Model Explainability


Discover how you can see into the black box and explain what is happening with your algorithms.

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.



1 hour

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