Record summary
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Risk profile
How this risk is described and categorized.
"Manipulating data (e.g., training data) carries a set of assumptions on how the data should appear and be used by those performing the manipulation. Common manipulations applied on data in the context of AI models include defining the ground truth label and merging different data formats or sources. People who have little or no expertise in the domain of the data performing such manipulations may render the data unusable or harmful to the development of the AI system [173]."
Suggested mitigations
Defenses that may help with related attacks.
Limit Model Artifact Release
Verify AI Artifacts
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.