This module introduces the course structure and objectives.

Course Introduction

This module focuses on AI interpretability and transparency. It provides various techniques and tools to help achieve interpretability and transparency in both data and AI models.

Overview of interpretability and transparency

Overview of interpretability techniques

Feature based explanations: Model agnostic

Feature based explanations: Model specific

Concept-based and example-based explanations

Tools for interpretability

Data and Model Transparency

Lab: Vertex Explainable AI

Explaining an Image Classification Model with Agent Platform Explainable AI

AI Interpretability & Transparency Question 1

AI Interpretability & Transparency Question 2

AI Interpretability & Transparency Question 3

AI Interpretability & Transparency

This module provides a summary of the entire course by covering the most important concepts, tools, and technologies.

Course Summary

Reading

Course Resources

This course introduces concepts of AI interpretability and transparency. It discusses the importance of AI transparency for developers and engineers. It explores practical methods and tools to help achieve interpretability and transparency in both data and AI models.

Responsible AI for Developers: Interpretability & Transparency


It discusses the importance of AI transparency for developers and engineers.

Reading

Responsible AI for Developers: Interpretability & Transparency

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