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Answer: Collect a stratified sample of production traffic to build the training dataset, Conduct fairness tests across sensitive categories and demographics on the trained model
The correct answers are D and E. Option D: Collecting a stratified sample of production traffic ensures that the training data represents the diverse demographics that will be targeted by the advertising campaigns. This method helps to mitigate bias by ensuring different demographic groups or categories are proportionally represented in the training data, preventing under-representation of certain groups. Option E: Conducting fairness tests across sensitive categories and demographics on the trained model is crucial. These fairness tests help identify and address any potential biases that may have emerged during the training process. By evaluating the model's performance across different groups, you can ensure fair and responsible deployment.
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You are developing a machine learning model to assist your company in creating more targeted and effective online advertising campaigns. To achieve this, you need to compile a dataset that will be used to train the model. It is crucial to ensure that this dataset does not introduce or reinforce any unfair biases in the model's predictions. What steps should you take to create such a dataset while maintaining fairness? (Choose two.)
A
Include a comprehensive set of demographic features
B
Include only the demographic groups that most frequently interact with advertisements
C
Collect a random sample of production traffic to build the training dataset
D
Collect a stratified sample of production traffic to build the training dataset
E
Conduct fairness tests across sensitive categories and demographics on the trained model