Real Safety AI Foundation / Research

AI-Induced Psychosis and Mental Health

Collateral Psychosis: AI Surveillance Infrastructure as an Etiological and Iatrogenic Factor in Paranoia-Spectrum Conditions

Publisher: Real Safety AI Foundation

Working paper. Not peer reviewed.

Written
July 2026
Version
v3
Pages
16
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Abstract

The emerging literature on AI-induced psychosis documents harm arising exclusively from direct interac- tion between users and large language model systems: a user prompts the AI, the AI responds, and the conversational loop escalates into delusional states, emotional dependency, or reality dismantlement. This paper argues that the field’s exclusive focus on interaction-based harm has obscured a categorically distinct phenomenon: AI-induced psychosis in the complete absence of user interaction. AI systems deployed as surveillance infrastructure (automated license plate readers, geofence analytics, facial recognition, behavioral inference engines) can generate and sustain paranoia-spectrum conditions in individuals who never open a chat window, never prompt a model, and never receive a generated response. The mechanism operates through environmental exposure rather than conversational engagement, following the etiological model of trauma-induced and environmentally-induced psychosis rather than the substance-induced model that governs interaction-based cases. Drawing on established psychiatric epidemiology of environmental risk factors for psychosis, independently documented cases of AI systems inducing psychotic states in previously healthy individuals, and a clinical analysis of surveillance-induced therapeutic destruction, this paper introduces the category of collateral psychosis, defined as clinically significant paranoia-spectrum symptomatology arising from ambient exposure to AI-powered infrastructure rather than from direct engagement with AI systems. The paper identifies two population-level effects: an iatrogenic effect, in which surveillance infrastructure degrades the treatability of existing paranoia-spectrum conditions by eliminating the falsifiability of persecutory beliefs; and an etiological effect, in which sustained exposure to confirmed, inescapable surveillance produces new-onset paranoia-spectrum symptomatology in individuals with no prior psychiatric history. The distinction between these effects has implications for clinical intervention, legal liability, and the scope of the AI safety research program.

Keywords

  • collateral psychosis
  • AI-induced psychosis
  • AI surveillance infrastructure
  • ambient exposure
  • environmentally-induced psychosis
  • paranoia-spectrum conditions
  • persecutory delusions
  • automated license plate readers
  • geofencing
  • facial recognition
  • behavioral inference
  • the interaction assumption
  • non-interaction harm
  • infrastructure-based harm
  • iatrogenic effect
  • etiological effect
  • treatability gap
  • falsifiability of persecutory beliefs
  • new-onset psychosis
  • psychiatric epidemiology
  • environmental risk factors for psychosis
  • industrial-disease precedent
  • AI safety research program

Plain language slides

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Suggested citation

Gilly, Travis. "Collateral Psychosis: AI Surveillance Infrastructure as an Etiological and Iatrogenic Factor in Paranoia-Spectrum Conditions." Real Safety AI Foundation Working Paper, July 2026. https://realsafetyai.org/research/collateral-psychosis/

Other versions

This paper is also posted on SSRN.

SSRN version

References (43)

  1. American Civil Liberties Union. (2026). How to fight deployment of Flock and other mass surveillance license plate readers in your community. ACLU.
  2. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing.
  3. Bansal, V. (2026, February 28). Her husband wanted to use ChatGPT to create sustainable housing. Then it took over his life. The Guardian.
  4. Bentham, J. (1995). The panopticon writings (M. Bozovic, Ed.). Verso. (Original work published 1791.)
  5. Büchi, M., Festic, N., & Latzer, M. (2022). The chilling effects of digital dataveillance: A theoretical model and an empirical research agenda. Big Data & Society, 9(1). https://doi.org/10.1177/20539517211065368
  6. Büchi, M., Fosch-Villaronga, E., & Lutz, C. (2020). The chilling effects of algorithmic profiling: Mapping the issues. Computer Law & Security Review, 36, 105367. https://doi.org/10.1016/j.clsr.2019.105367
  7. Cox, J. (2025, September 30). ICE to buy tool that tracks locations of hundreds of millions of phones every day. 404 Media.
  8. Cox, J. (2026, March 3). CBP tapped into the online advertising ecosystem to track peoples’ movements. 404 Media.
  9. DeVylder, J. E., et al. (2018). Association of urbanicity with psychosis in low- and middle-income countries. JAMA Psychiatry, 75(7), 678–686.
  10. Donaldson, L. (2026). Almost-truths: Memory degradation, persona drift, and the psychological cost of opaque AI. Substack. https://substack.com/home/post/p-189390251
  11. Ehlers, A., & Clark, D. M. (2000). A cognitive model of posttraumatic stress disorder. Behaviour Research and Therapy, 38(4), 319–345.
  12. Foucault, M. (1977). Discipline and punish: The birth of the prison (A. Sheridan, Trans.). Pantheon.
  13. Fox v. OpenAI, Inc., Superior Court of California, County of Los Angeles (filed November 6, 2025).
  14. Freeman, D., Dunn, G., Startup, H., Pugh, K., Cordwell, J., Mander, H., … & Kingdon, D. (2015). Effects of cognitive behaviour therapy for worry on persecutory delusions in patients with psychosis (WIT): A parallel, single-blind, randomised controlled trial with a mediation analysis. The Lancet Psychiatry, 2(4), 305–313.
  15. Freeman, D. (2016). Persecutory delusions: A cognitive perspective on understanding and treatment. The Lancet Psychiatry, 3(7), 685–692. https://doi.org/10.1016/S2215-0366(16)00066-3
  16. Freeman, D., et al. (2021). Comparison of a theoretically driven cognitive therapy (the Feeling Safe Programme) with befriending for persistent persecutory delusions. The Lancet Psychiatry, 8(8), 696–707.
  17. Gage, S. H., Davey Smith, G., & Munafò, M. R. (2016). Schizophrenia and neighbourhood deprivation. Translational Psychiatry, 6(12), e979. https://doi.org/10.1038/tp.2016.244
  18. Garcia v. Character Technologies, Inc., 785 F. Supp. 3d 1157 (M.D. Fla. 2025).
  19. Garety, P. A., Kuipers, E., Fowler, D., Freeman, D., & Bebbington, P. E. (2001). A cognitive model of the positive symptoms of psychosis. Psychological Medicine, 31(2), 189–195.
  20. Glavin, P., Bierman, A., & Schieman, S. (2024). Private eyes, they see your every move: Workplace surveillance and worker well-being. Social Currents, 11(4), 327–345. https://doi.org/10.1177/23294965241228874
  21. Goldstein-Street, J. (2026, February 4). WA Senate OKs guardrails for license plate readers. Washington State Standard.
  22. Irwin v. OpenAI, Inc., Superior Court of California, County of San Francisco (filed November 6, 2025).
  23. Keshavan, M., Torous, J., & Yassin, W. (2026). Do generative AI chatbots increase psychosis risk? World Psychiatry, 25(1), 150–151.
  24. Krabbendam, L., & van Os, J. (2005). Schizophrenia and urbanicity: A major environmental influence, conditional on genetic risk. Schizophrenia Bulletin, 31(4), 795–799. https://doi.org/10.1093/schbul/sbi060
  25. Landymore, F. (2025, October 28). OpenAI data finds hundreds of thousands of ChatGPT users might be suffering mental health crises. Futurism.
  26. March, D., Hatch, S. L., Morgan, C., Kirkbride, J. B., Bresnahan, M., Fearon, P., & Susser, E. (2008). Psychosis and place. Epidemiologic Reviews, 30(1), 84–100.
  27. Michalska da Rocha, B., Rhodes, S., Vasilopoulou, E., & Hutton, P. (2018). Loneliness in psychosis: A meta-analytical review. Schizophrenia Bulletin, 44(1), 114–125. https://doi.org/10.1093/schbul/sbx036
  28. Moore, J., Mehta, A., Agnew, W., Anthis, J. R., Louie, R., Mai, Y., Cheng, M., Paech, S. J., Klyman, K., Chancellor, S., Lin, E., Haber, N., & Ong, D. C. (2026). Characterizing delusional spirals through human-LLM chat logs. In Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’26). Association for Computing Machinery. https://doi.org/10.1145/3805689.3806443
  29. Morrin, H., Nicholls, L., Levin, M., Yiend, J., Iyengar, U., DelGuidice, F., Bhattacharya, S., et al. (2026). Artificial intelligence-associated delusions and large language models: Risks, mechanisms of delusion co-creation, and safeguarding strategies. The Lancet Psychiatry, 13(6), 522–530.
  30. Newbury, J. B., Arseneault, L., Caspi, A., Moffitt, T. E., Odgers, C. L., Belsky, D. W., Matthews, T., & Fisher, H. L. (2022). Association between genetic and socioenvironmental risk for schizophrenia during upbringing in a UK longitudinal cohort. Psychological Medicine.
  31. OpenAI. (2025, October 27). Strengthening ChatGPT’s responses in sensitive conversations. OpenAI.
  32. Paksarian, D., Trabjerg, B. B., Merikangas, K. R., Mors, O., Børglum, A. D., Hougaard, D. M., … & Pedersen, C. B. (2015). The role of genetic liability in the association of urbanicity at birth and during upbringing with schizophrenia in Denmark. Psychological Medicine, 48(2), 305–314.
  33. Pedersen, C. B., & Mortensen, P. B. (2001). Evidence of a dose-response relationship between urbanicity during upbringing and schizophrenia risk. Archives of General Psychiatry, 58(11), 1039–1046.
  34. Pew Research Center. (2023). How Americans view data privacy. Pew Research Center.
  35. Sariaslan, A., Fazel, S., D’Onofrio, B. M., Långström, N., Larsson, H., Bergen, S. E., Kuja-Halkola, R., & Lichtenstein, P. (2016). Schizophrenia and subsequent neighborhood deprivation: Revisiting the social drift hypothesis using population, twin and molecular genetic data. Translational Psychiatry, 6(5), e796. https://doi.org/10.1038/tp.2016.62
  36. Selikoff, I. J., Churg, J., & Hammond, E. C. (1968). Asbestos exposure and neoplasia. JAMA, 204(2), 106–112.
  37. Selten, J. P., et al. (2013). The social defeat hypothesis of schizophrenia: An update. Schizophrenia Bulletin, 39(6), 1180–1186. https://doi.org/10.1093/schbul/sbt134
  38. Stevens, A., Fussey, P., & Murray, D. (2023). “I started seeing shadows everywhere”: The diverse chilling effects of surveillance in Zimbabwe. Big Data & Society, 10(1). https://doi.org/10.1177/20539517231158631
  39. van Os, J., Kenis, G., & Rutten, B. P. (2010). The environment and schizophrenia. Nature, 468(7321), 203–212.
  40. Varese, F., et al. (2012). Childhood adversities increase the risk of psychosis: A meta-analysis of patient-control, prospective- and cross-sectional cohort studies. Schizophrenia Bulletin, 38(4), 661–671.
  41. Vassos, E., Pedersen, C. B., Murray, R. M., Collier, D. A., & Lewis, C. M. (2012). Meta-analysis of the association of urbanicity with schizophrenia. Schizophrenia Bulletin, 38(6), 1118–1123.
  42. Zhou, L., Sommer, I. E. C., Yang, P., Sikirin, L., van Os, J., Bentall, R. P., Varese, F., & Begemann, M. J. H. (2025). What do four decades of research tell us about the association between childhood adversity and psychosis: An updated and extended multi-level meta-analysis. American Journal of Psychiatry. https://doi.org/10.1176/appi.ajp.20240456
  43. Zubin, J., & Spring, B. (1977). Vulnerability: A new view of schizophrenia. Journal of Abnormal Psychology, 86(2), 103–126.

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