You work as a machine learning specialist for a large lending agency that issues mortgage loans to the residential home-buying population. You and your machine learning team build a model to assess loan risk based on loan application data. You have not yet chosen which algorithm your model will use. Still, you need to sanitize your data to ensure your data is not biased by demographic disparities, such as having different distributions for loan application outcomes for different demographic groups. Which option is the most efficient approach to use to identify bias in your data prior to training your modeling using the data?

Answer options:

A.Run a SageMaker Clarify job using the Total Variation Distance (TVD) pretraining metric.
B.Run a SageMaker Clarify job using the Difference in Proportions of Outcomes (DPO) pretraining metric.
C.Label your data using Ground Truth using Amazon Mechanical Turk with a custom labeling workflow.
D.Label your data using Ground Truth using Amazon Mechanical Turk using automated data labeling.

Correct Answer: A
Option A is correct. SageMaker Clarify allows you to identify bias during data preparation using attributes of interest, such as gender or demographic, and SageMaker Clarify runs a set of algorithms to detect the presence of bias in those attributes. The Total Variation Distance (TVD) metric measures the difference between distinct demographic distributions of the outcomes associated with different facets in a dataset, such as how different are the distributions for loan application outcomes for different demographics.
Option B is incorrect. There is no Difference in the Proportions of Outcomes (DPO) pretraining metric in SageMaker Clarify.
Option C is incorrect. Labeling your data using Ground Truth alone will not help you identify bias in your data, whether using a custom labeling workflow or automated data labeling.
Option D is incorrect. Labeling your data using Ground Truth alone will not help you identify bias in your data, whether using a custom labeling workflow or automated data labeling.
References:
Please see the Amazon SageMaker developer guide titled Measure Pretraining Bias (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-data-bias.html),
The Amazon SageMaker developer guide titled Use Amazon SageMaker Ground Truth to Label Data (https://docs.aws.amazon.com/sagemaker/latest/dg/sms.html),
The Amazon SageMaker developer guide titled Generate Reports for Bias in Pretraining Data in SageMaker Studio (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-data-bias-reports-ui.html)