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How do you remove bias from the machine learning models & ensure that the predictions are fair? What are the three stages in which the bias mitigation solution can be applied? In this workshop we will answer these questions to help you make informed decision by consuming the results of predictive models.
Fairness in data & machine learning algorithms is critical to building safe & responsible AI systems. While accuracy is one metric for evaluating the accuracy of a machine learning model, fairness gives you a way to understand the practical implications of deploying the model in a real-world situation.
In this workshop, you will use a diabetes data set to predict whether a person is prone to have diabetes. Youll use IBM Watson Studio, IBM Cloud Object Storage, & the AI Fairness 360 Toolkit to create the data, apply the bias mitigation algorithm, & then analyze the results.
After completing this workshop, you will understand how to:
Create a project using Watson Studio
Use the AI Fairness 360 Toolkit
Agenda :
5:00pm Check-In & Introductions
5:15pm AI Fairness Tech Talk
5:30pm AI Fairness 360 Toolkit Workshop
6:30pm Q&A
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