Model Explainability Forum

1024 1024 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

Today we’re bringing you the latest TWIML Discussion Series, The Model Explainability Forum.

The use of machine learning in business, government, and other settings that require users to understand the model’s predictions has exploded in recent years. This growth, combined with the increased popularity of opaque ML models like deep learning, has led to the development of a thriving field of model explainability research and practice.

In this panel discussion, we bring together experts and researchers to explore the current state of explainability and some of the key emerging ideas shaping the field. Each guest will share their unique perspective and contributions to thinking about model explainability in a practical way. We explore concepts like stakeholder-driven explainability, adversarial attacks on explainability methods, counterfactual explanations, legal and policy implications, and more. We round out the session with an audience Q&A! Check out the list of resources below!

Celebrating 400 Interviews!

You are about to listen to episode 400 of the TWIML AI Podcast, and we’re so happy to be sending it your way. While 400 is an amazing milestone, what’s even more interesting to us is how we grow and evolve over the next 100 episodes to better serve all of you. We’ve already introduced a ton of new programs this year, from video interviews to watch parties to premium courses, and this is just the beginning.

We want to hear your feedback on this occasion. Head to the comments section below and share your story on how the podcast has impacted you, your thoughts on how we can improve, and your tips on who we should interview!

As a thank you for your contribution we are printing up a fresh new batch of TWIML laptop stickers, and everyone who leaves a note will get some!

Thanks to our sponsor!

IBM Logo

Thank you to IBM for their support in helping to make this panel possible! IBM is committed to educating and supporting data scientists, and bringing them together to overcome technical, societal and career challenges. Through the IBM Data Science Community site, which has over 10,000 members, they provide a place for data scientists to collaborate, share knowledge, and support one another.

IBM’s Data Science Community site is a great place to connect with other data scientists and to find information and resources to support your career.

Join and get a free month of select IBM Programs on Coursera.

Connect with our Panelists!

Rayid Ghani, Carnegie Mellon University – Professor in the Machine Learning Department (in the School of Computer Science) and the Heinz College of Information Systems and Public Policy

Solon Barocas, Cornell University – Assistant Professor, Department of Information Science, Principal Researcher at Microsoft Research

Kush R. Varshney, IBM, Distinguished Research Staff Member and Manager at IBM Thomas J. Watson Research Center 

Alessya Labzhinova, CEO of a stealth startup and former CTO in residence AI2 

Hima Lakkaraju, Harvard  University Assistant Professor with appointments in Business School and Department of Computer Science

Join Forces!

“More On That Later” by Lee Rosevere licensed under CC By 4.0

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