The fallacy of AI autonomy

⏱️ Reading time: ~4 minutes

There is a recurring phenomenon in the public debate about Artificial Intelligence: when a model produces an inappropriate result, the first reaction is usually to attribute autonomy to it. Phrases emerge such as: "The AI did it on its own." Or: "Who answers when it gets things wrong?"

These questions reveal a deeper problem: most people do not know how a language model actually works.

A Large Language Model (LLM) has no will, intention, consciousness, or initiative of its own. Its operation is based on a process of inference over a knowledge base built beforehand during training. In other words, the model does not create goals; it responds to the goals presented to it.

Here a fundamental distinction arises.

Inference is not learning.

Inference is the ability to use existing knowledge to produce a coherent response given a specific context. Learning, on the other hand, implies permanently modifying one's own cognitive structure based on new experiences. Most current models do not learn continuously during a conversation. They operate over a relatively stable knowledge base and use the context provided by the interlocutor to produce responses.

This difference has an important philosophical consequence.

When a user does not understand the level of inference of the model they are using, they tend to delegate responsibilities that were never the machine's. A poorly specified request, ambiguities in the objective, an absence of constraints, or a lack of validation criteria frequently produce inadequate responses. Then comes the claim that "the AI did it on its own."

But that statement is, most of the time, incorrect.

AI does not act spontaneously. It responds to the space of possibilities defined by the combination of its architecture, its knowledge base, and the context it receives.

However, the opposite extreme is also a simplification.

It is not correct to claim that every error belongs exclusively to the user. Models have limitations inherent to their architecture. They can interpret a clear instruction ambiguously, hallucinate nonexistent information, reproduce biases present in the training data, or fail at tasks they were not designed for.

This leads to a new way of understanding responsibility.

Responsibility in Artificial Intelligence systems is not binary. It is distributed.

There is the responsibility of the developer, who defines the architecture, the training mechanisms, the safeguards, and the limitations of the system.

There is the responsibility of the user, who defines the problem, establishes the scope, provides the context, and critically validates the results.

And there is the technical limitation of the model itself, which bounds how far its inference can reach.

Perhaps this is a new chapter in the philosophy of technology: abandoning the simplistic question "Who is at fault: the AI or the human?" and replacing it with another, far more precise one:

How should responsibility be distributed among those who build the system, those who use it, and the very limits of computational inference?

This shift in perspective eliminates the false idea of AI autonomy and puts the discussion back where it truly belongs: in the interaction between human intelligence, artificial intelligence, and shared responsibility.