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Learn to adapt responsible AI that will help you build ethical models


October 11, 2021 6:00 pm GST

Fairness is the process of understanding bias in your data. Explainability shows how a machine learning model makes its predictions. And robustness measures the stability of the algorithm performance.
In this webinar, you will learn how to use a diabetes data set to predict whether a person is prone to have diabetes. You will learn the three pillars of building trustworthy AI pipelines, fairness, explainability, and robustness of the predictive models, and enhances the effectiveness of the AI predictive system. 

🎓 What will you learn?
he pillars of building trustworthy AI pipelines
Check fairness of data set using AI 360 Fairness Toolkit
Develop model
Explain the model using the AI 360 Explainability Toolkit

👩‍💻 Who should attend
Anyone who is interested building Machine Learning models
This is a beginner to intermediate session

👩‍🏫 Prerequisites
Log in or sign up for a free IBM Cloud Account: 
Register for the live stream or to watch the replay: 

🎙️ Speakers
Anam Mahmood – Developer Advocate, IBM,
Hashim Noor- Client Technical Specialist, IBM,
*By registering for this event, you acknowledge this video will be recorded and consent for it to be featured on IBM media platforms and pages.

  • IBM Developer Staff