[Demo+Webinar] Responsible AI, Bias Detection, Model Explainability

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[Demo+Webinar] Responsible AI, Bias Detection, Model Explainability

May 16 @ 12:00 pm - 1:00 pm EDT

RSVP Webinar: https://www.eventbrite.com/e/webinarkubeflow-tensorflow-tfx-pytorch-gpu-spark-ml-amazonsagemaker-tickets-45852865154

**Talk #0: Introductions and Meetup Announcements By Chris Fregly and Antje Barth**

**Talk #1: Responsible AI by Dr. Nashlie Sephus**

Abstract: Several topics fall under responsible AI that users may not be familiar with. This talk discusses those topics, examples, and best practices to keep in mind for the data science and builder community.

Speaker Bio: Dr. Nashlie Sephus is the Tech Evangelist for Amazon AI focusing on fairness and identifying biases in AI technologies. She formerly led the Amazon Visual Search team as an Applied Scientist in Atlanta, which launched visual search for replacement parts on the Amazon Shopping app in June 2018.

**Talk #2: Bias Detection and Model Explainability with Amazon SageMaker Clarify by Hasan Poonawala**

Abstract: Bias detection and explainability of AI systems has become a top concern for business leaders, data scientists, and software teams, especially in highly regulated industries. However, it can be cumbersome and time-consuming to choose from several techniques and integrate them into machine learning pipelines. In this talk, we will showcase the capabilities of Amazon SageMaker Clarify with tabular, NLP and CV datasets.

Speaker Bio: Hasan Poonawala is a AI/ML Specialist Solutions Architect at AWS, based in London, UK. He is passionate about building interpretable AI systems that are free from algorithmic bias and fair for society. He has over 12 years of work experience in software development, machine learning and research.

RSVP Webinar: https://www.eventbrite.com/e/webinarkubeflow-tensorflow-tfx-pytorch-gpu-spark-ml-amazonsagemaker-tickets-45852865154

Zoom link: https://us02web.zoom.us/j/82308186562

Related Links
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O’Reilly Book: https://www.amazon.com/dp/1492079391/
Website: https://datascienceonaws.com
Meetup: https://meetup.meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com