A. This is an example of expert system bias.

Expert system bias refers to bias introduced by the rules or logic defined by experts in the system, not by the data distribution.

B. This is an example of sample bias.

Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice. In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.

C. This is an example of hyperparameter bias.

Hyperparameter bias relates to the settings and configurations used during the training process, not the data distribution itself.

D. This is an example of algorithmic bias.

Algorithmic bias refers to biases introduced by the algorithmic processes and decision-making rules, not directly by the distribution of training data.

Based on the provided information, optionB(sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance​​.