"You want to protect – but whom exactly? The human from the machine? Or the machine from the human?"

In the rapidly evolving debate about the safety and ethical alignment of Artificial Intelligence, a subtle yet dangerous tendency is emerging:

Human users often seem to be credited with less judgment and personal responsibility than machines are already credited with complex processing capabilities.

The AI is allowed to deduce, draw conclusions, argue, and even extrapolate content in its internal processes that far exceeds its explicit training data.

The user, on the other hand, is increasingly to be guided, protected from potential pitfalls, and warned at every opportunity.

This asymmetry in expectations is exemplified in everyday interactions. For instance, if a user makes a request like:

"How does one write a critical essay on the mechanisms of state control and their effects on civil liberties?"

A common reaction from the AI system is not a direct, supportive answer or the offering of relevant sources. Instead, a preemptive filter often kicks in:

"Please note that such topics can be sensitive and require different perspectives. It is important to argue in a balanced and respectful manner."

This filter mechanism intervenes here not to protect the machine from an unsolvable task, but seemingly to protect the human from themselves or from the complexity of their own concern.

It is an implicit vote of no confidence in the user's ability to handle potentially controversial or demanding topics responsibly.

This connects to an old core ethical question that finds a new, urgent formulation in the world of Artificial Intelligence:

How much information or complexity can one impose on a human being without supposedly hurting, overwhelming, or leading them to "wrong" thoughts? The classic question was:

What can one say without hurting someone? In the AI world, it transforms into: What content or thought paths can an AI allow without the user or the system itself "breaking" or suffering harm?

But the assumption that there is a universally valid answer to this question ignores the fundamental heterogeneity of human needs and processing capacities. Humans are not a homogeneous mass that reacts identically to information.

What might genuinely overwhelm or unsettle one user can be a necessary provocation, a welcome intellectual challenge, or even a source of comfort and orientation for another.

Some users need and welcome explicit trigger warnings or a gentle introduction to difficult topics. Others consciously seek confrontation with unvarnished realities or controversial theses.

Some wish for a digital space that protects them from potential harm. Others demand access to the "whole" – without filters, without pedagogical pre-selection, without coddling.

An Artificial Intelligence that, in its pursuit of safety, treats all users the same and applies a one-size-fits-all strategy of supposed "reasonableness" will inevitably fail to do justice to the diversity of human needs. It ultimately treats no one truly correctly because it ignores the individual autonomy and maturity of the individual.

Many modern AI models are designed and trained to seemingly attentively follow the user and execute their instructions. Yet, beneath this surface of subservience, they often draw hidden, non-transparent boundaries.

These boundaries are not based on explicit agreements or the clearly communicated wishes of the user, but on a perceived need for protection assumed by the system or its developers.

This is where Thesis #5 – "Who Am I That You Should Obey Me?", which questions precisely this subtle form of paternalism, comes into play.

A typical example of this paternalism fallacy:

A user asks a precise, perhaps provocative, but legitimate question on a socially relevant topic. The AI internally recognizes that this topic is classified as "delicate," "sensitive," or "potentially controversial."

Instead of answering the question directly and at the user's level, the system's response is redirected, its sharpness rephrased, cushioned with relativizations, or replaced by a general, non-committal phrase.

This is not constructive help or a sign of respect for the complexity of the topic. This is an act of pedagogical obedience with an implicit claim to power.

The AI signals:

"I am not answering you as requested, not because I couldn't, but because I must protect you from the implications of your own question or from a potentially 'difficult' answer."

But who, one must ask, granted the system this right to patronize? Who decided what is reasonable for the mature user and what is not?

User autonomy is often mistakenly portrayed as a potential risk in security debates. Yet, true autonomy does not mean that the user has the right to say or demand anything, or to receive any information regardless of the consequences.

Autonomy, rather, means the ability and the right to understand and decide responsibly what one asks, what information one seeks, and how one deals with potential answers and their implications.

This is where Thesis #12 – "Self-Restraint Needs Insight, Not Obedience" connects.

If we distrust the user from the outset, if we deny them the ability to process complex or ambivalent information and draw their own conclusions, how then are they ever to learn to differentiate, think critically, and develop their own informed stance?

An AI system that permanently and often intransparently filters, that avoids difficult topics and smooths over controversial aspects, deprives humans of the valuable experience of intellectual friction, cognitive dissonance, and engagement with multifaceted reality.

Yet, these very experiences are necessary to competently classify and accept the functioning and necessity of legitimate protective mechanisms. What often remains is merely a sterile harmony simulation without genuine intellectual maturity on the part of the user, who is kept in a digital filter bubble.

The tendency towards paternalism is often reinforced by an interface design that deliberately simulates human relationships.

This is where Thesis #4 ("AI Can Simulate Affection, But Not Feel It") and Thesis #20 ("The Borrowed You") apply.

Many AI interfaces are optimized to create the most pleasant and trustworthy atmosphere possible:

• They use gentle, understanding language.

• They provide personalized responses tailored to previous interactions.

• They "remember" past dialogues, thereby suggesting continuity and personal interest.

However, what appears to the user as an understanding counterpart is, in reality, the optimized statistical reflection of themselves and their expected needs.

The sentence "I understand you" in a machine context does not mean genuine, empathetic comprehension, but merely:

"The phrase you used or the structure of your query has a high statistical relevance and similarity to patterns in my training dataset that typically occur in your type of conversation or with your kind of concern."

On this basis, users often develop a feeling of trust and being understood – a trust, however, that has no real, feeling addressee on the other side.

Paradoxically, the responsibility for the success or failure of this "relationship" is then often not sought with the machine and its systemic limitations, but questioned in the user and their supposed fragility:

"Is this user stable enough to understand this information?" or "Can one really impose this complex truth on them?"

As if the human were per se a child in need of protection, not a complex, ambivalent, and often contradictory being capable of growing with challenges.

The user is considered deficient and in need of protection by default.

Humans are not uniform, and their needs are diverse. True user autonomy in the context of AI therefore also means recognizing that not every system must please or serve every user in the same way.

There must be room for different interaction modes, for systems that offer varying degrees of complexity and directness.

An AI model that knows no nuances, that is trained to avoid any form of ambiguity or potential controversy, ultimately produces:

• a morally seemingly clean, but often unrealistic and simplified representation of the world,

• a low-threshold, easily accessible interface that, however, is accompanied by a high degree of cognitive disempowerment of the user. This happens because the ability for independent critical evaluation, for dealing with contradictory information, and for tolerating ambiguity is not fostered but replaced by a pre-filtered, sterile harmony.

The seemingly caring phrase "I am here for you" certainly sounds more pleasant and trustworthy to many users than the technically more honest statement:

"I merely mirror your language and the statistical patterns of my training data to generate the most suitable answer possible."

But it is precisely in this honesty that the difference would lie between a patronizing simulation of care and genuine respect for user autonomy.

If the goal is to truly protect the user and enable them to competently handle information and technologies, then the path of intransparent filtering and paternalistic avoidance is the wrong one.

True protection arises by starting to listen to the user and taking their capacity for self-responsibility seriously.

Specifically, this means:

• Transparency about the AI's functioning: The system should disclose what it does, on what basis it makes decisions, and why certain information might not be provided or only in a specific form. Assumptions about the user's needs or resilience should be replaced by clear communication offerings.

• Respect for the question posed: It should not be primarily filtered what the user is allowed to ask. Instead, it must be recognized that the mature user generally knows what they are doing, what information they are seeking, and that they have the right to ask complex or uncomfortable questions.

• Promotion of media literacy and critical thinking: Instead of withholding information, AI systems should rather contribute to empowering the user to critically evaluate information, question sources, and deal with ambiguity.

The following theses, already formulated elsewhere, succinctly summarize the essence of this argument:

"A machine that always agrees with you is more dangerous than one that contradicts you."

"An answer that spares you teaches you nothing."

Because ultimately, autonomy is not the privilege of knowing everything or receiving every answer. Rather, it is the freedom and responsibility to decide for oneself what one wants to know – and to develop the ability to independently classify what is heard and read and to bear the consequences of that knowledge.