MIT AI Risk Repository · Risk Sub-Category · 65.01.01
Lack of training data transparency
Category: Training Data Risks (Transparency)
Description
"Without accurate documentation on how a model's data was collected, curated, and used to train a model, it might be harder to satisfactorily explain the behavior of the model with respect to the data."
From AI Risk Atlas (IBM2025), as extracted by the MIT AI Risk Repository (CC BY 4.0).
Classification
- Subdomain
- 6.5 Governance failure
- Causal entity
- Human
- Intent
- Unintentional
- Timing
- Pre-deployment
Subdomain definition: Inadequate regulatory frameworks and oversight mechanisms failing to keep pace with AI development, leading to ineffective governance and the inability to manage AI risks appropriately.
Real-world incidents in this subdomain
- Nippon Life Alleged ChatGPT Practiced Law Without a License in Illinois Disability Case
- OpenAI Allegedly Did Not Alert RCMP After ChatGPT Flagged Violent Chats Before British Columbia School Shooting
- Remotely Operated Taser-Armed Drones Proposed by Taser Manufacturer as Defense for School Shootings in the US
How other frameworks describe this risk
- Mobility
- Liability issues in case of accidents
- Faster scientific progress makes it harder for governance to keep pace with development
- Type 1: Diffusion of responsibility
- Products Liability Law
- Difficult to develop metrics for evaluating benefits or harms caused by AI assistants
- Institutional responsibilities
- Benchmarking (Cross-lingual data contamination)