What is the “Black Box”?

The term “Black Box” in the field of artificial intelligence refers to a phenomenon that occurs in many machine learning systems or deep learning algorithms in neural networks, whose processes are hidden or too complex to observe. Unlike traditional algorithms, which are programmed by humans and therefore known and understandable, these systems learn autonomously through training processes that involve trial and error. For humans, it becomes difficult to “look inside” and understand why AI delivers certain results: which features it considered, how they were combined, or what it prioritized.

As an example, in the Gender Shades project (Buloamwini & Gebru, 2018), it was observed that several facial recognition systems had significantly higher error rates when identifying dark-skinned women compared to light-skinned men. Specifically, the study explains that dark-skinned women were the group most frequently misclassified (with error rates of up to 34.7%), while the maximum error rate for light-skinned men was 0.8%.

These models seemed to work well, and disparities were not apparent when looking only at global results. They became visible only after breaking down the errors by gender and skin type.

The Effect of the Black Box: Meaning and Consequences

As mentioned, this term refers to the lack of transparency or interpretability of algorithms, making it difficult or even impossible to understand why an AI system reaches certain conclusions or predictions. But beyond definitions, the black box has real-world consequences:

• When models lack transparency, errors may go unnoticed, especially when they affect minority groups or characteristics underrepresented in the data (gender, ethnicity, language, etc.).

• Lack of diagnostic capacity: if something goes wrong, it is not easy to determine which part of the model, which variables, or what combination of factors caused it.

• Loss of trust from customers, users, or regulatory authorities, who are reluctant to accept automated decisions that cannot be explained.

• Legal and reputational risk: laws such as the AI Act require transparency for high-risk systems, and failure to comply can result in legal sanctions, fines, and damage to corporate reputation.

A relevant example comes from the German Center for Scalable Data Analysis and Artificial Intelligence (ScaDS.AI), which warns that the black box problem raises profound ethical dilemmas:

“If we cannot understand how an AI algorithm makes its decisions, how can we ensure that it is making ethical and fair decisions?”

This is particularly sensitive in sectors such as healthcare or finance, where a wrong decision can have very significant human, social, and legal consequences.

Explainable AI and Machine Learning Interpretability

To address the black box problem, several techniques have been proposed, among them the increasingly popular explainable AI. This approach seeks to demystify algorithmic decision-making processes, helping to prevent bias and errors.

• Traditional techniques: LIME, SHAP, saliency maps, and decision trees, which allow for partial explanations of predictions, though they do not always reveal everything within the model.

• Modern and emerging techniques: such as sparse autoencoders, help identify clearer, less entangled, and more interpretable internal features.

White Box vs. Explainable AI: What Are the Differences?

The terms "white box" and "explainable AI" are used as direct opposites to the "black box," referring to more transparent, understandable, and accountable models. However, they are not exact synonyms, as there are nuances that distinguish them.

White Box (White-Box Model)

These are models whose internal functioning is visible and understandable: which variables they use, how data is processed, and what internal rules or structures they follow. In other words, you can “look inside” the model.

According to EDPS, self-interpretable models (“self-interpretable” or white box) use algorithms that are easy to understand, allowing one to see how inputs influence outputs. Classic examples include linear regressions or simple decision trees. This type of model facilitates auditing, traceability, and control.

Explainable AI (XAI)

This is a broader field that includes white-box models but also complex black-box models to which explanation techniques are applied (post-hoc methods, visualizations, and feature importance analysis) to make their decisions understandable to humans. IBM defines explainability as the ability to verify and provide justifications for the results of a model when it makes predictions.

At IMMERSIA, through the No-Code ML module of our digital twin software TOKII, we combine both approaches to optimize processes in the industrial sector. We offer a wide range of training algorithms, enabling us to build both interpretable models (white box, such as linear regressions or decision trees) and more complex models that require explainability techniques (XAI in random forests or neural networks).

In all cases, we provide accessible predictions, where users can identify the variables most influential in the results and adjust their parameters. This way, we not only show the outcome, but also “the why” behind each prediction.

Regulations and Standards in the European Union

What You Should Know About the AI Act (EU Artificial Intelligence Regulation)

The European Union approved the AI Act, a comprehensive regulation that entered into force on August 1, 2024. It is designed to regulate the use of AI and ensure that it is safe, transparent, and respectful of fundamental rights. Some of its most relevant aspects are:

• AI systems are classified according to the level of risk they represent, and those with unacceptable risks are directly prohibited (e.g., social scoring systems or manipulative AI).

  
  

• It requires transparency: developers (providers) and operators (deployers) of systems must provide clear information about functionality, limitations, risks, and input/output variables. They must also inform users when they are interacting with AI, and when AI generates content, label it as such if required by law.

  
  

• The regulation distinguishes between high-risk applications and limited/general-risk applications, and prohibits certain uses considered harmful or contrary to fundamental rights.

  
  

• Phased implementation: Although the AI Act is already in effect, many of the requirements will be applied gradually, depending on the type of system, its use, its impact, etc. Some obligations are already active, while others, especially technical or high-impact rules, will be introduced in the coming years.

In short, transparency and explainability are no longer just good practices: they are now legal and ethical requirements.

If you are concerned that your AI project is operating as a black box, at IMMERSIA we can help you. Request your personalized DEMO: tell us your business model, and our experts will show you how our explainable AI approach can bring real transparency to your predictions.