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

• 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