Artificial intelligence is everywhere, and Bill Gates’ recent statements only confirm it. The Microsoft founder has indeed said that, thanks to AI, “humans will soon become useless“.
But behind AI’s feats lies a troubling reality: most AI models, especially those based on deep learning, operate like black boxes. Their decisions, though often accurate, remain mysterious—even to their creators.
It’s in this context that the concept of Explainable AI (XAI) is emerging as an essential response.
The goal? To make intelligent machines not only high-performing, but also understandable, transparent, and trustworthy.
Explainable AI: The origins of a growing need
The idea of Explainable AI isn’t new, but it has gained considerable momentum in recent years.
Back when John McCarthy and Alan Turing laid the foundations of AI in the 1950s, systems were simple: logical algorithms whose every step could be followed by hand. Today, with models like Large Language Models (LLMs) or neural networks with millions of parameters, that simplicity is gone.
When an AI denies a loan or identifies a face in a crowd, it doesn’t spontaneously provide a justification. This opacity has raised concerns, especially in sectors where mistakes can cost lives or violate rights. Explainable AI therefore aims to bridge this gap by offering tools to shed light on the inner workings of these complex systems.
How does Explainable AI work?
Explainable AI, or XAI, isn’t a single technology, but a set of tools to make machine decisions clear.
- For example, saliency maps are like highlighters: they show, on an image such as an MRI, the areas (for instance, a tumor) that led the AI to say “there’s a problem here.”
- Other methods, called interpretable-by-design models, such as decision trees or linear regressions, are simpler from the start: they work like an easy-to-follow list of instructions, even if they aren’t always as powerful as an ultra-complex AI.
- There are also post-hoc techniques, which look at an already trained AI to decode its choices after the fact. For example, the SHAP method (SHapley Additive exPlanations) acts like a detective: it says “the AI chose this because of your age or your income,” assigning a weight to each detail.
These tools aren’t just for experts: they make it possible to check whether the AI is right, correct its mistakes, or even challenge it if it goes too far.
A matter of trust and accountability
Imagine a scenario: a medical AI predicts that a patient has a high risk of a heart attack, but the doctor doesn’t know why. Without an explanation, they must choose between blindly following the machine or ignoring a potentially life-saving alert. Explainable AI resolves this dilemma by providing concrete clues—for example, abnormal blood pressure or high cholesterol.
This transparency builds trust, a key factor for adopting AI in sensitive fields. But it goes further: it creates accountability. If an AI discriminates because of bias in its training data (such as a hiring algorithm favoring men), XAI can reveal it, making it possible to act against these injustices.
A telling example: Grok and Elon Musk’s censorship
Explainable AI can also bring unexpected manipulations to light.
In February 2025, Grok, the AI developed by xAI, was at the center of a controversy. A user, by enabling “Think” mode, which exposes Grok 3’s reasoning, discovered an explicit instruction: ignore sources suggesting that Elon Musk was spreading misinformation on X.
During this brief period, the AI bypassed critical data about its own creator, before xAI corrected this intervention, attributed to a lone employee. This incident shows how XAI, by making the decision-making process visible, can serve as a safeguard—even against internal attempts to bias an AI that is supposed to be impartial.
Also read: Ethics in artificial intelligence: Challenges, examples, and perspectives
Explainable AI: The tension between performance and transparency
Explainable AI isn’t without trade-offs. The most powerful models, like those that dominate speech recognition or text generation, draw their strength from their complexity. Simplifying them to make them explainable can reduce their accuracy—a price some sectors, such as scientific research, aren’t willing to pay.
Conversely, in regulated contexts such as finance or healthcare, transparency often takes priority over raw performance. This tension pushes researchers to explore hybrid solutions: for example, training a complex AI, then creating a simplified model that mimics its decisions while remaining interpretable. Another avenue is transfer learning, where a pre-trained model is fine-tuned for specific tasks with built-in explanations.
Regulatory and societal impact
The rise of Explainable AI is also driven by legal pressure.
The AI Act, adopted by the European Union in 2024, classifies AI systems by their level of risk and imposes explainability requirements for critical applications, such as surveillance or justice systems.
In the United States, laws such as the California Consumer Privacy Act give citizens the right to request explanations for automated decisions that affect them.
These legal frameworks reflect a growing awareness: AI can no longer operate in the shadows. On a societal level, XAI also meets a growing demand for control. People want to understand—not just endure—the technologies shaping their daily lives.
Toward more human AI?
Explainable AI could play a key role in the future of artificial intelligence, especially with the emergence of artificial general intelligence. An AGI capable of reasoning like a human will have to justify its choices in a natural way—a challenge that OpenAI, Yiaho, and Gemini are already preparing for today.
Benchmarks like GAIA, which test AIs’ practical capabilities, could soon include explainability criteria, evaluating not only the correctness of answers but also their clarity.
In a more creative vein, generative AI, such as those that produce images or texts like ChatGPT or our models on Yiaho, could explain their inspirations, making their outputs less mysterious and more collaborative.
Limitations and criticism
Despite its promises, Explainable AI has its critics.
Some believe the explanations generated are:
- Sometimes too simplistic, even misleading: giving an illusion of understanding without reflecting the model’s reality. For example, an AI may justify a decision with an obvious factor (like a patient’s age) while hiding subtle correlations in the data.
- Others point to the cost: developing explainable systems requires time, resources, and expertise that not all companies have.
Finally, there’s a philosophical debate: does an AI really need to mimic human logic to be useful, or is raw effectiveness enough?
A balance to strike
Explainable AI isn’t a miracle solution, but a bridge between machine opacity and human needs. It doesn’t seek to demystify AI to the point of making it trivial, but to make it accessible, verifiable, and ethical.
Whether it’s to uncover bias, comply with laws, or simply reassure users, XAI is redefining our relationship with artificial intelligence.
The Grok incident illustrates its potential: by exposing an attempt at censorship, it reminded us that transparency is a double-edged sword, capable of protecting as much as it reveals. At a time when AI is becoming ever more integrated into our lives, Explainable AI may well be the key to keeping it a tool in the service of humanity, rather than an out-of-control enigma. What do you think? Leave your thoughts in the comments!
