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Description
Not all artificial intelligence (AI) is created equal. Some machine learning algorithms are completely transparent and easily auditable, while others produce opaque models. This webinar discusses the spectrum from transparency to opacity in machine learning and provides a framework for deciding when to prioritize explainability over accuracy.
Key takeaways:
- Learn about the different levels of transparency in machine learning.
- Learn how to determine explainability requirements of different use cases.
Vendors mentioned: Pegasystems, simMachines, IBM, Google, Equifax
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