Duke University

Developing Explainable AI (XAI)

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

8 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • 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

Details to know

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Recently updated!

September 2024

Assessments

6 assignments

Taught in English

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Build your subject-matter expertise

This course is part of the Explainable AI (XAI) Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 3 modules in this course

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.

What's included

5 videos8 readings1 assignment5 discussion prompts

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.

What's included

10 videos2 readings2 assignments2 discussion prompts

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.

What's included

14 videos1 reading3 assignments3 discussion prompts

Instructor

Brinnae Bent, PhD
Duke University
3 Courses168 learners

Offered by

Duke University

Recommended if you're interested in Machine Learning

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