Record summary
A quick snapshot of what this page covers.
Techniques2Attack methods connected to this risk.
Mitigations6Defenses that may help with related attacks.
Domain7. AI System Safety, Failures, & LimitationsThe broad risk area this belongs to.
Risk profile
How this risk is described and categorized.
"When sharing data between multiple organizations, documentation may be missing or inadequate, making it difficult for other organizations to understand it. For example, a lack of metadata or a change in schema by a collaborating party can result in an unusable dataset and wasted data collection efforts, or it can lead to misunderstandings about the dataset’s limitations, resulting in downstream risks related to its use [173]."
Domain7. AI System Safety, Failures, & Limitations
Subdomain7.3 > Lack of capability or robustness
Entity1 - Human
Intent2 - Unintentional
Timing1 - Pre-deployment
CategoryModel Development
SubcategoryData-related (Lack of cross-organizational documentation)
Suggested mitigations
Defenses that may help with related attacks.
Passive AI Output Obfuscation
DeploymentML Model Evaluation
Restrict Number of AI Model Queries
Business and Data UnderstandingDeployment+1 more
Limit Model Artifact Release
Business and Data UnderstandingDeployment
Control Access to AI Models and Data at Rest
Business and Data UnderstandingData Preparation+2 more
Encrypt Sensitive Information
Data PreparationML Model Engineering+1 more
AI Model Distribution Methods
Deployment
Source
Research source for this risk, when available.
Included resource
Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.