group attribution bias

Assuming that what is true for an individual is also true for everyone in that group. The effects of group attribution bias can be exacerbated if a convenience sampling is used for data collection. In a non-representative sample, attributions may be made that don't reflect reality. See also out-group homogeneity bias and in-group bias. Also, see Fairness: Types of bias in Machine Learning Crash Course for more information.

Practitioners refer to group attribution bias when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.