Efficient Approach to Identify Bias in Data
Question
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?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.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)
The correct answer to the question is option A, which is to run a SageMaker Clarify job using the Total Variation Distance (TVD) pretraining metric.
Before building a machine learning model, it is essential to identify and address any biases present in the data used to train the model. Biases can occur due to demographic disparities or other factors that could affect the distribution of outcomes for different groups. Therefore, it is crucial to use a systematic approach to identify and address such biases in the data.
Amazon SageMaker Clarify is a tool that can help identify biases in data sets. It provides several pre-training and post-training metrics that can help detect different types of biases. The Total Variation Distance (TVD) pretraining metric is used to measure the difference between the distributions of outcomes for different groups in the data set. TVD measures the distance between two probability distributions, which indicates how different they are from each other.
To use SageMaker Clarify with the TVD pretraining metric, you need to perform the following steps:
Prepare the data: Before you can use SageMaker Clarify, you need to prepare your data by encoding it in a format that SageMaker Clarify can use. This step involves creating a CSV file that includes all the features and labels in your data set.
Configure the job: Once your data is prepared, you can set up a SageMaker Clarify job. To use the TVD pretraining metric, you need to configure the job to use the TVD metric and specify the sensitive attribute that you want to analyze for bias.
Run the job: Once the job is configured, you can run it. SageMaker Clarify will analyze the data and generate a report that includes the TVD metric for each sensitive attribute. The report will also include other information, such as histograms and heatmaps, to help you visualize the distribution of outcomes for different groups.
Address any identified biases: If SageMaker Clarify identifies any biases in your data, you need to address them before training your machine learning model. This could involve adjusting the data set or selecting a different algorithm.
By using SageMaker Clarify with the TVD pretraining metric, you can efficiently identify and address any biases in your data set, ensuring that your machine learning model is fair and accurate.
