Developing Explainable AI (XAI)

Duke University via Coursera

Go to Course: https://www.coursera.org/learn/explainable-ai-xai

Introduction

Define key Explainable AI terminology and their relationships to each other

Describe commonly used interpretable and explainable approaches and their trade-offs

Evaluate considerations for developing XAI systems, including XAI evaluation approach, robustness, privacy, and integration with decision-making

Syllabus

Responsible AI

In this module, you will be introduced to the concept of Explainable AI and how to develop XAI systems. You will learn how to differentiate between interpretability, explainability, and transparency in the context of AI; how to identify algorithmic bias, and how to critically examine ethical considerations in the context of responsible AI. You will apply these learnings through discussions and a quiz assessment.

Explainable AI Overview

In this module, you will learn how to describe XAI techniques and approaches, examine the trade-offs and challenges in developing XAI systems, and understand emerging trends in applying XAI to Generative AI applications. You will apply these learnings through discussions and a quiz assessment.

Developing XAI Systems

In this module, you will learn how to integrate XAI explanations into decision-making processes, understand considerations for the evaluation of XAI systems, and identify ways to ensure robustness and privacy in XAI systems. You will apply these learnings through case studies, discussion, and a quiz assessment.