutions are already building the skills needed for this approach. Caribbean News Global reported on an AI Academy for public-sector professionals built around practical, responsible use in a secure sandbox, with participants developing prototype use cases. A decision-record requirement would give those prototypes a common governance layer from the beginning.
It could also advance regional coordination. The CTU forum called for greater harmonisation around Caribbean AI governance. A shared decision-record template would give governments something concrete to harmonise without demanding identical laws, systems, or procurement contracts. Each country could preserve its own legal rules while using a common structure for documenting how AI participates in consequential public work.
That structure would also help leaders compare vendors and projects. Instead of asking only how many transactions an AI system processed, an agency could ask how often staff overrode it, why they did so, which data gaps appeared most often, and how many cases required citizen review. Those measures reveal whether automation is genuinely reducing friction or merely relocating it.
For citizens, the same logic can support trust. When appropriate, agencies could provide a concise notice explaining that AI assisted a decision, what role it played, and how a person can request human review. That turns transparency from a broad promise into a specific service feature.
The Caribbean has an opportunity to build public-sector AI around regional realities rather than import governance after systems are already entrenched. A simple decision record would not solve every problem. It would give public managers, auditors, employees, and citizens a common account of what the technology actually did.
Before governments ask AI to make public administration faster, they should make sure they can still explain how important decisions were made.
