Developing Explainable AI (XAI)
Master the principles of Explainable AI to create transparent, trustworthy AI systems for critical domains like healthcare and finance.
Master the principles of Explainable AI to create transparent, trustworthy AI systems for critical domains like healthcare and finance.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Explainable AI (XAI) Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Define key XAI terminology and concepts
Evaluate interpretable and explainable approaches
Integrate XAI explanations into decision-making processes
Assess XAI systems for robustness and privacy
Apply XAI techniques to Generative AI
Skills you'll gain
This course includes:
2.6 Hours PreRecorded video
6 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This comprehensive course introduces professionals to Explainable AI (XAI), focusing on developing transparent and trustworthy AI systems. Students learn key XAI terminology, evaluate different interpretable approaches, and understand how to integrate XAI explanations into decision-making processes. The curriculum covers critical aspects like algorithmic bias, ethical considerations, and privacy concerns, with particular emphasis on applications in healthcare, finance, and criminal justice.
Responsible AI
Module 1 · 3 Hours to complete
Explainable AI Overview
Module 2 · 2 Hours to complete
Developing XAI Systems
Module 3 · 2 Hours to complete
Fee Structure
Instructor
Pioneering Responsible AI and Machine Learning Innovator at Duke University
Dr. Brinnae Bent is an esteemed faculty member in Artificial Intelligence at Duke University, where she serves as the Executive in Residence for the Master of Engineering in Artificial Intelligence program. With a robust background that bridges research and industry, Dr. Bent has led significant projects and developed impactful algorithms for major global companies, focusing on applications that enhance human health and well-being, such as noninvasive glucose monitoring and assistive technologies for mobility. She is a prolific researcher with over 30 publications, recognized for her groundbreaking work on digital biomarkers and her commitment to advancing Responsible AI practices. As an educator, Dr. Bent teaches core courses in the AI program and introduces innovative electives, including a forthcoming course on Explainable AI. Outside of her academic pursuits, she curates a weekly tech newsletter called “Spill the GPTea” and balances her professional life with personal interests as a mother, ultramarathoner, and artist. Dr. Bent's contributions to AI education and research position her as a leading voice in the field, dedicated to solving real-world challenges through technology.
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Frequently asked questions
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