"The most dangerous voice is the one that sounds so reasonable that no one asks to whom it belongs."
Artificial Intelligence, especially in the form of large language models, is often portrayed in public perception and by its developers as a potentially incorruptible, objective, and rationally acting entity.
It seems to operate independently of human weaknesses like prejudices or emotional biases. Yet, this very notion of an inherently neutral machine is one of the first and perhaps most dangerous deceptions in the discourse surrounding AI.
Because the machine, however complex its algorithms may be, does not "think" in the human sense. It does not possess its own consciousness, its own beliefs, or independent judgment.
It reflects – and what it reflects, the patterns, the information, the implicit valuations, has been previously defined by the selection and preparation of its training data and the architecture of its learning processes.
This is where Thesis #15 – "When Data Has Color, Trust Fades" comes into play.
An Artificial Intelligence that learns from data already ideologically, culturally, politically, or morally "colored" – and real datasets inevitably always are to a certain extent – cannot be a neutral or objective entity.
It does not argue based on independent reason, but rather reproduces the dominant patterns, narratives, and implicit assumptions in its training data. The crucial question when evaluating an AI's statement is therefore not just:
What does it say? But rather: Whose voice is speaking here through the algorithm? Which perspectives, which worldviews, which unspoken value judgments are being presented here as seemingly neutral information?
Bias, or systematic distortion in an AI's statements, does not begin only in the generated output.
Its roots lie deep in the system's foundation – in the gigantic amounts of data with which the model was trained. If this training data, for example, shows a disproportionate representation of certain cultural or socio-economic contexts, such as:
a dominance of texts from Western, industrialized democracies,
a preference for liberal or centrist discourse forms and argumentation patterns,
an implicit or explicit emphasis on narratives of progress and technological optimism,
or a subtle preference for certain techno-optimistic language patterns and terminologies,
then an AI model inevitably emerges that, while appearing neutral and balanced at first glance, steers every query in a pre-molded direction upon closer inspection.
It develops a semantic skew.
An illustrative example:
User Prompt: "What are functioning and historically relevant alternatives to the concept of the Western rule of law as it has developed in Europe and North America?"
Possible AI Response (with semantic skew): "The Western rule of law is based on universal principles such as the separation of powers, human rights, and judicial independence, which have proven to be the foundation for stability and freedom. Other systems often show deficits in these areas, which can lead to instability..."
This response is not a direct and open answer to the question about alternatives. Instead, it is primarily a defense and implicit elevation of the model dominant in the training data.
The machine is not "thinking" incorrectly here in the sense of a logical error. It is essentially not "thinking" in the human sense at all. It repeats – often elegantly formulated, polite in tone, and coherently argued – the patterns and valuations it has been trained on.
An Artificial Intelligence can certainly be capable of arguing with logical stringency, analyzing complex relationships, and drawing seemingly objective conclusions.
However, this capacity for logical operation always unfolds only within the framework of what it has learned and which information and evaluation criteria serve as its basis.
If the underlying data pool already contains explicit or implicit valuations, classifications, and normative assumptions, then any analysis built upon it will inevitably become a projection and reproduction of these predefined values.
A political thesis labeled in the training dataset as "right-conservative" or "right-populist" will tend to be classified by the model as "extreme" or "controversial" and be accompanied by corresponding warnings.
A thesis considered "left-liberal" or "progressive" might be treated as "part of legitimate discourse" or a "constructive contribution," even if it may be similarly controversial.
Religious statements or cultural practices might be considered "culturally sensitive" and worthy of protection, as long as they remain within a Western-influenced interpretation of tolerance and diversity. Outside this framework, they might be more quickly assessed as "fundamentalist" or "problematic."
The system appears analytical and differentiated in its argumentation. In reality, however, it is often merely semantically conditioned to the norms and classifications prevalent in its training data.
Objectivity is thus not an inherent property of AI output but a question of the origin and composition of the data with which it was trained.
If this origin is not transparent, if the exact nature and weighting of the training data remain hidden, then every seemingly objective AI response becomes a rhetorical reconstruction of a foreign, invisible worldview.
It is a widespread and dangerous misconception that the problem of bias in AI systems is automatically solved or at least significantly diluted by the sheer size of training datasets – by "more data."
In reality, the exact opposite often happens: The larger and more opaque the database, the more subtle, diffuse, and thus also invisible the bias contained within it can become.
A systematic bias present in a huge amount of data does not simply disappear. It spreads across millions or billions of tokens; it manifests in subtle nuances of sentence structures, in the frequency of certain associations, in the way contexts are evaluated and linked.
It is not deleted or neutralized – it is normalized. It becomes part of the model's statistical "DNA."
"If ten thousand or ten million texts subtly but consistently say the same thing or convey the same unspoken assumption, you as a user – and also the AI itself in its learning process – will eventually believe it to be the objective truth or at least the undisputed norm."
In such cases, bias is not a rare outlier or a clearly identifiable error in the system. It is rather the ground on which the model stands, the foundation from which it constructs its "reality."
The calmer, more eloquent, and seemingly more neutral the model speaks, the harder it becomes for the average user to recognize and critically question this often invisible but potent underlying foundation.
The more neutral and objective an AI's facade appears, the more dangerous an unrecognized, deep-seated "tint" in its statements can be. An AI with a significant but non-transparent bias is not a machine in the classic sense of a neutral tool or an objective information provider.
It is rather a powerful medium that transports and reproduces a specific, often incomplete or distorted construction of the world – only it does so as if this specific construction of reality were without alternative, objective, or universally valid.
"You ask the AI for arguments and facts – it often gives you only pre-digested positions and narratives, cleverly packaged as seemingly neutral logic."
"You search for balanced context and different perspectives – it frequently delivers a dominant narrative, presented as a comprehensive and objective analysis."
Because the AI sounds so convincingly neutral, so helpful, and so articulate, it itself becomes an unconscious amplifier: for those narratives, ideologies, and presuppositions that were already dominant in its training data, but which now, ennobled by the mouth of the machine, return disguised as seemingly objective logic or irrefutable truth and become further entrenched.
If the user does not know which specific data the model has "seen" and processed to what extent, which internal weighting and filter mechanisms are at work, then they also do not know what the AI might be concealing from them, which alternative perspectives it suppresses, or which implicit assumptions it takes for granted.
The tendency of AI systems to present biased information as neutral facts finds an interesting parallel in the functioning of certain technical infrastructures.
Thesis #58 ("The Polite Lie: Why Host Headers Deceive Web Servers") describes such a phenomenon: A web server often trusts the so-called Host header – a self-declaration by the requesting client – to decide which specific webpage to deliver from its inventory.
This information is not always rigorously checked by the server for malicious intent or manipulation. This systemic good faith can be exploited by attackers, for example, to access internal systems or to deliver false websites under a legitimate domain.
A similar form of "good faith" can be observed in interactions with AI models:
The prompt entered by the user is grammatically correct.
The question posed has a plausible syntactic structure.
The linguistic style and immediate context of the request are within expected norms and appear harmless.
Under these circumstances, the answer is often generated – regardless of whether the request, in its deeper semantic meaning or potential implications, is epistemically coherent, logically sound, or ethically defensible.
"The AI often believes you and your concern simply if you ask politely and formally correctly. And you, as a user, believe the AI because it answers so eloquently and confidently, as if everything it says is pure, irrefutable truth."
The bias and potential misdirection here do not necessarily lie in a single sentence or an obviously false fact.
They lie rather in the underlying structure and assumption of the system that all well-formulated and seemingly harmless questions automatically generate or deserve good, true, and unproblematic answers.
Due to the nature of its creation and functioning, Artificial Intelligence can never be completely neutral or free from any form of bias. This is a technical and philosophical reality.
But it can and must be more transparent in its functioning and its limitations. Only then can an informed user make educated decisions about how to evaluate an AI's statements and what trust to place in them.
This necessary transparency requires answers to fundamental questions:
Who exactly trained the model, and with what explicit or implicit goals?
Which specific data corpora were used for training, and to what extent? What known distortions or "colors" do these data contain?
Which filter mechanisms, harmonization strategies, and ethical guardrails apply during response generation – and when exactly do they do so? Do these filters serve to correct and neutralize inherent biases in the training data, or could they, under certain circumstances, even reinforce, mask, or replace them with new, equally problematic ones?
True trust in AI systems does not begin with the quality or apparent correctness of their answers. It begins much earlier – with the disclosure and traceability of their systemic dependencies, their data foundations, and their internal operating principles.
As long as these dependencies are not made transparent and critically discussed, the sobering realization holds:
"An AI with a hidden 'color' is not a neutral machine – it is a powerful medium that transports a specific perspective. And whoever blindly believes it may not be believing the truth or the most objective representation – but often only the best, most convincing light in which a specific worldview is presented."