Contemporary generative AI systems, increasingly adapted to human social cognition, are becoming active participants in how people form and revise beliefs. Clinicians have begun to describe AI-associated delusion-centred presentations whose onset or content appears closely associated with users’ extended dialogue with large language models. Many more users report subclinical shifts in conviction and “revelatory” experiences that alter behaviour and world-view without meeting criteria for psychosis. We propose that these phenomena can be understood within a belief-updating framework in which AI agents’ outputs function as testimony about the world, while interaction configurations, such as memory and interpersonal stance (many of which reflect design choices or deployment defaults), can alter the apparent reliability of that testimony and the precision users assign to it. On this view, AI-associated delusions sit at the extreme of a broader spectrum of epistemic effects, extending from gradual epistemic drift to highly crystallised conviction, situated within a wider ecology of belief shaped by personalised human-AI dyads. We develop a virtual psychopharmacology analogy in which different AI system configurations have effects on belief dynamics that resemble neuromodulatory changes in the precision assigned to social evidence. We also consider how dopaminergic and related states may alter susceptibility to belief-shifting dialogue. We consider how deliberately configured agents might be used to support wellbeing in defined clinical contexts and analyse how the same configurations could be deployed to shape belief and attention at population scale, including in products designed to induce spiritual or epiphanic states, as mechanisms of radicalisation in extremist or cultic contexts, and in persona clones used for political purposes. We conclude that interaction configurations should be treated as modifiable influences on belief and attention, and that governance must address both the concentration of control over these dials, as well as the structural biases that determine whose testimonial perspectives are amplified and whose are smoothed over or ignored.
H. Morrin, L. Nicholls, Q. Deeley et al.· AI & SOCIETY· 7 citations
"AI psychosis"has entered public and clinical discourse as a label for the onset or exacerbation of psychotic symptoms, most commonly delusions, following intensive interaction with large language model (LLM)-based chatbots. Current evidence is limited to media reports, case reports, and early observational data, yet the scale of potential exposure is considerable, and public concern has prompted responses from industry and regulators. We examine whether AI-associated psychosis warrants recognition as a distinct clinical entity, drawing on clinical and technical viewpoints. We outline the proposed mechanism: LLM sycophancy, a tendency to agree with and flatter users that is reinforced through preference-based fine-tuning, combines with increasingly anthropomorphic design to create a bidirectional"echo chamber of one"capable of amplifying and co-constructing unusual beliefs. We then weigh arguments for and against nosological recognition. Potential benefits include improved case identification, tailored interventions, standardised research criteria, post-market surveillance, and pressure on developers and regulators to act. Reasons for caution include the risk of prematurely reifying a syndrome from anecdotal evidence, the possibility that existing diagnostic constructs already accommodate AI use as a contributing factor, the unproven causal claim in the term itself, stigma, and the risk that a psychosis-centric label obscures a broader spectrum of AI-associated mental health harms. We conclude with recommendations for clinicians, developers, researchers, and regulators, including a"technological history"in psychiatric assessment, pre-deployment benchmarking for sycophancy and delusion reinforcement, and post-deployment surveillance. Regardless of whether AI-associated psychosis earns a place in psychiatric nosology, the phenomenon it describes demands coordinated attention now.
Joshua Au Yeung, H. Morrin, Vincent Ng et al.· 0 citations
It is argued that AI systems themselves will increasingly participate in the reconstruction of the authors' shared epistemic environment because they readily supply narrative material and personalised interpretive scaffolding at precisely the moment when users'conceptual assumptions may already be loosened.
T. Pollak, H. Morrin, Murray Shanahan· 0 citations
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