Pura Vida, Public Interest: Costa Rica’s ENIA 2024–2027 and the Ethics-First Playbook for AI

A Quiet Contender Enters the AI Arena

While Silicon Valley debates risks and Brussels fine tunes rules, Costa Rica is quietly assembling one of the first national AI strategies in the Global South. The country that abolished its army and invested in education, health and nature is now directing that same civic DNA toward responsible automation.

The signal is simple: innovation is welcome, but it must serve people, not the other way around.

This does not come out of nowhere. Costa Rica’s public identity is tied to human rights, due process, and environmental stewardship. Those commitments translate naturally into digital governance that values transparency, accountability, and safety. ENIA 2024 to 2027 takes that tradition and applies it to algorithms, data lifecycles, and public procurement. The plan treats AI as public infrastructure that requires design rules, guardrails, and routine maintenance.

The thesis is pragmatic. ENIA is not a brochure about distant possibilities. It is a working blueprint that small and mid sized nations can adopt to direct AI toward public interest outcomes. The approach favors clear responsibilities, measurable controls, and interoperable standards. Think of it as Pura Vida for AI policy. Less hype. More hygiene. And just enough optimism to keep researchers, startups, and public agencies rowing in the same direction.

Why It Matters Globally

Putting Costa Rica on the AI regulation map is about interoperability, not isolation. ENIA’s risk-based lens can be mapped to the EU AI Act categories of minimal, limited, high, and prohibited risk. That shared vocabulary helps vendors and public agencies reuse compliance work across borders. On the other side of the Atlantic, U.S. policy is moving through sector playbooks in health, finance, and education. ENIA echoes that structure by pairing general principles with sector guidance, so a medical triage tool is validated differently than an edtech tutor or an anti-fraud model. A company that documents model cards, data lineage, and human oversight once can adapt that package for Brussels, Washington, and San José with fewer surprises.

ENIA also serves as proof of concept that smaller nations can lead on ethical deployment without moonshot budgets. The recipe is practical: publish procurement clauses that demand algorithmic impact assessments, bias testing, and audit rights. Stand up a lightweight registry of public sector AI systems that lists purpose, datasets, and contacts. Pilot regulatory sandboxes that let startups test under supervision. None of this requires massive infrastructure. It does require clarity about roles, deadlines, and evidence. The return is immediate: buyers know what to ask for, vendors know what to deliver, and citizens know where to file questions or complaints.

There is a market effect too. Clear rules lower transaction costs for responsible suppliers and raise them for those who cut corners. Investors notice when a jurisdiction reduces compliance uncertainty with stable templates, predictable reviews, and credible redress. That is especially relevant for nearshoring, cloud operations, and health data services that cross borders every day. ENIA can become a regional reference point that aligns Costa Rican projects with European clients and U.S. partners, creating a trusted supply chain for AI-enabled services.

Finally, the strategy extends a national brand that already carries weight. Costa Rica’s reputation for human rights and environmental stewardship now reaches into digital governance. Privacy by design, fairness testing, and energy-aware computing fit naturally beside national parks and the abolition of the army. Trust becomes an export. Schools, hospitals, and agencies gain tools that respect the person behind the data. Companies gain a policy partner that values innovation with guardrails. The message to the global community is straightforward: stewardship is not a constraint on AI, it is the reason people will choose to use it.

What’s in the Plan

ENIA anchors AI governance in a clear ethical framework. Systems that touch people’s lives must be transparent so users know when AI is involved and what it does. Accountability assigns named owners for models and decisions, with records that enable traceability and redress. Fairness requires active testing for disparate impact before and after deployment. Explainability fits the risk, a chatbot needs a simple disclosure while a medical triage tool needs structured explanations that a clinician can review. Proportionality keeps the tooling and safeguards aligned with the stakes, higher risk means stronger controls and more frequent checks.

Data protection by design runs throughout the plan. Consent must be freely given, informed, specific, and easy to withdraw. Purpose limitation prevents silent scope creep, the data collected for scheduling cannot be repurposed for profiling without a lawful basis and notice. Security is treated as hygiene, not an afterthought, with baseline controls for encryption, access, logging, and incident response. Privacy preserving techniques are promoted where useful, including pseudonymization for internal analytics and anonymization for open data releases, with clear tests to avoid reidentification.

Bias aware development is mandatory for sensitive use cases. ENIA calls for dataset governance that documents sources, selection criteria, and labeling methods. Teams are asked to check demographic representativeness where legally appropriate, and to monitor model drift over time. Independent audits provide an outside look at methodology and outcomes, which helps catch blind spots and build trust with regulators and users. The point is not to promise perfection, it is to prove disciplined effort and rapid correction.

Sector links make the framework practical. In education, the plan promotes AI literacy for students and teacher tooling that respects minors’ data, with human oversight for grading or placement. In health, decision support can help triage and manage waiting lists, but only with robust privacy controls, clinical validation, and clear escalation to human professionals. In public services, smart procurement templates require model documentation, testing protocols, and kill switch clauses. Fraud detection and eligibility scoring can be used with safeguards that prevent automated denials without human review and appeal.

International alignment keeps Costa Rica interoperable with larger markets. ENIA references OECD and UNESCO AI principles to guide public interest design. It encourages adoption of ISO and IEC management standards for AI lifecycle, information security, and risk management, which helps vendors reuse evidence across clients. Regional cooperation aims to attract responsible AI investment by offering predictable rules, compatible documentation, and channels for cross border incident coordination. The result is a policy stack that speaks a language investors and regulators already understand.

The Legislative Gap: When AI Moves Faster Than Law

Costa Rica's ENIA 2024-2027 emerges against a backdrop of profound legislative silence on artificial intelligence. Unlike the comprehensive regulatory frameworks taking shape in Europe or the sector-specific mandates proliferating in the United States, Costa Rica currently lacks dedicated AI legislation. This absence is not unique to the country—most Latin American jurisdictions find themselves in similar positions—but it creates a peculiar governance challenge: how does a nation implement an ambitious AI strategy when the legal scaffolding to enforce it remains under construction?

The answer lies in ENIA's deliberate reliance on existing legal infrastructure. Costa Rica's constitutional framework, particularly its robust protections for human dignity under Articles 20, 24, and 33 of the Political Constitution, provides foundational guardrails against algorithmic abuse. The habeas data doctrine, developed through decades of Constitutional Chamber jurisprudence, establishes that individuals possess inherent rights to know, access, correct, and oppose the processing of their personal information—principles that translate directly into AI accountability requirements. The proposed Data Protection Law (Bill 23097) would formalize these rights with enforcement mechanisms aligned with GDPR standards, creating a privacy baseline that constrains how AI systems can collect, process, and retain training data.

However, privacy law alone cannot address AI's distinctive challenges. Automated decision-making introduces risks that transcend data protection: algorithmic discrimination that perpetuates historical biases, opacity that prevents meaningful challenge of adverse decisions, and concentration of predictive power that can amplify existing inequalities. ENIA acknowledges this gap by establishing administrative guidance and procurement requirements that operate within ministerial authority, effectively creating a "soft law" regime that shapes AI deployment without awaiting legislative action. This pragmatic approach allows Costa Rica to move immediately while signaling to the Legislative Assembly the policy directions that future statutory frameworks should codify. The risk, of course, is that administrative guidance lacks the enforcement teeth and democratic legitimacy of parliamentary legislation—a tension that will require resolution as AI systems become more consequential in Costa Rican life.

The Regional Context: Latin America's Fragmented AI Governance

Costa Rica's ENIA must be understood within Latin America's broader struggle to govern artificial intelligence amid competing pressures from economic development, technological sovereignty, and human rights protection. The region presents a patchwork of approaches, each reflecting different national priorities and institutional capacities. Brazil, leveraging its strong data protection authority (ANPD) established under the LGPD, has begun drafting comprehensive AI regulation that would create risk-based obligations similar to the EU AI Act, with particular emphasis on protecting vulnerable populations and preventing discriminatory outcomes. Argentina's National AI Strategy, launched in 2023, focuses heavily on research capacity and talent development, treating AI governance as subordinate to industrial policy objectives.

Mexico's approach has been characterized by fragmentation, with sector regulators addressing AI applications within their domains—financial services, telecommunications, health—without overarching coordination or common principles. This creates compliance complexity for companies operating across sectors and limits the government's ability to respond to cross-cutting risks like deepfakes or synthetic media manipulation. Chile, meanwhile, has positioned itself as a data center hub for the region, attracting hyperscale cloud infrastructure from major technology companies, but has struggled to translate that physical infrastructure advantage into coherent AI governance that protects Chilean citizens' rights while maintaining the country's attractiveness to foreign investment.

What distinguishes Costa Rica's ENIA from these regional peers is its explicit prioritization of ethical guardrails as a competitive advantage rather than a compliance burden. While other countries fear that robust AI regulation might deter investment or slow adoption, Costa Rica's strategy posits that clear, predictable, rights-respecting rules will attract higher-quality projects and partners.

This thesis finds support in Costa Rica's existing reputation in adjacent domains: the country's strong data protection framework and cybersecurity incident response capabilities (developed in response to the 2022 Conti ransomware attack) have made it an attractive destination for business process outsourcing and cloud services where clients demand robust security and privacy controls. ENIA extends this logic to AI, betting that European and North American companies subject to strict AI regulations in their home markets will prefer Costa Rican partners who can demonstrate compliance-ready practices rather than jurisdictions where regulatory ambiguity creates downstream liability risks.

The regional dimension also matters for cross-border data flows and model deployment. Many AI systems trained on Latin American data operate from cloud infrastructure spanning multiple countries, with training datasets aggregated across jurisdictions and inference services delivered to users regardless of national boundaries. Costa Rica's emphasis on interoperability—aligning AI Impact Assessments with EU structures, adopting ISO/IEC standards, coordinating incident reporting with regional Computer Emergency Response Teams—positions it as a potential hub for responsible AI services that can legally and efficiently serve clients throughout the Americas. This regional leadership opportunity becomes particularly valuable as multinational companies seek to consolidate their Latin American operations in jurisdictions that offer regulatory clarity, technical competence, and alignment with global standards.

Governance Architecture & Legal Interoperability

ENIA works only if everyone knows their lane. The plan assigns a coordinating ministry or national commission to set policy, issue guidance, and publish metrics. Day to day oversight lives with sector regulators that already understand their domains. Health authorities validate clinical safety and patient rights. Financial supervisors handle model risk, credit decisioning, and anti fraud analytics. Telecom and digital agencies watch over connectivity, platform conduct, and data flows. This split keeps strategy centralized and technical decisions close to subject matter expertise.

Controls arrive through clear oversight instruments. High risk projects complete an AI Impact Assessment before procurement or launch. The AIIA records purpose, datasets, logic, risks, mitigations, and human oversight plans. Algorithmic audits bookend the lifecycle. A pre deployment audit checks documentation quality, testing methods, and security posture. A post deployment audit reviews drift, incidents, complaints, and bias findings. For key systems, ENIA promotes risk registers and model cards that summarize how the model was trained, what it should and should not do, and who is accountable for updates.

Public sector buying power turns principles into practice. Procurement templates require risk tiering, vendor attestations on data provenance and evaluation methods, and contractual audit rights. Incident notification timelines are specified so that agencies are not the last to know when models misbehave. Sunset or kill switch clauses let buyers pause or retire systems that no longer meet safety, privacy, or fairness expectations. These clauses protect both sides. Vendors gain clarity on performance duties. Agencies gain levers to manage real world risks.

Interoperability with privacy and cyber reform is built in. Consent must be valid and revocable. Purpose limitation and data minimization shape what training data may be used. Security baselines align with the national cybersecurity framework, including access control, encryption, logging, and incident reporting for critical infrastructure. When a security incident affects an AI system, routes to notify both the data authority and the cyber agency are defined, avoiding duplication and confusion. The same alignment applies to fines and corrective orders, which follow the upgraded data protection regime.

Legal portability matters for companies that serve multiple markets. ENIA encourages documentation that can travel. AIIAs mirror the structure used in EU risk assessments. Model cards can be mapped to ISO and IEC standards on AI management and information security. Contract clauses reflect common requirements seen in the EU and U.S. sector guidance, including human review for significant decisions and record keeping for accountability. By speaking this shared language, Costa Rican agencies and vendors can reuse compliance work with clients in Europe and North America, reducing cost without lowering the bar.

Finally, the architecture supports learning by doing. The coordinating body maintains a public catalog of government AI systems with purpose, risk tier, and contact points. Regulators publish anonymized audit lessons and testing patterns that others can adopt. Sandboxes allow controlled pilots with tight guardrails, rapid feedback, and clear exit criteria. This loop turns oversight into a source of practical templates that help the next project start stronger and move faster, while keeping trust at the center.

The Public-Private Divide: Different Rules for Different Actors?

One of ENIA's most significant structural questions, addressed only implicitly in the strategy document, concerns the differential treatment of public and private sector AI deployment. The framework's mandatory requirements—AI Impact Assessments, algorithmic audits, human oversight, explainability—apply most directly to government agencies and public procurement. Private sector entities face these obligations primarily when contracting with the state or when sector regulators (health, finance, telecommunications) extend ENIA principles through domain-specific rules. This creates a potential governance asymmetry: the same facial recognition system might face stringent oversight when deployed by the Ministry of Public Security but operate with minimal constraint when implemented by a private shopping mall.

This asymmetry reflects both practical and philosophical considerations. Governments wield coercive power—they can deny benefits, impose sanctions, restrict liberty—which justifies heightened scrutiny of their algorithmic systems. The constitutional principle of legality requires that public administration act only within explicit legal authorization, making it appropriate to demand that agencies demonstrate not only that AI systems are effective but that their deployment serves legitimate public purposes through proportionate means. Private entities, by contrast, generally enjoy broader freedom to innovate and deploy technology, constrained primarily by prohibitions against specific harms (discrimination, fraud, privacy violations) rather than affirmative obligations to demonstrate public benefit.

However, this public-private distinction grows increasingly untenable as private AI systems accumulate power over consequential life domains. Credit scoring algorithms determine access to housing and economic opportunity. Hiring platforms filter job candidates before human recruiters ever see their applications. Health insurers use predictive models to assess risk and set premiums. Educational technology platforms shape learning trajectories for millions of students. When private systems exercise these gatekeeping functions, the distinction between public and private power blurs—the person denied credit or excluded from employment experiences the decision as wielding state-like authority over their life prospects, regardless of whether the algorithm deciding their fate runs on government or corporate servers.

Costa Rica's ENIA acknowledges this reality indirectly through its sector-specific guidance and its call for extending impact assessment and audit requirements to "sensitive use cases" in private deployment. The strategy anticipates that health regulators will require clinical validation of diagnostic AI tools, that financial supervisors will demand bias testing in credit models, and that education authorities will mandate transparency in algorithmic grading or placement systems. This sector-by-sector approach offers flexibility and domain expertise but risks creating regulatory gaps in novel applications that don't fit neatly into existing regulatory jurisdictions—think AI-powered tenant screening, algorithmic management systems that control gig workers, or social media recommendation algorithms that shape political discourse.

The optimal governance structure likely involves a hybrid model: baseline obligations that apply to all high-risk AI systems regardless of deployer (public or private), with additional safeguards for governmental use given its coercive character. This approach would extend core ENIA principles—transparency, explainability, human oversight, bias monitoring, contestability—to consequential private AI applications while preserving enhanced requirements for public sector deployment. The practical challenge lies in defining "high-risk" and "consequential" with sufficient precision to provide legal certainty while capturing genuinely problematic applications. ENIA's risk-tiering framework provides a conceptual foundation, but translating it into enforceable legal obligations for private entities will require legislative action that the current strategy deliberately avoids prejudging.

Ethical Guardrails in Practice

ENIA treats transparency as a user right. People should never have to guess when an algorithm is in the loop, so notices appear at the point of interaction rather than being buried in a privacy policy. Chatbots identify themselves as automated, eligibility tools disclose the factors they evaluate, and biometric systems clearly explain the legal basis, purpose, and retention rules. Every high-impact use includes a human contact point who can answer questions and receive complaints. Agencies also maintain public FAQs and short model summaries so residents can understand the system without needing a computer science degree.

Human oversight is non-negotiable. Automation supports decisions, it does not replace due process. Any high-impact determination in credit, health, or public benefits receives human review before a final outcome. ENIA requires clear escalation paths, second-level review for edge cases, and documented reasons when staff override a model. Contestability is built in so people can challenge outcomes, submit additional evidence, and receive a timely human response. Oversight boards receive periodic reports on overrides, appeals, and error patterns to guide corrective action.

Fairness and bias management are treated as ongoing duties rather than one-off checks. Teams test for bias before launch and then monitor drift over time with dashboards that flag disparate error rates. Where the law allows, demographic representativeness is tracked against the use case, and samples are refreshed when the population changes. Redress mechanisms go beyond apologies. Models are retrained, thresholds are adjusted, and impacted groups are informed of fixes. Regulators can require independent audits when patterns persist or when a deployment touches sensitive rights.

Explainability is proportional to risk. A school chatbot provides a short, human-readable reason, while a clinical triage tool offers structured justifications that a doctor can evaluate. Credit models support adverse action notices that identify the main factors and how an applicant can improve. For regulators, technical annexes supply deeper detail, including model cards, features used, data lineage, validation results, and security controls. This layered approach avoids information overload while ensuring serious cases receive serious explanations.

ENIA draws clear lines where caution is warranted and uses sandboxes when careful experimentation serves the public interest. Biometric surveillance in public spaces, remote emotion inference, and opaque social scoring face strict necessity and proportionality tests along with heightened transparency and security. Regulatory sandboxes permit narrow, time-bound pilots under independent evaluation and defined exit criteria. If benefits are not proven and risks cannot be mitigated, the pilot stops.

Implementation Roadmap (2024–2027)

Foundation (2024–2027). The first phase, 2024 to 2025, lays the foundations. The government designates a coordinating body with clear authority, budget, and reporting lines, then issues draft secondary regulations that translate ENIA principles into operational rules for agencies and vendors. Procurement templates arrive early so new tenders embed risk-tiering, audit rights, and incident notification from day one. Pilot sandboxes begin with tightly scoped use cases in health, education, and public services, each with independent evaluation and public summaries of results. In parallel, a skills program launches for civil servants, regulators, and oversight bodies, focusing on AI impact assessments, audit basics, data protection by design, and contract management. The private sector is invited through open calls to participate in sandboxes and to co-develop practical guidance on model documentation, evaluation protocols, and security baselines.

2025 to 2026 moves from pilots to scale. High-risk public deployments must complete an AI Impact Assessment before launch, including risk registers, intended uses, data governance, evaluation methods, and human oversight plans. National audit guidelines are published so public buyers and private vendors share a common checklist covering accuracy, bias, robustness, explainability, and security. To support cross-border services and cloud supply chains, Costa Rica executes cooperation memoranda with regional peers, data protection authorities, and national CERTs, aligning incident reporting windows, evidence requirements, and mutual assistance channels. Agencies begin scheduling periodic post-deployment audits, while procurement units refresh vendor frameworks to reward compliance maturity and continuous monitoring rather than one-off attestations.

The maturity phase, 2026 to 2027, consolidates practice into sector playbooks. Sector-specific codes of conduct emerge for health, education, finance, and public safety, translating general rules into domain realities such as clinical validation thresholds, grading support limits, or transaction monitoring guardrails. Certification or labeling schemes recognize systems that meet defined ethical and technical standards, giving buyers and citizens a quick signal of trustworthiness. The government commits to periodic reviews of ENIA’s instruments, publishing KPI dashboards and lessons learned, and adjusting rules where evidence shows gaps or unintended effects. Sunset clauses ensure legacy systems are retired or upgraded if they fall short of current requirements.

KPIs keep the plan honest. Core metrics include the percentage of public AI projects with a completed and published AIIA; audit completion rates and corrective action follow-through; the share of identified bias issues resolved within defined timeframes; median time-to-notify for material incidents; and the level of AI-related foreign direct investment linked to the sandbox, certification, or procurement programs. Supplementary indicators track staff trained, sandbox pilots converted to production with safeguards, public satisfaction scores, and regulator capacity, such as audits per quarter and average review times. Targets should tighten each year, with agencies required to explain variances and publish remediation plans.

Execution details matter. Each ministry designates an AI lead accountable for the portfolio, maintains an updated inventory of systems, and publishes model cards for significant deployments. Legal, security, and procurement teams work from a common playbook so contract clauses, incident response, and audit rights stay aligned. Budget planning reserves funds for audits, red-teaming, and retraining data pipelines, not just initial builds. Civil society and academia are invited to quarterly briefings to review KPIs and provide independent feedback. International alignment continues through participation in OECD and ISO committees, ensuring Costa Rica’s labels and assessments are recognized by external buyers and investors.

Outcome. By sequencing foundations, scale, and maturity—and by measuring what counts—ENIA turns principles into daily practice. Agencies gain repeatable processes, vendors know the rules up front, and residents see tangible safeguards. The roadmap’s cadence creates a habit of responsible deployment that attracts high-quality investment while protecting rights and strengthening digital trust.

“Pura Vida” vs. Surveillance-First Models

Costa Rica’s approach to AI begins with people. The ENIA framework treats dignity, transparency, and proportionality as design inputs, not afterthoughts. That posture contrasts with surveillance-first or security-led models elsewhere, where deployment speed and population-level monitoring often dominate the objectives. In those environments, biometric systems, opaque risk scoring, and broad data fusion can precede clear legal limits. Costa Rica flips the sequence: set the limits, prove the need, then build.

This values-first stance does not mean abandoning innovation. It channels it. By requiring impact assessments, human oversight, and explainability, ENIA encourages solutions that solve concrete problems in health, education, and public services without normalizing mass surveillance. The promise is practical: better queues, safer roads, smarter procurement, fairer credit. The constraint is principled: no shortcuts that erode rights.

Replicability matters for peers in the Global South. Many countries do not have massive AI budgets or armies of auditors. Costa Rica’s blueprint is intentionally lightweight: common procurement templates, shared audit guidelines, narrow sandboxes, and clear escalation paths. These are building blocks that other mid-sized regulators can adopt without rebuilding their legal systems from scratch. The signal to investors is equally clear. Rights-respecting guardrails reduce compliance ambiguity, which lowers risk and can attract higher-quality AI vendors.

The guiding question still stands: can a country without an army become a moral compass for AI. Costa Rica’s wager is that trust, not coercion, is the durable foundation for digital transformation. If ENIA delivers measurable service gains while holding firm on rights, it offers a credible answer and a portable model.

The Talent Challenge: Building AI Capacity in a Small Market

Costa Rica's AI ambitions confront a structural constraint that policy documents tend to understate: the country's limited pool of specialized AI talent and the fierce global competition for those skills. While ENIA acknowledges the need for capacity building and includes provisions for training civil servants and regulators, the deeper challenge involves cultivating a domestic AI ecosystem capable of supporting the strategy's implementation while competing with technology hubs offering dramatically higher compensation.

The numbers are sobering. Costa Rica produces approximately 4,500 graduates annually in science, technology, engineering, and mathematics (STEM) fields from its public and private universities. Of these, only a small fraction specialize in machine learning, data science, or AI-relevant disciplines. The University of Costa Rica, the country's flagship public institution, offers strong computer science and electrical engineering programs, but its AI-specific coursework remains limited compared to leading technical universities in North America, Europe, or Asia.

The Costa Rica Institute of Technology (ITCR) has developed respectable programs in data science and computational engineering, but enrollment numbers remain modest. Private institutions like LEAD University and Universidad Cenfotec are beginning to address market demand with applied data science programs, but these are nascent efforts.

Meanwhile, global demand for AI talent has created unprecedented wage disparities. A machine learning engineer in San José, Costa Rica might command a salary of $40,000-$60,000 annually—excellent by local standards but a fraction of the $150,000-$300,000 that comparable roles offer in San Francisco, Seattle, or New York. Remote work, normalized during the COVID-19 pandemic, means talented Costa Rican engineers can access global salaries without relocating, creating brain drain pressure even for those who remain physically in the country. Companies like Amazon, Google, and Microsoft actively recruit Costa Rican talent for their regional engineering centers, offering compensation packages that public sector positions or local startups cannot match.

ENIA's capacity-building provisions address this challenge through multiple vectors, though whether they prove sufficient remains uncertain. The strategy calls for expanded AI curriculum at public universities, supported by partnerships with international research institutions that can provide faculty exchange, joint research projects, and access to computational resources. Scholarships for advanced degrees in AI-related fields, particularly those tied to return-to-Costa-Rica commitments, aim to develop expertise while mitigating brain drain. The establishment of AI research centers focused on problems relevant to Costa Rican contexts—agricultural optimization, biodiversity monitoring, tropical disease prediction—creates intellectually compelling projects that might retain talent who could earn more elsewhere but value working on socially meaningful challenges.

For the public sector specifically, ENIA proposes several mechanisms to acquire AI competence despite budgetary constraints. Shared services models allow smaller agencies to access centralized AI expertise rather than each ministry building redundant capabilities. Rotational fellowships bring private sector data scientists into government for defined terms, transferring knowledge while avoiding permanent salary commitments. Partnerships with universities create opportunities for faculty and graduate students to work on real-world government AI projects, benefiting research agendas while providing agencies with sophisticated analytical support. External audit panels, composed of experts who volunteer time or work under competitive procurement, provide technical review capacity that agencies need not maintain in-house.

The private sector talent challenge differs but remains acute. Costa Rica's existing technology sector—dominated by business process outsourcing, software development for foreign clients, and regional service centers for multinationals—provides a foundation, but these operations historically have not focused on cutting-edge AI development. The workforce is strong in software engineering, system administration, and technology-enabled services, but less deep in the specialized skills required for developing, deploying, and auditing sophisticated machine learning systems. ENIA's sandbox mechanisms and public-private pilot projects aim to create learning opportunities where local companies can develop AI capabilities on real problems with government as an anchor client, potentially growing expertise that can subsequently be commercialized regionally or globally.

Perhaps most critically, Costa Rica must decide whether to compete for top-tier AI talent or to instead focus on cultivating competent practitioners who can responsibly deploy, monitor, and govern AI systems developed elsewhere. The latter strategy—building governance capacity rather than frontier research capability—may better align with the country's resources and ENIA's emphasis on responsible deployment over technological leadership. Costa Rica need not train the researchers who will develop the next generation of foundation models; it must train the professionals who can evaluate vendor claims, conduct meaningful audits, interpret model outputs, and recognize when systems are failing or causing harm. This "smart buyer" approach requires different skills than cutting-edge AI development but remains intellectually demanding and critically important for ENIA's success.

Risks & Mitigations

Capability gaps are real. Many agencies lack data scientists, auditors, or contract managers with AI fluency. The mitigation is targeted capacity building and shared services. A central team can publish impact-assessment playbooks, model-card templates, and bias-testing checklists. Rotational fellowships and short accredited courses equip civil servants to read audit reports, question vendors, and spot red flags. Pooled panels of external experts can support smaller agencies during high-risk deployments.

Regulatory overreach and underreach are twin hazards. Rules that are too heavy freeze useful pilots. Rules that are too light invite harm. ENIA addresses both through scheduled reviews, public KPI dashboards, and feedback loops from sandboxes. Pilots operate under tight scopes, independent evaluation, and clear exit criteria. Findings then refine guidance, so the rulebook evolves with evidence rather than anecdotes.

Vendor capture is another risk when a few large providers set de facto standards. Competitive procurement, open technical specifications, and interoperability requirements counter that dynamic. Contracts should include audit rights, incident-notification timelines, data-portability terms, and the ability to switch providers without losing models or data lineage. Reference implementations and open benchmarks help public buyers compare options on performance and safety, not only on marketing claims.

Bias and exclusion require continuous attention. One-time fairness tests do not survive real-world drift. Mandatory monitoring plans track error rates across relevant groups, with thresholds that trigger model retraining or human review. Agencies publish plain-language notices on how to contest outcomes, and regulators can order independent audits when patterns persist. Where datasets are known to be skewed, procurement can require synthetic or augmented data strategies, along with governance to validate their limits.

None of these mitigations depend on limitless budgets. They depend on discipline. Clear roles, repeatable processes, and public reporting keep systems aligned with rights while delivering value. If Costa Rica keeps that balance, compliance becomes a competitive advantage rather than a compliance tax, and trust becomes the country’s most exportable technology.

Costa Rica’s AI strategy is less about coding machines, and more about coding values. ENIA 2024–2027 treats dignity, transparency, and accountability as first-class requirements, the same way engineers treat uptime and latency. That design choice turns ethics from a compliance chore into a product feature users can feel and institutions can defend.

Looking ahead, ethics becomes a competitive advantage and trust a national export. Jurisdictions that can prove their systems are fair, explainable, and secure will win the confidence of citizens, regulators, and investors. For Costa Rica, that means attracting higher quality projects in health, education, and public services, as well as private R&D that prefers clear rules over regulatory guesswork.

The invitation to builders is simple: align early and move faster. Publish plain-language notices, complete impact assessments before launch, and adopt audit-ready documentation from day one. Use ENIA’s procurement templates, sandbox controls, and bias-monitoring playbooks to shorten the path from pilot to production without cutting corners.

If you are deploying or buying AI in Costa Rica, make transparency your default, human oversight your safety net, and data protection your operating system. Build to the guardrails, not around them. That is how responsible deployment scales, public trust compounds, and Costa Rica turns values into velocity.

Environmental Sustainability: The Carbon Cost of Computation

An issue that ENIA 2024-2027 touches only obliquely but that deserves greater prominence involves the environmental implications of AI deployment—a concern particularly salient for a nation that has built its international brand on environmental stewardship and carbon neutrality commitments. Training large language models and other sophisticated AI systems consumes enormous amounts of electrical energy, with recent estimates suggesting that training GPT-3 generated approximately 552 metric tons of carbon dioxide equivalent, roughly equivalent to driving 1.2 million miles in an average gasoline-powered car. As AI systems grow more complex and widespread, their aggregate energy consumption and carbon footprint become material considerations for climate policy.

Costa Rica's electricity matrix, powered predominantly by renewable sources—hydroelectric, geothermal, wind, and solar—provides a significant advantage in this domain. The country has achieved periods of 100% renewable electricity generation and consistently maintains renewable penetration above 95%. This means that AI computation performed within Costa Rica carries a dramatically lower carbon footprint than identical workloads run in jurisdictions relying heavily on fossil fuels. For companies subject to scope 2 emissions reporting requirements (indirect greenhouse gas emissions from purchased electricity), deploying AI infrastructure in Costa Rica rather than coal- or gas-dependent regions offers meaningful climate benefits.

ENIA should leverage this advantage more explicitly, positioning Costa Rica as a destination for "green AI" deployment. The strategy could establish preferential procurement scoring for AI solutions that demonstrate energy efficiency, reward vendors who optimize model architectures to reduce computational intensity, and require environmental impact assessments for large-scale AI infrastructure projects. Public reporting on the energy consumption of major government AI systems would create transparency and accountability around computational resource use, while also generating data to inform best practices. Costa Rica could develop technical standards for energy-efficient AI deployment, potentially creating certification schemes that become regionally or internationally recognized markers of sustainable artificial intelligence.

The talent and environmental dimensions of Costa Rica's AI strategy reveal a broader truth: successful AI governance requires confronting resource constraints honestly while identifying asymmetric advantages that smaller nations can exploit. Costa Rica cannot outspend technology superpowers on compute infrastructure or talent acquisition, but it can build governance expertise, exploit clean energy advantages, and position itself as a trusted partner for companies and countries that value responsible AI deployment. ENIA's success will ultimately be measured not by whether Costa Rica develops the most sophisticated AI systems, but by whether it demonstrates that human-centered governance can coexist with—and indeed enable—technological progress.

SEBASTIAN JIMENEZ

Attorney at Law