Group attribution bias is the tendency to assume that a characteristic observed in one person or subset applies to an entire group. In AI development, it can enter datasets through non-representative sampling or labeling and can lead systems to make unfair generalizations about people.
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.