Final answer:
It is impractical to protect all possible subgroups in predictive models because it can diminish the model's accuracy by overfitting to insufficient data, leading to lesser and possibly misleading predictions. Furthermore, overclassification into many small risk groups can provoke issues like moral hazard and adverse selection, complicating the balance between prediction accuracy and fairness.
Step-by-step explanation:
The question asks why it is impractical to protect all possible subgroups in predictive models. The impracticality lies in the fact that attempting to protect every subgroup can significantly diminish the accuracy of the model, as indicated by option C. When predictive models account for a vast number of subgroups, the dataset may not have enough relevant data for each subgroup, which can degrade the model's overall performance. This can happen because models rely on patterns found in historical data to make predictions. Oversegmenting the data into too many subgroups might lead to less meaningful predictions due to overfitting to noise or artifacts in the dataset rather than capturing genuine and reliable trends.
Predictive models draw upon existing data to forecast outcomes, but they may not perform well when there is insufficient data to reflect the complexity and diversity of smaller subgroups (risk groups and actuarial fairness). Additionally, efforts to classify individuals into too many risk groups can lead to problems such as moral hazard and adverse selection, which disrupt the balancing act between accurate prediction and fair treatment of individuals within those subgroups.