Fairness is the process of understanding bias in your data. Explainability shows how a machine learning model makes its predictions. Lastly, robustness measures the stability of the algorithm performance.

In this webinar, you will learn how to use a fraud dataset to predict fraudulent transactions, to reduce monetary loss and mitigate risks. You will learn to address three of the pillars of building trustworthy AI pipelines (Fairness, Explainability, and Robustness of the predictive models), and enhance the effectiveness of the AI predictive system.

The pillars of building trustworthy AI pipelines

Check fairness of data set using AI 360 Fairness Toolkit

Explain the model using the AI 360 Explainability Toolkit

Anyone who is interested in building Machine Learning models

This is a beginner to intermediate session

Log in or sign up for a free IBM Cloud Account: https://ibm.biz/aiethics

Register for the live stream or to watch the replay: https://www.crowdcast.io/e/adopt-responsible-ai

If you've never heard of AI Ethics before, watch this quick video: https://www.youtube.com/watch?v=aGwYtUzMQUk

Read more about Trustworthy AI here: https://www.ibm.com/watson/trustworthy-ai

Anam Mahmood - Developer Advocate, IBM, https://www.linkedin.com/in/anam-mahmood-sheikh/

Hashim Noor- Client Technical Specialist, IBM,  https://www.linkedin.com/in/hashim-noor/

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Learn to Adopt responsible AI that will help you build Build Ethical Models