Also known as: In group Bias
In-group bias is the tendency to favor one’s own group or people perceived as similar to oneself. In AI development and evaluation, it can distort sampling, testing, labeling, or product assessment when reviewers or data sources are not representative.
Showing partiality to one's own group or own characteristics. If testers or raters consist of the machine learning developer's friends, family, or colleagues, then in-group bias may invalidate product testing or the dataset. In-group bias is a form of group attribution bias. See also out-group homogeneity bias. See Fairness: Types of bias in Machine Learning Crash Course for more information.