As Artificial Intelligence (AI) becomes integrated into high-risk domains like healthcare, finance, and criminal justice, it is critical that those responsible for building these systems think outside the black box and develop systems that are not only accurate, but also transparent and trustworthy. This course provides a comprehensive introduction to Explainable AI (XAI), empowering you to develop AI solutions that are aligned with responsible AI principles.
Developing Explainable AI (XAI)
This course is part of Explainable AI (XAI) Specialization
Instructor: Brinnae Bent, PhD
Included with
Recommended experience
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
Skills you'll gain
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September 2024
6 assignments
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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 assignment4 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
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Frequently asked questions
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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
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