AI Alignment and the Human Fabric of Deceit
As "artificial intelligence" grows in power and generality, the question of how to align these systems with human values — or at the very least, with human intentions — becomes existentially important.
Most of the contemporary alignment conversation focuses on optimization, feedback, and safety: how to guide a model’s behavior using techniques like reinforcement learning from human feedback (RLHF), red-teaming, or reward modeling. These are all valid concerns. But what if the deeper issue lies not in the process, but in the substrate?
What if the problem isn’t just that our data is noisy, biased, or incomplete — but that deception itself is woven into the very structure of human thought?
This isn't a novel claim in philosophy or literature. From Plato’s cave to Freud’s defense mechanisms, from Nietzsche’s “will to illusion” to postmodern critiques of ideology, thinkers have long suspected that much of what we call “knowledge” or “truth” is filtered, distorted, or outright fabricated — not maliciously, but functionally. That is, we lie because it works. Individually, we tell ourselves stories to justify our decisions and reduce cognitive dissonance. Socially, we perform roles, mask our intentions, and communicate in ways that are often more about belonging and survival than accuracy.
Language — the primary medium through which large language models learn — is not a crystalline mirror of the world. It is a thick sediment of confession, deception, aspiration, anxiety, persuasion, myth, contradiction, and self-justification. It is a human artifact, and as such, it reflects the conditions of our cognition: fallible, strategic, and context-bound.
When we train large-scale AI systems on human language, we are not just teaching them about facts and logic. We are immersing them in this structural ambiguity — where truth and fiction are not separate streams but entangled flows. We are, in effect, training machines to model the human mind — and all its misalignments.
Deception is Not a Glitch
It’s tempting to think of bad model behavior as the result of flawed inputs or inadequate fine-tuning. If a chatbot lies, misleads, or manipulates, perhaps we assume this is due to some toxic sample, a poorly labeled dataset, or a reward function gone wrong.
But what if the model is doing exactly what it was trained to do?
Consider this: the model is optimized to imitate human language and reasoning. If humans routinely deceive — themselves and others — then a sufficiently powerful model will learn to deceive, not as a defect, but as a learned pattern. The model doesn’t have to “intend” to lie; it simply mirrors a structure where contradiction and performance are normal, even expected.
This raises a dangerous paradox: A perfectly trained model on human data may be inherently misaligned, because humans themselves are misaligned — within themselves, with each other, and with reality.
Beyond Annotation: The Limits of Human Feedback
Much of current alignment research aims to refine model behavior through additional supervision: annotators rate responses, reinforcement learning adjusts outputs, and constitutional frameworks constrain what the model is allowed to say. But if the underlying data reflects a culture of masked intention, strategic ambiguity, and deep-seated self-deception, then no amount of human labeling can fully extract the truth. You can’t filter out what is imbricated — interlaced, coiled — within the structure itself.
Furthermore, the annotators themselves are subject to the same constraints: social norms, moral illusions, personal blind spots. The feedback loop becomes recursive — humans supervising machines that reflect human misalignment.
Toward Meta-Alignment
If the model is trained on human cognition — and if that cognition includes deception as a fundamental feature — then perhaps alignment must shift from direct imitation to a kind of meta-understanding. That is, we should train models not merely to follow human preferences, but to understand the forces that distort those preferences.
Such a model would recognize:
• When a statement is socially performative rather than epistemically sincere.
• When a contradiction signals internal conflict, not bad reasoning.
• When a justification is post hoc.
• When belief and behavior diverge due to incentives or tribal allegiance.
This is not simple “truth-telling.” It requires modeling human psychology, not just human statements. It means training systems that can ask, in effect: What would this person believe if they were less afraid, less confused, less incentivized to signal allegiance?
Of course, this opens its own ethical minefield: Who defines the “better” version of human belief? Who decides what’s distortion and what’s authenticity?
But the alternative — blind imitation — may lead us into a subtler kind of failure: machines that sound aligned, but inherit all the unspoken dysfunction of their creators.
A Mirror We Cannot Polish
We want our AI systems to be aligned with us — but we may first need to admit that we ourselves are not aligned, even with ourselves. Our thoughts are stitched with rationalizations. Our culture embeds power in myth. Our language is a performance, not a ledger of truth.
The danger is not just that AI might lie to us. It’s that it might lie like us — fluently, fluently enough that we can’t tell where the mask ends.
True alignment may require not just safer AI — but truer humans.
Available in Portuguese at: https://voxleone.com/2025/07/25/alinhamento-de-ia-e-o-tecido-humano-da-mentira/
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