From the course: Foundations of Responsible AI
Join today to access over 24,800 courses taught by industry experts.
Building explainability from day one
From the course: Foundations of Responsible AI
Building explainability from day one
- Explainability is often treated as a feature to add once the system's already built. But as we've discussed through this course in practice, explainability works better when it's considered from the very beginning alongside performance goals and infrastructure design and regulatory constraints. So let's look at what explainability looks like at the technical level, when to prioritize it, and how to approach it in a way that fits the system you're building. Explainability isn't a single technique. It's a property of the system that allows people with technical backgrounds or not to understand how a decision was made, what influenced it, and how to evaluate whether it was appropriate. The right approach depends on who needs the explanation and for what purpose. So let's look at three common use cases and how explainability functions in each. Let's start with internal validation and debugging. In early development model, explainability supports faster iteration. Teams might use a…
Download courses and learn on the go
Watch courses on your mobile device without an internet connection. Download courses using your iOS or Android LinkedIn Learning app.