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Learner Reviews & Feedback for Human Factors in AI by Duke University

4.9
stars
21 ratings

About the Course

This third and final course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the critical human factors in developing AI-based products. The course begins with an introduction to human-centered design and the unique elements of user experience design for AI products. Participants will then learn about the role of data privacy in AI systems, the challenges of designing ethical AI, and approaches to identify sources of bias and mitigate fairness issues. The course concludes with a comparison of human intelligence and artificial intelligence, and a discussion of the ways that AI can be used to both automate as well as assist human decision-making. At the conclusion of this course, you should be able to: 1) Identify and mitigate privacy and ethical risks in AI projects 2) Apply human-centered design practices to design successful AI product experiences 3) Build AI systems that augment human intelligence and inspire model trust in users...

Top reviews

HN

Feb 11, 2023

Excellent course concept and material.Peer review grading process needs human and AI monitoring as a course completion certificate would inspire learners to register for this course in the future

EF

Jan 18, 2022

Well detailed insights into the precarious world of security, bias and privacy in AI

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1 - 6 of 6 Reviews for Human Factors in AI

By Hemendra P

Aug 9, 2022

The three Course bundle provides the necessary theoretical material to provide an end to end Machine Learning Analytics background. The instructor has provided necessary material to produce a meaningful machine learning product management document for the Machine Learning Team for them to implement and deploy. There are small case studies and articles to support the presentations.

The Instructor provides examples from his experience why M/L Project fail due to the lack of focus on basics and details. statistics such as 87% Machine Learning Projects failed in North America, based on a 2019 study. 82% of the applications that have been contributed in the field of AI/ML have come from Universities or Academic contributions are a good indicator about the risk and focus of AI/ML field.

By ELIOT G J

Apr 3, 2022

While preparing the video presentation, I was able to search and review a lot of related materials and papers. I would like to thank my colleague for checking out my project along with the professor who gave me the inspiration for the new course. We are also very grateful to the staff for their technical assistance in the peer review area.

By Aarks M

Feb 12, 2023

Excellent course concept and material.Peer review grading process needs human and AI monitoring as a course completion certificate would inspire learners to register for this course in the future

By Johanny P

Dec 22, 2021

This course is awesome! I'm so glad to be given the opportunity to learn world-class content in human-centered AI from experienced instructors. Thank you!

By Ewen F

Jan 19, 2022

Well detailed insights into the precarious world of security, bias and privacy in AI

By Gabriel S

Jan 30, 2023

great specialization