Human Factors in AI
Learn to design ethical, human-centered AI systems that protect privacy, ensure fairness, and effectively augment human intelligence.
Learn to design ethical, human-centered AI systems that protect privacy, ensure fairness, and effectively augment human intelligence.
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 AI Product Management 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.
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English
پښتو, বাংলা, اردو, 2 more
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What you'll learn
Apply human-centered design to AI products
Identify and mitigate privacy risks
Implement ethical AI principles
Create trustworthy AI systems
Balance automation with human augmentation
Skills you'll gain
This course includes:
3.1 Hours PreRecorded video
4 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This comprehensive course explores the critical human aspects of AI product development. Students learn human-centered design principles, data privacy considerations, ethical AI development practices, and strategies for building trust in AI systems. The curriculum covers bias detection, fairness implementation, and effective ways to augment human intelligence with AI technologies.
Design of AI Product Experiences
Module 1 · 4 Hours to complete
Data Privacy and AI
Module 2 · 3 Hours to complete
Ethics in AI
Module 3 · 3 Hours to complete
Human and Societal Considerations
Module 4 · 6 Hours to complete
Fee Structure
Instructor
Director of AI for Product Innovation at Duke University
Jon Reifschneider is the Director of the Master of Engineering in Artificial Intelligence for Product Innovation (AIPI) program at Duke University's Pratt School of Engineering, where he also teaches graduate courses in machine learning. With a robust background in data services and analytics, Jon previously held senior management roles for 15 years, most notably as Senior Vice President at DTN, where he led the Weather Analytics division. His team developed predictive analytics systems that have become integral to the operations of major transportation, aviation, and energy utility organizations across the United States and globally. Jon's academic credentials include a B.S. in Mechanical Engineering from the University of Virginia, a Master of Engineering Management from Duke University, an M.S. in Analytics from Georgia Tech, and a Global MBA from EBS in Germany. His international experience spans the U.S., Luxembourg, Germany, and India, enriching his perspective on global engineering challenges. As a leader in integrating AI with product innovation, Jon Reifschneider is committed to advancing education and research in this dynamic field.
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Frequently asked questions
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