Model Explainability Is Not an Impossibility
Discover how you can see into the black box and explain what is happening with your algorithms.
Oct. 28 • 10 a.m. ET | 3 p.m. CET • Cost: Complimentary
About the webinar
AI algorithms are complicated by nature.
It’s often difficult to understand – much less justify – their conclusions.
But all is not lost. Emerging techniques for explaining algorithmic logic are shining a light into the black box.
Model explainability is not a silver bullet. However, these rapidly evolving capabilities are a critical tool in your AI toolkit.
Join Yannick Martel, AI & Analytics Lead at Capgemini, and SAS’ Brett Wujek as they discuss methods and best practices for explaining AI algorithms.
You will learn:
- Why model explainability is mandatory for machine learning in production.
- When to distinguish between local and global explanations.
- How model explanations help address bias.