What CARICOM Actually Needs from an AI Policy Framework

The European Union AI Act runs to 144 pages. The United Kingdom's AI Safety framework has produced multiple white papers, two international summits, and an ongoing stream of consultation documents. The United States has issued executive orders, launched the AI Safety Institute, and generated enough policy documents to fill a bookshelf. All of these frameworks are serious work. They represent genuine intellectual effort by capable people who care about getting AI governance right.

None of them is particularly useful for Jamaica, Barbados, Trinidad, or any other CARICOM member state trying to build a functioning AI governance framework in 2026.

That is not a criticism. It is a statement of context. The EU AI Act was designed for an economy of 450 million people with a unified regulatory architecture, deep institutional capacity across member states, and a domestic AI industry substantial enough to be regulated. CARICOM is fourteen full member states, most with populations under a million, all with their own regulatory bodies of varying capacity, none with a domestic AI industry of significant scale, and all deeply dependent on AI systems built entirely elsewhere for infrastructure they cannot meaningfully audit.

Importing a framework designed for that context and applying it here is not just inefficient. In some respects it is actively counterproductive. Understanding why requires being specific about what is different and what a genuinely Caribbean approach needs to address instead.

What CARICOM Has Done So Far

To be fair about where we are: CARICOM has not been completely inactive on digital economy governance. The CARICOM Digital Agenda has been in development, there have been regional consultations on data protection harmonization, and individual member states have made progress on specific pieces of the regulatory infrastructure. Jamaica passed the Data Protection Act. Trinidad's legislation has moved. Barbados has been active on cybersecurity frameworks.

What does not yet exist in any coherent form is an AI-specific governance framework that reflects CARICOM's actual situation. Data protection is a foundation for AI governance, but it is not the same thing. A data protection act tells you how personal information can be collected, stored, and used. It does not tell you how an automated decision system should be designed, tested, evaluated for bias, or challenged by a citizen who was denied a benefit because of its output.

That gap is where most of the risk lives.

The question for Caribbean AI governance is not how to regulate the AI industry. We don't have one yet. The question is how to govern the AI systems being imported from elsewhere and deployed on Caribbean citizens without Caribbean oversight.

The Import Problem Nobody Is Talking About

There is a framing problem at the centre of most Caribbean AI policy conversations, and it is important to name it clearly. When large economies think about AI governance, they are primarily thinking about governing their domestic AI industry: the companies building AI systems, the research institutions developing models, the platforms deploying AI-driven products. The governance question is fundamentally about what obligations AI builders have to the people affected by their systems.

The Caribbean barely has that problem. We have very few AI companies and almost no AI model development happening domestically at scale. What we have instead is a different and in some ways more difficult problem: we are consumers of AI systems built entirely elsewhere, by companies operating under foreign regulatory regimes, trained on data that may not represent our populations, and deployed into Caribbean institutions with minimal transparency about how those systems make decisions.

Every time a Caribbean bank uses a credit scoring model built by a North American vendor, it is deploying AI governance made by someone else, for someone else's context, on Caribbean citizens. Every time a Caribbean government deploys an automated benefits eligibility system, it is running an algorithmic decision process built to assumptions that may not hold in a Caribbean social context. Every time a Caribbean school uses an AI-based learning platform, the adaptive models underlying it were trained overwhelmingly on data from other places and other kinds of learners.

A CARICOM AI governance framework that focuses primarily on regulating domestic AI development is a framework addressing the wrong problem. The urgent work is building the institutional capacity to evaluate, audit, and where necessary reject AI systems imported from outside the region.

Five Things a Genuine CARICOM AI Policy Actually Needs

1. Algorithmic Accountability for Imported Systems

Any AI system used in a consequential decision-making context in a CARICOM state, credit decisions, government benefits, law enforcement risk assessment, health triage, educational assessment, must be subject to a minimum transparency standard. This means the deploying institution must be able to explain, in plain terms, how the system makes its decisions, what data it was trained on, how it has been tested for accuracy and bias in a Caribbean context specifically, and what appeal mechanism exists for a citizen who believes the decision was wrong.

This is not technically burdensome. It requires vendors to provide documentation they should already have. The barrier is not technical. It is contractual and political. CARICOM governments need to make this a procurement standard, which means it needs to be in every public sector AI contract.

2. Data Sovereignty That Accounts for Scale

Data sovereignty discussions in the Caribbean tend to focus on cloud infrastructure: where is data stored, who has jurisdictional access, what happens in a political dispute between data storage location and data origin country. These are real concerns and they deserve serious attention.

But there is a deeper sovereignty question that gets less attention. When Caribbean citizens' data is used to train AI models in other jurisdictions, those models embed Caribbean patterns, behaviors, and characteristics in a system that the Caribbean has no ownership over and no visibility into. The data leaves. The model stays elsewhere. And the model, which contains the distilled intelligence derived from Caribbean data, is then sold back to Caribbean institutions as a product they license rather than own.

A serious Caribbean data sovereignty framework needs to address this loop, not just cloud storage location.

3. Caribbean Dialect and Cultural Representation Requirements

Language AI systems, from speech recognition to natural language processing to large language models, are built on training data that is overwhelmingly English in its standard American or British form, with some representation of major European languages. Caribbean English, Jamaican Patois, Trinidad English Creole, Haitian Creole, Papiamento, and the other languages and dialects of the Caribbean are represented either minimally or not at all in the training data of the AI systems currently being deployed in our region.

This has concrete consequences. Voice recognition systems that work accurately for an American accent fail on a Jamaican accent. Sentiment analysis tools trained on American English social media data misclassify the sentiment of Caribbean social media posts. Educational AI that adapts to a student's comprehension level is calibrated for comprehension patterns that may not match how Caribbean students express their understanding.

These are not minor inconveniences. They are systematic sources of AI underperformance in Caribbean contexts. A CARICOM AI policy should establish minimum dialect and cultural representation standards for AI systems used in public-facing Caribbean applications, and create incentives for Caribbean-specific training data development.

4. A Regional AI Capacity Body With Real Authority

Fourteen small states individually trying to build AI governance capacity is a recipe for duplicated effort, inconsistent standards, and institutional fragility. A CARICOM AI capacity body, modeled in some respects on the Caribbean Court of Justice's regional authority model but focused on technical AI governance, could provide member states with shared auditing capacity, shared technical expertise, shared procurement standards, and a coordinated voice in international AI governance forums where Caribbean interests are currently unrepresented.

This body does not need to replace domestic regulators. It needs to supplement them with capabilities that no individual CARICOM state can cost-effectively develop alone. Shared AI auditing tools. A shared register of AI systems deployed in CARICOM public sectors. Shared technical assistance for governments evaluating AI procurement decisions. The infrastructure of governance, not just its principles.

5. Talent Retention Mechanisms

Every AI governance framework is only as good as the people implementing it. The Caribbean has a meaningful pool of AI talent. The problem is that the economic incentives to keep that talent in the Caribbean are heavily outcompeted by the economic incentives to leave. A developer in Kingston with genuine AI skills can triple their income by taking a remote role for a North American company. A data scientist in Port of Spain has the same calculation to make.

Brain drain is not new in the Caribbean. But AI is a domain where the loss is particularly acute because the talent is so concentrated. Losing ten AI practitioners from a Caribbean economy is not a statistical blip. It can represent a significant fraction of the country's operational AI capability.

A serious CARICOM AI policy addresses this directly. Not with restrictions on where people can work, but with incentives for AI talent to work on Caribbean problems: public sector AI fellowships, grants for Caribbean-focused AI projects, tax incentives for AI companies headquartered in the Caribbean, and procurement preferences for AI solutions developed by Caribbean companies.

The Political Economy of Caribbean AI Governance

Building the CARICOM AI governance framework described above requires political will. That is not in infinite supply anywhere, and in small states with limited administrative capacity and competing policy priorities, it is a particularly scarce resource.

But the political economy also has a favorable dimension that is underappreciated. Caribbean governments that establish rigorous AI governance frameworks early gain a genuine competitive advantage. For investors and companies looking to deploy AI in the Caribbean or use it as a regional hub, governance clarity is an asset, not an obstacle. The Caribbean's geographical position, timezone alignment with both North America and Europe, English-speaking workforce, and established financial services infrastructure make it a credible AI services hub if the governance environment is reliable.

The countries that establish trustworthy AI frameworks first attract the investment, the companies, and the talent that build on those frameworks. The countries that wait for others to establish standards, then adopt them passively, get the regulatory overhead without the first-mover advantages.

The CARICOM AI governance conversation is not just about protecting citizens from AI harm. It is about positioning the Caribbean to capture value from AI rather than only experiencing its risks. Those are different conversations with different levels of urgency for different political audiences, and making that distinction clearly is part of building the political will to act.

A Practical Path Forward

None of what I have described above requires starting from zero. Much of the technical work on AI governance has been done globally. CARICOM does not need to reinvent the conceptual framework for algorithmic accountability or rebuild the theory of data sovereignty from scratch. What it needs is the institutional structures to implement that work in Caribbean conditions, and the political decisions to prioritize doing so.

The most actionable near-term step for any CARICOM member state is to establish a minimum AI procurement standard for public sector AI contracts. Require vendors to document how their systems work, what data trained them, how they've been tested, what their accuracy rates are on Caribbean-context data specifically, and what appeal mechanisms exist. That standard creates the enforcement mechanism that currently does not exist and begins generating the institutional knowledge to build more sophisticated governance over time.

It is not the complete framework. But it is the foundation that every other element of the framework depends on. And it can be implemented this year, in any CARICOM state that chooses to do it, without waiting for regional coordination or new legislation.

The Caribbean has always been capable of more than it has been given credit for. The challenge with AI governance is not capability. It is the urgency to treat it as the priority it is, before the systems being deployed without oversight have been in place long enough that changing course becomes far harder than building right from the start.