Which concept describes a mismatch between training data and the target population that can bias results?

Which concept describes a mismatch between training data and the target population that can bias results?

• A. Representation Bias
• B. Imbalanced Dataset
• C. Explainability
• D. Mathematical Impossibility

Answer: (A)

When training data don’t reflect the population the model will be applied to, the results can be biased. This mismatch between the data you trained on and the real-world population is called representation bias. The model picks up patterns that are true for the training sample but not for the broader group it’s meant to serve, so its predictions can be systematically skewed for underrepresented groups, regions, time periods, or conditions. For example, a credit model trained mostly on data from one geographic area or a particular demographic may not generalize well to others, leading to biased decisions. Imbalanced datasets involve unequal counts of outcomes or classes, which can skew performance metrics and learning focus, but that’s about class distribution rather than a mismatch with the target population. Explainability is about understanding how the model makes decisions, not about whether the training data represent the population. Mathematical impossibility isn’t related to how data represent the target group.

When training data don’t reflect the population the model will be applied to, the results can be biased. This mismatch between the data you trained on and the real-world population is called representation bias. The model picks up patterns that are true for the training sample but not for the broader group it’s meant to serve, so its predictions can be systematically skewed for underrepresented groups, regions, time periods, or conditions. For example, a credit model trained mostly on data from one geographic area or a particular demographic may not generalize well to others, leading to biased decisions.

Imbalanced datasets involve unequal counts of outcomes or classes, which can skew performance metrics and learning focus, but that’s about class distribution rather than a mismatch with the target population. Explainability is about understanding how the model makes decisions, not about whether the training data represent the population. Mathematical impossibility isn’t related to how data represent the target group.