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Glossary term
A systematic error or skew in a model's outputs, often inherited from the data or design.
In everyday AI discussion, bias means a systematic skew in a model's results, for example when it performs worse for some groups because they were underrepresented in the data.
The word has other meanings too: in statistics, bias is an error from a model being too simple, and in a neural network, a bias term is a learned offset added to a unit.
Bias in data and models can cause real harm, so measuring and reducing it is a central part of responsible AI.