- Lab
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Libraries: If you want this lab, consider one of these libraries.
- Data
Assess Fairness and Mitigate Bias
In this guided Azure Machine Learning lab, you will prepare a credit risk classification model for fairness assessment, create a Responsible AI dashboard, and analyze disparities in prediction outcomes across age groups to identify potential bias.
Lab Info
Table of Contents
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Challenge
Prepare a Model and Dataset for Fairness Assessment
- Prepare a registered credit risk model and test dataset for fairness analysis in Azure Machine Learning Studio.
- Identify sensitive attributes used for fairness evaluation, such as age.
- Confirm that the correct model version and dataset are used for the governance review.
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Challenge
Create a Responsible AI Dashboard
- Configure and create a Responsible AI dashboard with fairness assessment components.
- Select the appropriate model, datasets, and target features for analysis.
- Monitor the pipeline execution until completion.
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Challenge
Analyze Fairness Metrics Across Age Groups
- Create cohorts by age range to compare model performance.
- Interpret fairness metrics to evaluate how predictions differ across age groups.
- Identify and quantify any disparities in outcomes or error rates using error analysis.
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