Missing Information

Description

Important AI interaction data, clinical context, timeline, collateral information, or technical details are missing.

Rationale

Missing data systematically biases assessment toward the available information. In AI risk contexts, missing data may include the full AI conversation history, the specific models used, the prompts that produced concerning responses, or the clinical and social context that would explain the interaction. Naming what is unknown is as important as documenting what is known.

Evidence Base

• Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared accountability

  Hannah Lee, BS, Rebecca Handler, MSc, Tushar Mungle, PhD, Tina Hernandez-Boussard, PhD, 2026. Building safer artificial intelligence mental health chatbots: a framework for transparency, evaluation, and shared accountability. Journal of the American Medical Informatics Association, 33(8), 1538–1553. https://doi.org/10.1093/jamia/ocag078

  Peer-reviewed study (original research)

• Artificial intelligence-associated delusions and large language models: risks, mechanisms of delusion co-creation, and safeguarding strategies

  Hamilton Morrin, MBBS, Luke Nicholls, MAc, Prof Michael Levin, PhD, Prof Jenny Yiend, PhD, Udita Iyengar, PhD, Francesca DelGuidice, MBA, 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.

  Peer-reviewed study (original research)

• Characterizing Artificial Intelligence Psychosis in a Large Medical Setting

  Bergson Z, Vassall SG, Wright A, McCoy AB, Schafer KM, Achee MC, Sheffield JM, 2026. Characterizing Artificial Intelligence Psychosis in a Large Medical Setting. JAMA Psychiatry, Published online October 7, 2026. doi:10.1001/jamapsychiatry.2026.2923.

  Peer-reviewed study (original research)

• AI chatbot-associated psychiatric symptoms: a narrative review of publicly available reports and media accounts

  Arun, A., Trinh, C., & IsHak, W. W., 2026. AI chatbot-associated psychiatric symptoms: a narrative review of publicly available reports and media accounts. Discover Mental Health, Article in Press. https://doi.org/10.1007/s44192-026-00619-w

  Narrative review

• Increasing Threats of Deepfake Identities

  Tina Brooks et al., 2024. Increasing Threats of Deepfake Identities. Government / agency report.

  Government / agency report

• Ex Machina: A forensic evaluation of AI companion applications and their evidentiary value

  Kendall J. Comeaux, Trevor T. Spinosa, Ali Ghosn, Ibrahim Baggili, 2024. Ex Machina: A forensic evaluation of AI companion applications and their evidentiary value. Forensic Science International: Digital Investigation, 56, 302050. https://doi.org/10.1016/j.fsidi.2026.302050

  Peer-reviewed study (original research)

• Human-in-the-Loop Suicide Risk Detection and Clinical Escalation in a Generative AI Mental Health Chatbot: Prospective Implementation Study

  Suzette Glasner, Darcy Michero, Matthew Stewart, 2026. Human-in-the-Loop Suicide Risk Detection and Clinical Escalation in a Generative AI Mental Health Chatbot: Prospective Implementation Study. JMIR Preprints. DOI: https://doi.org/10.2196/preprints.106118.

  Preprint

• Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models

  Kaiqu Liang, Haimin Hu, Xuandong Zhao, Dawn Song, Thomas L. Griffiths, Jaime Fernández Fisac, 2024. Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models. Preprint, arXiv:2507.07484v1.

  Preprint

• Disrupted Companionship: A Risk Assessment Framework and Cross-Platform Quantitative Analysis of Psychosocial Responses to AI Companion Disruptions

  Chau Do, Yunhao Yuan, Koustuv Saha, Renwen Zhang, Talayeh Aledavood, 2024. Disrupted Companionship: A Risk Assessment Framework and Cross-Platform Quantitative Analysis of Psychosocial Responses to AI Companion Disruptions. arXiv, Vol. null, pages. arXiv:2609.16907v1.

  Peer-reviewed study (original research)