Data bias
AI Risk"Specifically, data bias refers to certain groups or certain types of elements that are over-weighted or over-represented than others in AI/ ML models, or variables that are crucial to characterize a phenomenon of interest, but are not properly captured by the learned models."
Overview
A source-backed snapshot of this AI risk.
Risk profile
How the MIT AI Risk Repository categorizes this risk.
Merged risk records
Source records unified into this canonical risk concept.
MITRISK-Zhang2022-21.01.01 - Data bias
"Specifically, data bias refers to certain groups or certain types of elements that are over-weighted or over-represented than others in AI/ ML models, or variables that are crucial to characterize a phenomenon of interest, but are not properly captured by the learned models."
MITRISK-IBM2025-65.04.01 - Data bias
"Historical and societal biases that are present in the data are used to train and fine-tune the model."
Mitigations
Defenses that may help with related attacks.
Source evidence
Research source for this risk, when available.
Included resource
Towards risk-aware artificial intelligence and machine learning systems: An overview
Original source
MIT AI Risk Repository
Open the public repository used for AI risk records and taxonomy fields.