You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or rein

Including a comprehensive set of demographic features is important for bias detection and mitigation later, but simply including them doesn't by itself prevent bias during dataset creation if the overall sampling is skewed or features are used improperly.

Including only the demographic groups that most frequently interact with advertisements would actively reinforce bias by excluding and underrepresenting other groups, leading to a model that performs poorly or unfairly for those excluded populations.

Collecting a simple random sample might inadvertently perpetuate existing biases present in the production traffic, as it does not guarantee adequate representation of minority or sensitive subgroups, potentially leading to a biased model.

Collecting a stratified sample of production traffic helps ensure that all relevant demographic groups or sensitive categories are adequately represented in the training dataset. This proportional representation is crucial for mitigating potential biases that might arise from underrepresented groups in a simple random sample, thereby preventing the model from creating or reinforcing unfair biases.

Conducting fairness tests on the trained model is a critical step for detecting and mitigating bias after training, but it is not an action taken during the dataset creation phase to directly prevent bias in the dataset itself.