Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.
Sampling Bias:
Occurs when the training data is not representative of the broader population, leading to skewed model outputs.
In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.
Why Option B is Correct:
Reflects Data Imbalance: A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.
Common Issue in ML Models: Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.
Why Other Options are Incorrect:
A. Measurement bias: Involves errors in data collection or measurement, not sampling.
C. Observer bias: Refers to bias introduced by researchers or data collectors, not the model's output.
D. Confirmation bias: Involves favoring information that confirms existing beliefs, not relevant to model output bias.
