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D12 — Data Reliability, Ethics, and Assessment Limits

D12-E03

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

  • 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)

  • Increasing Threats of Deepfake Identities

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

    Government / agency report

  • 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

  • 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)
This platform is NOT a scoring engine, diagnostic tool, or risk prediction system. It is a research and evidence-mapping tool to support the transparent development of an SPJ framework.