In cases where certain groups are overrepresented or data is skewed, which potential cause of disparity applies here?

When attempting to mitigate bias, if you want to differentiate features that are directly predictive from features that encapsulate complex or nuanced relationships, which strategy should you apply?

True or false?

Applying a trade-off overall predictive performance between sensitive feature groups means that a model that is 97.5% accurate with similar performance across all groups can be more desirable than a model that is 99.9% accurate but has a bias against a group.

Which mitigation algorithm is a post-processing technique that applies a constraint to an existing classifier and transforms the prediction as appropriate?

Which mitigation algorithms support binary classification and regression models? Select all that apply.