ALT Representation bias can arise for several reasons, including: 1. The sampling methods only reach a portion of the population. For example, datasets collected through smartphone apps can under-represent lower-income or older groups, who are less likely to own smartphones. Similarly, medical data for a particular condition may only be available for the population of patients who were considered serious enough to bring

ALT Measurement bias can arise in several ways: 1. The granularity of data varies across groups. For example, if a group of factory workers is more stringently or frequently monitored, more errors will be observed in that group. This can also lead to a feedback loop wherein the group is subject to further monitoring because of the apparent higher rate of mistakes (Barocas and Selbst 2016; Ensign et al. 2017). 2. The qual

ALT Aggregation bias arises when a one-size-fit-all model is used for groups with different conditional distributions, p(Y |X). Underlying aggregation bias is an assumption that the mapping from inputs to labels is consistent across groups. In reality, this is often not the case. Group membership can be indicative of different backgrounds, cultures or norms, and a given variable can mean something quite different for a p

ALT Evaluation bias occurs when the evaluation and/or benchmark data for an algorithm doesn’t represent the target population. A model is optimized on its training data, but its quality is often measured on benchmarks (e.g., UCI datasets (Huang et al. 2007), Faces in the Wild (Dheeru and Karra Taniskidou 2017), ImageNet (Deng et al. 2009)), so a misrepresentative benchmark encourages the development of models that only p