Lab11.md

Analyzing and Mitigating Unfairness in Models

Machine learning models can often encapsulate unintentional bias that results in unfairness. For example, a machine learning model that predicts whether or not a patient should be tested for diabetes may predict more accurately for some age groups than others, with the result that a subsection of the patient population is either deprived of appropriate preventative health checks or subjected to unnecessary clinical testing.

Before You Start

Before you start this lab, ensure that you have completed the Create an Azure Machine Learning Workspace and Create a Compute Instance tasks in Getting Started with Azure Machine Learning. Then return to this lab.

Use FairLearn to analyze and mitigate unfairness

In this task, you'll train a model and use the Fairlearn package to analyze it for disparity of predictive performance across specific subsections of the population. You'll also integrate FairLearn analysis into an Azure Machine Learning workspace.

1. In Azure Machine Learning studio, view the Compute page for the workspace you created in the Getting Started with Azure Machine Learning lab; and on the Compute Instances tab, ensure your compute instance is running. If not, start it.

2. When the compute instance is running, click the Jupyter link to open the Jupyter home page in a new browser tab.

3. In the Jupyter home page, in the Users/mslearn-aml-labs folder, open the 11-Fairlearn.ipynb notebook.

4. Read the notes in the notebook, running each code cell in turn.

   Tip: If you cloned the repository previously, and the notebook file is not in the Users/mslearn-aml-labs folder, open a new terminal in your Jupyter environment and run the following commands to refresh the lab files (overwriting any changes you have made):

   cd Users/mslearn-aml-labs
   git reset --hard HEAD
   git pull

Important: When you have finished the lab, close all Jupyter tabs and Stop your compute instance to avoid incurring unnecessary costs.