D and E is right answer, question asks us to select 2 right answers • To avoid creating or reinforcing unfair bias in the model, you should collect a representative and diverse dataset (option D) that includes a stratified sample of production traffic. This ensures that your training data is inclusive and accurately represents the diversity of your target audience. • Once you have collected your training dataset, you should conduct fairness tests across sensitive categories and demographics on the trained model (option E). This involves evaluating whether the model treats different demographic groups fairly and without bias. If biases are detected, you can take steps to mitigate them and ensure that your model is fair and accurate.

D and E is right answer, question asks us to select 2 right answers • To avoid creating or reinforcing unfair bias in the model, you should collect a representative and diverse dataset (option D) that includes a stratified sample of production traffic. This ensures that your training data is inclusive and accurately represents the diversity of your target audience. • Once you have collected your training dataset, you should conduct fairness tests across sensitive categories and demographics on the trained model (option E). This involves evaluating whether the model treats different demographic groups fairly and without bias. If biases are detected, you can take steps to mitigate them and ensure that your model is fair and accurate.

Agree with D and E

D. Stratified sampling to ensure the different demographic groups or categories are proportionally represented in the training data. This helps mitigate bias that might arise if certain groups are under-represented. E. Fairness tests can reveal disparities in how the model treats different populations, allowing you to identify and address potential biases.

D and E is the two answers. Two selections are required

I went D, E

DE D. Collect a stratified sample of production traffic to build the training dataset: This ensures that the training data represents the diverse demographics that will be targeted by the advertising campaigns. Random sampling might unintentionally underrepresent certain groups, leading to biased model outputs. E. Conduct fairness tests across sensitive categories and demographics on the trained model: This allows you to identify and address any potential biases that may have emerged during the training process. Evaluating the model's performance on different groups helps ensure fair and responsible deployment.

I go for D & E: A stratified sample ensures that the training data represents the distribution of the target population across relevant demographics or other sensitive categories. This helps mitigate bias arising from underrepresented groups in the data. Regularly testing the model for fairness across sensitive categories helps identify and address potential bias issues before deploying the model in production. This can involve metrics like precision, recall, and F1 score for different demographic groups.

D E. ChatGPT explanation below (but I think makes quite a lot of sense) Collect a Stratified Sample (Option D): Stratified sampling involves dividing the population into subgroups (strata) and then randomly sampling from each subgroup. This ensures that the training dataset represents the diversity of the population, helping to avoid biases. By collecting a stratified sample of production traffic, you are more likely to have a balanced representation of different demographic groups, reducing the risk of biased model outcomes. Conduct Fairness Tests (Option E): After training the model, it's crucial to conduct fairness tests to evaluate its performance across different sensitive categories and demographics. This involves measuring the model's predictions and outcomes for various groups to identify any disparities. Fairness tests help you assess and address biases that may have been inadvertently introduced during the training process.

C, D - Conducting fairness tests across sensitive categories and demographics on the trained model is indeed important. However, this option focuses on post-training analysis rather than dataset creation. While it's a crucial step for ensuring fairness, it doesn't directly address how to create a training dataset to avoid bias. Hence C,D

D. Stratified Sampling: Randomly sampling your data might not accurately represent the diversity of your target audience, potentially introducing bias by over- or under-representing certain demographics. Stratified sampling ensures your training dataset reflects the distribution of sensitive features (e.g., age, gender, income) observed in your production traffic, helping mitigate bias during model training. E. Fairness Testing: Simply collecting unbiased data isn't enough. Regularly testing your trained model for fairness across sensitive categories is crucial. This involves measuring and analyzing metrics like accuracy, precision, recall, and F1 score for different demographic groups. Identifying disparities in performance can trigger further investigation and potential re-training to address bias.