What is opacity or the black box in AI and why does it matter?

The so-called "black box" of artificial intelligence has become one of the most controversial topics whenever we talk about algorithms that make decisions for us. We trust systems that recommend medication, grant loans, or filter resumes… but often we have no idea why they arrive at those decisions , not even when they directly affect our rights.

This lack of transparency is not just a technical problem: it has ethical, legal, social, and business implications . That's why there's so much talk about algorithmic opacity, explainability (XAI), and new regulations like the European AI Act, which aim to bring order to this area. Let's examine, calmly but in detail, what opacity or the "black box" in AI really is , why it arises, what risks it entails, and how efforts are underway to open this box without losing the advantages of the technology.

What do “black box” and opacity mean in AI?

In the context of artificial intelligence, a "black box" is a system whose internal processes cannot be clearly understood . We know what data goes in and what the output is, but the intermediate "path" is incomprehensible or inaccessible to humans, and even to many developers.

This phenomenon is primarily associated with complex machine learning models, such as deep neural networks , which work with thousands or millions of parameters distributed across numerous layers. Unlike a classic algorithm based on transparent rules, here the model learns from experience, adjusting internal weights in such a way that no one can manually track which exact combination of neurons led to a specific response.

Opacity can arise in two different but complementary ways: on the one hand, because the company decides not to show the code or the details of the model (to protect its intellectual property or for pure commercial strategy); on the other hand, because the mathematical and statistical complexity itself makes an intuitive human interpretation practically impossible , even if the code is open source.

In this second case, we often talk about “ organic black boxes ”: not even the system's creators can accurately describe what internal patterns the AI ​​has learned or how it combines them in each decision. With deep learning models, this is the norm, not the exception.

When dealing with these systems, we can only clearly observe the so-called visible layers: the input layer and the output layer . We see the data that is entered (images, text, numerical variables) and the predictions or classifications that are produced (approved/denied, diagnosis, recommendation, etc.). But what happens in the many hidden layers in between remains, to a large extent, beyond our understanding.

How black box models work: neural networks and deep learning

To understand the source of this opacity, it's helpful to review, even if only in broad strokes, how deep learning models are structured . Instead of a single, simple formula, these systems are made up of neural networks with many layers (sometimes hundreds) and a large number of neurons in each layer.

Each neuron is essentially a small block of code that receives input, applies a mathematical transformation, and generates an output . Learning involves adjusting, through millions of examples, the weights and thresholds of all these neurons so that the system minimizes prediction errors. The problem is that, once trained, the result is a gigantic network of parameters that doesn't correspond to clear and distinct human concepts.

These types of networks can ingest large volumes of raw data (images, audio, free text, sensor data) and detect patterns of enormous complexity: nonlinear relationships, combinations of very subtle features, correlations that defy our intuition. Thanks to this, they are capable of translating languages, generating images, writing coherent texts, or analyzing X-rays with precision comparable to that of specialists.

But this power comes at a price: the internal representations they create (for example, the famous vector embeddings ) are high-dimensional numerical structures that don't align directly with simple human categories . We can intuit that certain vectors group similar meanings or that certain neurons respond to specific patterns, but the complete map is practically unmanageable.

Even when the model is open source and we can see every line of code, that doesn't mean we can explain every prediction in detail . We can follow how data flows between layers and what operations are applied, but it's not feasible to rationalize why a specific combination of millions of parameters results in "approved" for one person and "denied" for another.

In summary, the black box is not only due to corporate secrecy ; it is also a consequence of having opted for extremely complex architectures that optimize accuracy but sacrifice interpretability.

Opacity, bias, and discrimination: when the black box causes harm

The lack of transparency is not just a theoretical problem. Algorithmic opacity can lead to unfair, discriminatory, or outright wrong decisions , without any clear way to detect the problem or correct it in time.

A frequently cited example is the Gender Shades project by Joy Buolamwini and Timnit Gebru, which analyzed various commercial facial recognition systems. The study showed that error rates were significantly higher when identifying dark-skinned women than when identifying light-skinned men: in some cases, over 34% error compared to less than 1% for the better-treated group.

Looking at the overall results, these systems seemed to work well. But when the errors were broken down by gender and skin tone, very worrying inequalities came to light. That is precisely one of the pitfalls of the black box: serious flaws can be hidden in the average and go unnoticed if no one scrutinizes the results closely.

This type of bias is usually unintentional. AI learns from the data we give it, and if that data reflects historical inequalities or underrepresents certain groups, the model reproduces and amplifies those injustices without anyone having explicitly "ordered" it to do so . And because it is opaque, detecting which variables or combinations are generating the discrimination becomes a very complex task.

Opacity also makes it difficult to identify systematic errors or vulnerabilities . If we don't know how the model is "reasoning," it's harder to predict what kinds of inputs might cause it to "hallucinate" (generate false but convincing responses) or fall into adversarial traps designed to manipulate it.

All of this has a clear consequence: it erodes the trust of users, customers, and authorities . If someone suffers a negative decision based on AI and no one can clearly explain what factors were taken into account, it's normal for doubts to arise about the fairness and legitimacy of the system.

Ethical, legal and liability impact

From a legal perspective, the black box creates a serious problem: it complicates proving the causal link between the AI ​​system and the harm suffered . To establish civil liability, a combination of harm, culpable or defective conduct, and a causal relationship is usually required. When the decision relies on an opaque model, this third element becomes unstable.

In the analog world, discussing a dismissal, a loan denial, or an access filter involved reviewing documents, criteria, witnesses, and explicit motivations . With AI models, layers of inference, difficult to reconstruct, intervene between the input data and the final decision. These layers are often managed by a chain of actors (model provider, integrator, user company, third parties contributing data), which dilutes who controls what.

Furthermore, there is a clear incentive to keep the information secret: the operator can use trade secrets or technical complexity as a shield to avoid providing relevant information in litigation. If the victim cannot access records, technical documentation, or decision traces, proving that the harm is due to the AI ​​system becomes virtually impossible.

The European legislator's response has been decisive: if full explicability is not possible, the entire burden of proof cannot be placed on the weaker party . Thus, we see new regulations requiring the maintenance of logs, documentation of how the system works, the permitting of audits, and, procedurally, opening the door to the presentation of evidence and presumptions in favor of the injured party when the operator fails to cooperate.

Let's consider, for example, a company that uses AI tools in human resources to screen resumes, score performance, or recommend promotions . Formally, the final decision rests with a person, but in practice, it relies heavily on reports generated by AI. If an employee is rejected or dismissed and is not given access to what data was used, its weight, what patterns were detected, or what technical documentation supports the system, the "black box" not only makes the decision but also prevents them from effectively challenging it.

Explainable AI and interpretability: trying to open the box

To mitigate these problems, the field of explainable AI, or XAI (Explainable Artificial Intelligence) , has emerged . The goal is not so much to "translate" line by line what the algorithm does, but to provide useful, understandable, and actionable explanations as to why the model has made a particular decision.

There are two main approaches. On the one hand, there are intrinsically interpretable or white-box models : simple algorithms such as linear regressions, shallow decision trees, or logical rules, in which it is clear which variables are included, which rules are applied, and how the result is reached. These types of models facilitate auditing and traceability, although they sometimes sacrifice some precision.

On the other hand, we have complex models (black boxes) to which explanatory techniques are applied . This is where tools such as LIME, SHAP, salience maps, or Grad-CAM come into play, allowing us to estimate which features have had the greatest impact on a specific prediction, or to visualize which areas of an image have been crucial for a diagnosis.

For example, in medical settings, SHAP-like techniques have been used to analyze diagnostic imaging models and discover that, in some cases, the system was paying too much attention to markings or annotations on the X-ray instead of relevant clinical patterns. Detecting these deviations allows for model correction and risk reduction.

Furthermore, explainability has a key human dimension: an explanation is of little use if the recipient doesn't understand it . A doctor doesn't have the same needs as a data engineer, a judge doesn't have the same needs as a patient or a bank customer. That's why we work in a multidisciplinary way, combining technology with cognitive psychology and interface design to adapt the explanation to the profile of the recipient.

Black box vs white box vs explainable AI: how do they differ?

The terms "white box," "black box," and "explainable AI" are often used interchangeably, but they are not exactly the same . Clarifying these terms is important because this confusion leads to significant misunderstandings.

A white-box model is one whose internal workings are transparent and understandable . It's clear which variables are involved, how they combine, what rules apply, and how the input is transformed into the output. Typical examples include well-specified linear regressions and simple decision trees . These models are self-interpreting: their structure itself provides the explanation.

A black box model , on the other hand, is one whose internal logic cannot be easily followed. This would include deep neural networks, highly complex random forests, XGBoost-type boosting , and, in general, any system with multiple layers of parameters that are difficult to translate into clear human rules.

Explainable AI (XAI) is a broader umbrella term that includes both white-box models and techniques applied to black boxes to generate post-hoc explanations . A highly complex model can be considered "explainable" if it is accompanied by tools that allow, for example, breaking down the importance of variables, visualizing salience, or generating contrastive examples ("if your salary had been X and your seniority Y, the result would have changed").

In practice, many organizations are combining both approaches: they use simple models when transparency outweighs accuracy (highly regulated cases) and they resort to more powerful models accompanied by XAI when they need to maximize predictive capacity, but without completely abandoning interpretation.

European regulation: AI Act, GDPR and product liability

The European Union has decided to address algorithmic opacity from several angles. On the one hand, the General Data Protection Regulation (GDPR) already imposes certain obligations when automated decisions are made based on personal data, requiring that “meaningful” information about the logic used be provided in a way that is understandable to the data subject.

In addition, there is the AI ​​Act, or European Artificial Intelligence Regulation , in force since August 2024, which establishes a specific framework for the development and deployment of AI systems in the EU. The regulation classifies systems by risk levels , directly prohibiting those of “unacceptable risk” (such as social scoring in the style of mass social credit or certain extreme behavioral manipulation techniques).

High-risk systems (e.g., some uses in health, finance, human resources, education, or security forces) are subject to strict obligations: they must have detailed technical documentation, automated records (logging) that allow traceability, clear and understandable information for users , and effective human supervision mechanisms.

Furthermore, the AI ​​Act imposes transparency obligations in scenarios such as the use of chatbots or content generators: users must be notified when they are interacting with AI and, in certain cases, automatically generated content must be labeled. Many of these obligations will be phased in over the coming years, starting with the most impactful cases.

In parallel, the new Directive (EU) 2024/2853 on liability for defective products updates the civil liability framework to adapt it to an environment where products can also be software and where faults can arise from digital functions. The Directive expressly recognizes the technical and scientific complexity of AI systems and empowers judges to require the presentation of relevant evidence, including digital evidence, in an accessible and understandable manner.

If an operator fails to cooperate or breaches safety obligations, presumptions of negligence and causation may come into play . In other words, if the injured party provides reasonable evidence and the defendant does not provide the records or documentation requested by the court, the law compensates for the imbalance of evidence by tipping the scales in favor of the victim.

This entire regulatory package sends a clear message: anyone introducing algorithmic complexity into the market must assume the responsibility of making it auditable . The black box ceases to be a defensive advantage and becomes a compliance and reputational risk.

Transparency, open models and pending challenges

One way to reduce opacity is to embrace open-source models and comprehensive documentation practices . Open systems allow researchers, regulators, and the technical community to examine the code, replicate experiments, and detect potential biases or vulnerabilities.

However, even with open source, we still face the fundamental problem: the interpretability of internal parameters and representations . Transparency of access does not automatically imply transparency of understanding. This is why there is so much emphasis on combining openness with XAI techniques and clear governance and auditing processes.

Authorities and experts emphasize the importance of promoting a culture of transparency and accountability : maintaining detailed records of training and use, documenting model changes, defining human supervision protocols, and designing interfaces that explain the system's capabilities, limitations, and risks to the user.

Researchers are also working on new interpretability techniques , such as sparse autoencoders and other methods that aim to extract cleaner and more readable latent factors from highly complex models. The idea is to gradually move towards a kind of "glass box," where internal complexity remains, but with more robust layers of explanation.

However, experts acknowledge that we will not achieve complete transparency in all models . The real challenge is to balance accuracy, efficiency, and explainability, focusing particularly on making systems that make decisions with a high impact on fundamental rights more understandable.

Ultimately, working with AI today requires accepting that the relationship must be collaborative and not blind : machines contribute computing power and pattern detection, but humans must continue to set ethical criteria, validate critical results, and demand reasonable explanations when something doesn't fit.

In this context, the so-called “opacity” or black box effect of AI is not just a technical problem but a central point of friction between innovation, regulation, and social trust . As European legislation, AI techniques, and good governance practices advance, the black box ceases to be an insurmountable mystery and begins to be seen more as a system that, although complex, can and should be sufficiently illuminated so that citizens, businesses, and courts can trust its decisions.