Question

Why is a model that has been overfitted to its training data a source of fairness risk?

1) Because the model won't generalize to the entire population.
2) Because the model includes too much noise.
3) Because the model has a temporal bias.
4) Because the model is too complex.

Answer 1

Overfitting a model to training data poses a fairness risk as it may not generalize to the entire population, potentially learning from noise and biases in the training set which leads to erroneous predictions and unfair treatment of certain groups when the model is deployed.

Step-by-step explanation:

Overfitting a model to its training data is a source of fairness risk because the model will likely not generalize well to unseen data. When a model overfits, it does not merely capture the underlying patterns in the data but also learns the noise that is specific to the training set. This can lead to erroneous predictions when the model is applied to new data, which is the population that the model is intended to serve.

Furthermore, overfitted models may reflect and amplify biases in the training data, potentially leading to unfair treatment of certain groups or individuals when the model is used in decision-making processes. Hence, overfitting is not solely a technical issue but has broader implications for fairness and equity in the use of predictive models.