Privacy and Data Sensitivity

Description

Material includes sensitive personal, clinical, educational, employment, or legal information.

Rationale

AI interaction records may contain highly sensitive personal, clinical, or legal information about both the subject and third parties. The ethical obligations around handling this material — including disclosure limits, storage, and use in proceedings — must be explicitly considered and documented as part of any assessment that relies on AI content.

Evidence Base

• A scoping review on the mental health harms of LLM-based chatbots

  Diel A, Torous J, Cuijpers P, Kleesiek J, Nensa F, Weber N, Faust F, Lalgi TJ, Mellis FS, Teufel M, Bäuerle A, 2024. A scoping review on the mental health harms of LLM-based chatbots. npj Digital Medicine, 9(644). https://doi.org/10.1038/s41746-026-03054-x

  Peer-reviewed study (original research)

• The role of artificial intelligence in radicalisation, recruitment and terrorist propaganda: deconstructing violent extremism and reimagining counterterrorism in contemporary digital ecosystems

  N A Nasir Ahmad Ganaie, 2024. The role of artificial intelligence in radicalisation, recruitment and terrorist propaganda: deconstructing violent extremism and reimagining counterterrorism in contemporary digital ecosystems. Frontiers in Political Science, 7(1), pages. https://doi.org/10.3389/fpos.2025.1718396

  Peer-reviewed study (original research)

• 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)

• Increasing Threats of Deepfake Identities

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

  Government / agency report

• 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)

• 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)

• 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

• 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)