What type of bias occurs when data is skewed toward a particular subset of a group?

Sampling bias occurs when certain members of a population are systematically more likely to be selected for a study or dataset than others. This can lead to results that do not accurately reflect the entire population, as the data may be skewed toward specific characteristics or preferences of the subset that was included. This is particularly important in artificial intelligence and machine learning, as models trained on biased samples are likely to produce biased outcomes, potentially resulting in inequitable and unfair practices in applications.

In the context of AI governance, understanding and mitigating sampling bias is crucial for ensuring that algorithms function fairly and effectively across diverse populations. Addressing this issue often involves using more representative sampling methods or employing techniques to adjust for bias in the training data.

The other types of bias, while relevant in different contexts, do not directly address the issue of data representation and selection in the same way. Implicit bias refers to unconscious attitudes or stereotypes that affect understanding and actions, temporal bias relates to changes over time affecting data reliability, and overfitting pertains to a model that performs well on training data but poorly on unseen data. Each of these concepts plays a role in AI governance, but sampling bias specifically targets the skew in dataset representation.