In this series of liveProjects, you’ll apply techniques for measuring and mitigating bias in a machine learning algorithm. You’ll step into the role of a data scientist for a bank, and investigate the potential biases that arise when automated decision-making is applied to your company’s mortgage offers—in particular, whether your algorithm is biased by gender. Each project in this series covers a different aspect of fairness measurement and intervention, including exploring a dataset with a focus on fairness, and mitigating bias in a logistic regression model.
The liveProject is for beginner data scientists and software engineers looking to tackle the basic principles of measuring and mitigating ML bias. To begin this liveProject, you will need to be familiar with the following:
In this liveProject, you’ll learn to assess your training dataset for bias and identify any patterns or issues that may be unfairly prejudiced against protected characteristics.