Representation bias	Data underrepresents or misrepresents subsets of the population measurement bias—data inaccurately reflects the variables.
Omitted variable bias	Omitted data. For example, the “Black-box” nature of many algorithms makes it difficult to figure out which variables were used and omitted.
Aggregation bias	Conclusions are drawn about individuals based on observations about a larger group.
Linking bias	Correlations that AI draws about particular users based on the characteristics of other users that may not be accurate in a heterogeneous population.
Label bias	Bias that arises when the same object is labeled differently based on differing perceptions by the annotator.