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Introduction

Artificial intelligence has been advancing rapidly, revolutionizing sectors such as healthcare, business, and technology. However, as these systems become more sophisticated, a growing concern arises: the lack of transparency in their functioning. The so-called “black box problem” refers to the difficulty of understanding how neural networks make decisions, even when we provide inputs and analyze outputs. This issue presents not only technical challenges but also ethical and regulatory ones. In this article, I explore how neural networks process information, why humans cannot fully understand their operations, and what current efforts are being made to make AI more explainable.

The “Black Box” Problem

Artificial neural networks were inspired by the functioning of biological neurons, yet how they arrive at certain decisions remains largely a mystery to humans. This phenomenon is known as the “black box problem”—we know the inputs and outputs of the model, but what exactly happens inside to make these decisions is not fully understood.

How Do Neural Networks Process Information?

• Layers and Weights: Neural networks have multiple layers of artificial neurons. Each connection between neurons has a weight that adjusts the influence of an input on the output.
• Activation and Weight Adjustment: During training, the network adjusts these weights millions or billions of times to minimize errors in its predictions.
• Non-Linear Process: The problem is that internal calculations do not follow simple human logic—they involve thousands (or millions) of non-linear mathematical operations that combine information in ways we cannot intuitively track.

Why Don’t Humans Understand?

• Dimensions and Complexity: Modern neural networks can have billions of parameters (such as GPT-4, Llama, Gemini, and the currently acclaimed DeepSeek, among others). No human can mentally visualize what happens in this multidimensional mathematical space.
• AI’s Self-Learning Ability: When we train a network, it independently learns patterns that even we do not know. For example, AI can find hidden statistical relationships in data without us knowing how it did so.
• Lack of Transparency: Unlike traditional code that follows step-by-step instructions, AI adjusts its internal parameters in ways we cannot directly interpret.

Real Examples of AI Being a Black Box

• AlphaGo: The system that defeated the world champion of Go made moves that even the best human players could not understand. It discovered new strategies that no one had thought of before.
• Medicine and Diagnosis: Medical AIs can detect diseases by analyzing images, but doctors do not know exactly what the AI is observing to make its decisions.
• Language Models (LLMs): Models like GPT-4 and others mentioned earlier can generate highly coherent responses, but even the engineers who created them do not know exactly how they associate words and concepts.

The Future: Trying to Explain AI

There is a major movement called “Explainable AI” (XAI) that seeks to make models more comprehensible. Some approaches include:

• Heat maps to understand which parts of an image influence a decision.
• Interpretable neural networks, which limit complexity to facilitate explanations.
• Techniques like SHAP and LIME, which analyze which variables were most important for each prediction.

Neural networks are already going beyond our understanding in many aspects. We create the basic rules, but the way AI discovers patterns and makes predictions may be something that even humans have never imagined. This raises fascinating questions about artificial creativity, transparency, and even the limits of human knowledge.

The AI Paradox

It seems paradoxical when we say that humans do not understand how AI, their own creation, works. But it makes sense when we analyze how artificial intelligence learns and evolves. This phenomenon is not exclusive to AI—it also happens in other areas of science and technology, where we create systems that surpass our ability to fully comprehend them.

AI Learns Differently from Humans

• Humans learn through experience and intuition, while AI learns through statistics and optimization.
• AI does not think like a human but rather adjusts internal parameters based on mathematical probabilities.

Example: An AI trained to recognize cats may learn patterns that humans would never notice. It might be observing tiny details in the texture of the fur, whereas a human sees only “pointy ears and big eyes.”

Other “Black Boxes” Exist

This phenomenon is not exclusive to AI! We coexist with other situations that the human brain still does not fully explain. Examples:

• The human brain: We know it is composed of neurons, but we do not completely understand how consciousness emerges.
• Evolution of Life: DNA stores genetic information, but we still do not understand all the interactions that regulate living organisms.
• Earth’s Climate: Despite having advanced meteorological models, there are still limitations in predicting the exact weather.

AI Still Lacks Consciousness—It’s Just Extremely Complex

Many people feel uneasy thinking that we have created something we do not fully understand. But the truth is that AI does not have its own consciousness (at least not yet)—it simply operates at a level of complexity that surpasses our intuition.

The future of AI is not just about creating more powerful models but also making them more explainable! The fields of Explainable AI (XAI) and algorithmic transparency are focused on helping us better understand how AI makes its decisions.

In Summary:

• Yes, we created AI, but it learns in ways that even its creators cannot predict.
• This is not unprecedented in science, as we also do not fully understand the brain, DNA, or even certain areas of physics.
• The challenge now is to make AI more transparent and reliable so that we can harness its power without losing control.

Conclusion

Artificial intelligence is changing the world, but its opaque nature raises crucial questions about reliability, ethics, and transparency. While these models bring undeniable benefits, our inability to fully understand their decisions can lead to unexpected risks. The field of Explainable AI (XAI) emerges as a promising solution, aiming to make models more transparent and auditable. The great challenge now is not just to improve AI but to ensure that we can use it safely and responsibly. As we move forward, the central question remains: will we ever be able to fully comprehend how these powerful tools work? The future of AI and humanity depends on the answers we find to this question.