Explainable AI (XAI) is the set of techniques that allow you to understand why an artificial intelligence model made a decision. Instead of accepting an answer as a "black box", XAI shows which variables weighed and how the result was reached. With the European AI Act already in force and a growing demand for transparency, more and more LATAM companies in sectors like fintech, healthcare, and HR are requesting it as a requirement before putting a model into production.

For years, the priority was for the models to be accurate. Today that's no longer enough: if a system rejects a loan, prioritizes a patient, or discards a résumé, someone has to be able to explain the why. That requirement stopped being an academic luxury to become a business condition, especially for Latin American companies that operate with clients or partners in Europe.

What explainable AI (XAI) is

Explainable AI (XAI) is the branch of artificial intelligence focused on making a model's decisions understandable to people. It's not about simplifying the model, but about adding a layer of interpretation that translates its logic into something auditable.

The key distinction is between models that are interpretable by design (like a linear regression or a decision tree, where the logic is transparent) and opaque models (like deep neural networks), which are very accurate but don't show their reasoning. XAI is born to open up that second category without sacrificing precision.

How it works: SHAP and LIME explained simply

The two most used techniques today seek to answer the same question —"what drove this decision?"— from different angles.

• SHAP (SHapley Additive exPlanations): distributes the "credit" of a prediction among all the variables, using an idea taken from game theory. It tells you, for example, that in a loan rejection the payment history added so much and the job tenure subtracted so much. It's precise and consistent, but more costly to calculate.

• LIME (Local Interpretable Model-agnostic Explanations): builds a simple model "around" a specific case to approximate why the big model decided that. It's fast and works for any model, although its explanation is local and approximate.

In practice, many teams combine both: SHAP for formal audits and regulatory reports, LIME for quick case-by-case explanations.

Why LATAM companies are starting to require it

The regulatory trigger is the European Union's AI Act, which classifies AI systems by level of risk and requires documenting and explaining the "high-risk" ones (credit, employment, healthcare, among others). Any Latin American company that sells software, processes data, or has clients in Europe is indirectly affected.

Added to this is market pressure: according to IBM, transparency and trust became central criteria for adopting AI in corporate environments. Three sectors lead the demand in the region:

Transparency also connects with an increasingly sensitive topic: the responsible handling of information. If you're interested in that angle, check out our guide on data privacy when using AI at work, because explainability and privacy are two sides of the same problem of trust.

How to start applying XAI in a real project

You don't need to redo the whole stack. A realistic path is:

• Document what data enters the model and with what objective.

• Add SHAP or LIME on the model you already have, without replacing it.

• Define an "explanation threshold": which decisions require justification and for whom (client, auditor, regulator).

• Validate with a human the high-impact cases before automating them.

Recommended Coderhouse courses

To understand the fundamentals and then take them to production, it's a good idea to combine training of different levels:

• Introduction to Artificial Intelligence Course: ideal if you come from business or a non-technical role and want to understand how models "think" before talking about explainability.

• AI Engineering Course: for those who already program and want to build and audit models with transparency criteria.

• AI Products Course: designed for product profiles who must decide which AI decisions to expose and how to communicate them to users and regulators.

If your company is starting to look at these topics, training the team now is the cheapest way to avoid a regulatory problem later.

Frequently asked questions

Does explainable AI make the model less accurate?

Not necessarily. Techniques like SHAP and LIME are applied on the existing model to interpret it, without modifying its predictive capacity. Explainability is an additional layer, not a replacement for the model.

What's the difference between SHAP and LIME?

SHAP distributes the weight of each variable in a prediction in a mathematically consistent way and is ideal for audits; LIME generates fast local explanations for specific cases. Many teams use both depending on the context.

Is my company in LATAM bound by the European AI Act?

If you offer AI products or services to users in the European Union, or process data of people in that territory, the AI Act may affect you indirectly. It's a good idea to review the case with specific legal advice.

Do I need to be a programmer to understand explainable AI?

To understand the concepts and decide when to require explainability, no. To implement SHAP or LIME it's a good idea to have technical foundations. An introductory AI course is enough to participate in the conversation with judgment.