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Unrepresentative data risk for AI
Last updated: Feb 07, 2025
Unrepresentative data risk for AI
Alignment Icon representing alignment risks.
Accuracy
Training data risks
Traditional AI risk

Description

Unrepresentative data occurs when the training or fine-tuning data is not sufficiently representative of the underlying population or does not measure the phenomenon of interest.

Why is unrepresentative data a concern for foundation models?

If the data is not representative, then the model will not work as intended.

Parent topic: AI risk atlas

We provide examples covered by the press to help explain many of the foundation models' risks. Many of these events covered by the press are either still evolving or have been resolved, and referencing them can help the reader understand the potential risks and work towards mitigations. Highlighting these examples are for illustrative purposes only.