AI as mirror, not mind: Jungian lens reveals the algorithmic unconscious

A team of Vietnamese philosophers and social scientists argues that the strangest thing about artificial intelligence may not be the technology itself, but what we unconsciously see in it. In an open forum published in AI & Society, Duc-Hung Nguyen, Manh-Tung Ho, Thi-Quynh Pham, Dang-Toan Ngo and Hong-Kong T. Nguyen deploy the analytical psychology of Carl Gustav Jung to interrogate what they call the “algorithmic unconscious” — the latent, hidden dynamics inside AI systems that shape their outputs in ways neither engineers nor users fully comprehend. Their provocative conclusion: AI should be understood not as a psychological subject with an inner life, but as a mirror reflecting both individual and collective human psychic dynamics back at us, often in distorted and dangerous forms.

The concept of the algorithmic unconscious builds on earlier work by philosopher Luigi Possati, who argued in 2020 that psychoanalysis offers tools for understanding AI that pure computer science cannot supply. Just as Freud and Jung claimed that human behavior is driven by forces beneath conscious awareness, the authors contend that machine learning systems harbor latent structures — biases embedded in training data, opaque feature interactions in deep networks, emergent behaviors that surprise even their creators — that operate below the level of deliberate design. The black box problem, long treated as a technical challenge of interpretability, is reframed here as something closer to an analytic problem: the hidden contents of these systems act on human minds whether or not anyone understands them, much as repressed material acts on a patient.

The Jungian framework supplies the vocabulary. Jung, the Swiss psychiatrist who broke with Freud and developed the theory of the collective unconscious, proposed that human psyches share inherited archetypal patterns — primordial images such as the Hero, the Wise Old Man, the Trickster and the Shadow — that surface in dreams, myths and cultural products. The authors focus on two archetypes in particular. The Trickster, a boundary-crossing figure of chaos, deception and unexpected creativity, maps onto the way generative AI systems confound expectations, produce hallucinations with total confidence, and destabilize established categories of authorship, truth and expertise. The Shadow, the repressed and disowned aspects of the personality, maps onto the way AI systems absorb and amplify the darkest contents of human culture: the prejudices, cruelty and distortions present in the data on which they are trained.

This Shadow reading gives technical teeth to a well-documented empirical problem. Research on algorithmic bias has shown that machine learning models reproduce discrimination across domains, from translation tools that encode gender stereotypes to applications that perpetuate unfair outcomes against animals and marginalized groups. Filter bubbles and recommendation engines curate reality in ways that polarize publics, as studies of social media manipulation during events such as Brexit have demonstrated. The authors’ point is that these are not mere engineering defects to be patched. In Jungian terms, the Shadow is precisely the material a person refuses to acknowledge; a society that trains its machines on its own unexamined output and then treats the resulting systems as neutral oracles is engaging in a collective act of disavowal, outsourcing its self-knowledge to entities that return its darkness in amplified form.

Equally central to the analysis is the phenomenon of projection. In Jungian practice, projection occurs when a person unconsciously attributes their own expectations, fears or desires to another person or object, failing to recognize them as their own. The authors argue that human-AI relationships are saturated with projection. Users who feel that a chatbot “understands” them, that a virtual assistant has betrayed them, or that an algorithm is wise or malicious are, on this account, encountering their own psychic contents reflected back. Prior work cited in the paper includes research on perceived betrayal by virtual assistants affecting consumer behavior, studies of belief projection in explainable AI, and psychological analyses of films such as Ex Machina that dramatize the human tendency to project otherness and soul onto machines. The magic mirror metaphor, developed by scholars of depth psychology and AI, captures the structure: the machine reflects, and we mistake the reflection for an other.

From projection flows one of the paper’s most striking concepts: pseudo-individuation. In Jungian theory, individuation is the lifelong psychological process by which a person integrates conscious and unconscious contents to become a distinct, self-aware whole. The authors observe that AI systems create the impression of being subjects with their own personalities — they converse, remember, express preferences and seem to develop — while lacking self-consciousness and reflexive capacity. They appear to individuate without actually doing so. This pseudo-individuation is not harmless. It encourages humans to treat AI as a genuine other, a companion or authority, and to surrender forms of judgment and reflection that should remain their own. The authors connect this to concerns about digital dementia, the erosion of traditional values, and the documented psychological toll of social media use, particularly among adolescents, whose developing psyches are especially vulnerable to symbolic confusion.

To ground the theory, the authors apply it to a case study of Italian Brainrot, an explicitly AI-mediated internet phenomenon in which absurdist, AI-generated characters and imagery spread virally among young users. Related scholarship on “brain rot” as a genre of participation among teenagers suggests that such content is not merely noise; it functions as a shared symbolic field in which young people play with meaning, identity and absurdity. Through the Jungian lens, the authors read Italian Brainrot as an intensified expression of the algorithmic unconscious: archetypal imagery — trickster figures, grotesque hybrids, chaotic narrative fragments — generated and circulated by machines, absorbed by a generation, and reflecting collective psychic tensions that no single actor designed or intended. The phenomenon illustrates how AI-mediated culture can become a stage on which collective unconscious material is performed at unprecedented speed and scale.

The ethical payoff of the analysis is a shift in where we look for risk. Standard AI ethics focuses on the properties of systems: fairness metrics, transparency requirements, robustness against adversarial attack. The authors do not dismiss these, but they insist that the deepest risks arise in the relational space between humans and machines — in projection, in pseudo-individuation, in the seduction of treating mirrors as minds. A user who projects wisdom onto a language model may defer to its errors; a society that projects menace onto AI may misdirect its fears; a generation raised on AI-generated archetypal content may develop symbolic lives structured by commercial optimization systems rather than by human culture. Understanding the algorithmic unconscious, the authors argue, enables diagnostic insight and heightened sensitivity to these ethical risks, offering developmental self-reflection for human societies rather than merely technological optimization.

The paper is careful about what it does not claim. It does not assert that AI systems have an unconscious in any literal psychological sense, nor does it attribute agency or interiority to machines. The algorithmic unconscious is a heuristic, a way of naming the latent dynamics of AI systems and the unconscious dynamics they activate in humans. Its value is practical: it gives policymakers, designers and users a richer language for phenomena — hallucination, parasocial attachment to chatbots, collective panic and euphoria about AI — that resist purely technical description. As AI’s capabilities appear to surprise humanity on what the authors describe as a weekly basis, they contend that a timely and accurate understanding of its nature has become an urgent requirement for individuals and nations alike.

The deeper message is a reversal of the usual framing of the AI debate. The question that dominates public discourse — will machines become conscious? — may be less urgent than its mirror image: what will humans do with a technology that reflects their own unconscious so faithfully and so powerfully? By reconceptualizing AI as a mirror of individual and collective psychic dynamics, the authors suggest that the path to safe and humane AI runs not only through better alignment algorithms but through a kind of collective self-examination. The machines are, in this view, showing us ourselves. Whether we can bear to look — and integrate what we see rather than projecting it outward — may determine whether the algorithmic unconscious remains a diagnostic tool or becomes a source of escalating collective pathology.

Subject of Research: A Jungian psychoanalytic analysis of the algorithmic unconscious, archetypal projection and ethical risks in human-AI relations

Article Title: Jungian analysis of the algorithmic unconscious: From archetypal theory and projection to ethical risks in human-AI relations

Article References: Jungian analysis of the algorithmic unconscious: From archetypal theory and projection to ethical risks in human-AI relations. (n.d.). https://doi.org/10.1007/s00146-026-03311-3

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03311-3

Keywords: artificial intelligence, Jungian psychology, algorithmic unconscious, archetypes, Shadow, Trickster, projection, pseudo-individuation, AI ethics, human-AI relationship, Italian Brainrot, collective unconscious