Artificial Intelligence · Public Sector

AI in Latin American Governments: Beyond the Hype, Toward Real Impact

By William Vides · OpenBrains.tech

Artificial intelligence is on everyone’s lips in the Latin American public sector. Every government modernization conference has at least one presentation on “AI for citizens.” But what does this really mean in contexts where databases are inconsistent, connectivity is unreliable and technical teams are small?

The problem of ignored context

Most AI implementations in the region’s public sector start with the wrong question: “What AI can we use?” The right question is: “What specific problem do we want to solve and what are our actual data?”

In our experience building violence analysis systems for UNDP Guatemala or dashboards for the Attorney General’s Office of the Dominican Republic, the biggest obstacle wasn’t the algorithm: it was the quality and consistency of source data.

Three field lessons

1️⃣

Clean before modeling70% of the work in public data projects is cleaning, normalization and validation. AI models are the remaining 30%.

2️⃣

Start with classical statisticsBefore deploying an ML model, check if a descriptive analysis or logistic regression doesn’t already solve the problem. Almost always it does.

3️⃣

The model must be explainablePublic sector decision-makers need to understand the why. A “black box” never survives political scrutiny.

AI in Latin American Governments: Beyond the Hype, Toward Real Impact

The problem of ignored context

Three field lessons

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