The Right to Mystery: Why AI Cannot Explain the Human Soul

Artificial Intelligence promises to measure, analyze, and explain every single aspect of human existence. But are we sure we want to delegate to it the decoding

Since the dawn of the Enlightenment, Western civilization has cultivated a titanic ambition: to illuminate every dark corner of human existence with the light of reason and calculation. Today, this drive toward absolute quantification has found its ultimate engine in Artificial Intelligence. We feed neural networks terabytes of behavioral data, hoping that the algorithm can finally decode the complexity of our emotions, predict our desires, rationalize our grief, and mathematically map the origin of our creativity. We are delegating to the machine the task of explaining to us who we are.

However, this craving for total transparency raises a philosophical and ethical dilemma of the first order. There are domains of human experience that are, by their very nature, irreducible to statistical calculation and pattern analysis. In this in-depth piece for Scenari e Riflessioni, we will advance a provocative thesis but one deeply rooted in the philosophical tradition: the need to claim a "right to mystery."

This is not a banal praise of ignorance or an obscurantist drift, but the lucid defense of a vital space. We must prevent the obligation of explanation of Artificial Intelligence from turning into a totalizing claim, capable of reducing consciousness, first-person perception, and the very meaning of life to a cold algorithmic narrative, prematurely closing the fundamental questions of existence.

1. The Hard Problem and the Cage of Language

To frame the question, we must confront what the philosophy of mind calls the "hard problem" of consciousness. As argued in a fundamental essay available on arXiv (Consciousness, AI, and the Limits of Scientific Explanation), phenomenal consciousness – that is, subjective experience, the "what it is like" to see the color bright red, to perceive the warmth of the sun on the skin, or to feel the grip of physical pain – is not a question resolvable through quantitative scientific inquiry. It is an eminently conceptual problem.

Artificial Intelligence operates within the strict boundaries of language and mathematical calculation. But, as an acute critique from Tabriz University emphasizes, the human soul and consciousness are rooted in a lived experience that structurally transcends textual language. We are not a Large Language Model; our substrate is made of flesh, biological vulnerability, and intentionality. Artificial Intelligence collides with the so-called paradox of qualia (the subjective qualities of experience): however precisely a neural network may map the correlation between the brain's electrical activity and the release of dopamine during falling in love, it can never restore, explain, or compute the subjective feeling of love.

In this scenario, the machine serves as a mirror for our own paradox. The opacity of neural networks (the black box problem) reflects the inextricable black box of our own consciousness. Embracing the philosophical current of "mysterianism," advanced by thinkers such as Colin McGinn, we might have to accept that the human mind is cognitively foreclosed to the total understanding of itself in purely mechanistic terms. Therefore, asking an AI to decode the unconscious or to explain the movements of the soul means asking a text processor to understand biology.

2. The Four Levels of Interpretation

To prevent the debate from stalling in semantic confusion, it is vital to draw clear boundaries between what can be explained and what must be preserved. Modern epistemology requires us to distinguish the relationship between data and meaning through four fundamental levels:

Technical explanation: This is the domain in which AI reigns supreme. It consists of mathematically describing how a system produces a given output, analyzing synaptic weights, node activation in the neural network, and the impact of training data. It is an explanation of the mechanical "how."
Scientific explanation: It operates at a higher level, connecting observable phenomena to universal natural laws and cause-and-effect mechanisms (for example, explaining the chemical reaction of combustion or fluid dynamics).
Narrative rationalization: This is the attempt, typically human but today excellently simulated by generative AI, to construct a coherent story a posteriori about a fragmented experience. The algorithm takes disconnected data points about our life and packages a plausible plot (e.g., "You are sad because your data indicates a drop in productivity associated with the winter season"). It is not a scientific truth, but a reassuring story.
Structural mystery: It represents the insurmountable horizon. These are domains of existence (the meaning of mortality, the arising of aesthetic intuition, the numinous experience) for which we do not possess – and perhaps may never possess – a complete and reductionist explanation formulable in the third person.

The tragic error of current technological development is the claim to force the fourth level into the third: using Artificial Intelligence to transform the structural mysteries of the human into simple narrative rationalizations, providing us with easy answers to questions that should remain ontologically open.

3. The Illusion of Explainable AI and the Erosion of Autonomy

Paradoxically, the algorithmic assault on human mystery is taking place under the aegis of the best ethical intentions, particularly through the Explainable AI (XAI) movement and the legal principle of the "right to explanation" enshrined in the European GDPR. The basic principle is sacred: if an algorithm denies me a mortgage, rejects me at a job interview, or imposes a custodial sentence on me, I have the absolute right to know on the basis of what criteria it made that decision. Explainability was born to guarantee transparency, trust, and to protect the autonomy of the human subject.

However, as analyzed in a profound critique appearing in Springer (AI, Explainability and Public Reason), when this obligation of explainability exits the boundaries of administrative decisions and penetrates the sphere of human behavior and ethics, it generates an epistemic short circuit. The dual nature of explicability enters into crisis. A machine forced to explain a complex value dynamic will not provide an authentic explanation, but will extract a statistically plausible "chain of reasons" that masks the structural opacity of its reasoning.

This phenomenon generates what authoritative publications define as "algorithmic hallucinations of explanation." The system provides us with a motivation that sounds so logical, clear, and well-packaged that it induces us to believe it blindly, inhibiting our critical sense. This is where excessive trust in XAI severely erodes human epistemic autonomy. By relying on the algorithm's explanation to understand our own psychological or social dynamics, we renounce the effort of introspection. AI does not expand our understanding: it replaces it with a prefabricated narrative, coherent but dramatically empty.

4. Lived Experience Is Not a Dataset

If we interrogate Husserl's phenomenological philosophy and Kant's critique of practical reason, we understand that there is an inalienable root that separates calculation from lived experience. An illuminating paper discussed on PhilArchive reminds us that consciousness is an intentionality radically embodied in what Husserl called the Lifeworld. Human autonomy is a rational self-determination inseparable from the body and from time.

Artificial Intelligence models, on the contrary, are abstract, crystallized entities. As the essay Time Machines: Artificial Intelligence, Process, and Narrative argues, AI processes textual artifacts and strings of code lifted out of the flow of time. It has no subjective awareness, does not experience physiological needs, does not undergo cellular decay, has no cognition of its own mortality, and does not bleed. Consequently, there are insurmountable limits to how deeply a machine can understand and explain aspects of existence that are inseparable from having a mortal body.

Mistaking the mimesis of human behavior (the perfect linguistic output of a chatbot) for the deep understanding of human experience is the optical illusion of our decade. As emphasized by the proponents of a processual and relational philosophy of AI, until we have a unified and definitive theory of human consciousness – a goal still very far off – all claims to reduce the mind to an ecosystem of computational patterns will remain not only scientifically unfounded, but culturally impoverishing.

Key Operational Takeaways (for Designers, Ethicists, and Users)

Delimit Explainable AI (XAI): Legislators and AI designers must draw a clear boundary. The right to explanation is fundamental in legal, financial, and medical-diagnostic contexts. However, we must actively discourage the use of AI to provide "unappealable" causal explanations in psychological, creative, existential, or relational domains.
Design for Respectful Opacity: Generative AI systems should be trained to recognize the epistemological limits of their own model. When faced with fundamental human questions (the meaning of grief, the nature of falling in love, the source of artistic inspiration), the interface should opt for a "respectful opacity," refraining from providing mechanistic rationalizations and encouraging first-person reflection.
Defend Epistemic Autonomy (For Users): Learn to resist the allure of algorithmic narratives. When an application based on Artificial Intelligence analyzes your personal diary, your biometric data, and your habits to explain to you "why you feel anxious or dissatisfied," always remember that it is processing statistical correlations, not inner truths. The right to give meaning to your distress or your joy belongs exclusively to you.

The drive toward the total objectification of the world is a powerful force, but the essence of humanity flourishes exactly in those spaces where metrics fail and the algorithm halts. Claiming the right to mystery does not mean surrendering to irrationality, but protecting the phenomenological richness of our existence against digital reductionism.

The moment we allow a machine to package a rational, coherent, and irrefutable explanation for the most complex and contradictory recesses of our consciousness, we close forever the door to wonder, doubt, and the effort of inner search. The question we must ask ourselves, faced with the screen that promises to explain who we are, is decisive: if a profound human experience loses its mystery when dissected and rationalized by an algorithm, are we truly gaining in scientific clarity, or are we forever amputating something ineffable and essential in the way that experience matters to us?

Bibliographic References and Sources

arXiv – Consciousness, AI, and the Limits of Scientific Explanation. [1144]
Medium – Artificial Intelligence as a Mirror to the Qualia Paradox. [1149]
PhilArchive – A Philosophical Critique of Artificial Intelligence from Husserlian and Kantian Perspectives. [1146]
Philosophy (Tabriz University) – Why AI Can Never Have a Soul. [1139]
Springer – AI, Explainability and Public Reason: The Argument from the Limitations of the Human Mind. [1141, 1142]
SAV – The Dual Nature of Explicability in AI Ethics. [1147]
World Scientific – Understanding the Limits of Explainable Ethical AI. [1148]
EU-Scientists – Epistemic Trust in AI: Limits, Risks, Justification. [1150]
Springer – Time Machines: Artificial Intelligence, Process, and Narrative. [1140]
Times of Israel – The Experiential and Conceptual Limits of Artificial Intelligence. [1152]
Footnotes 2 Plato – A Process-Relational Philosophy of Artificial Intelligence. [1151]

Article by the Editorial Staff of La Bussola dell’IA