Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Duke University

Human Factors in AI

Duke University via Coursera

Overview

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

Syllabus

  • Design of AI Product Experiences
    • In this module we will discuss approaches and tools to perform human-centered design, which is critical to designing successful AI products. We will then walk through the key challenges involved in the user experience design of AI products and how to resolve them.
  • Data Privacy and AI
    • In this module we will focus on data privacy as it relates to AI products. We will first cover best practices in ensuring user privacy and the relevant U.S. and international privacy laws to be aware of. We will then discuss how AI creates unique challenges in ensuring privacy and some of the methods and tools which can be employed to protect the privacy of user data.
  • Ethics in AI
    • In this module we will discuss the three main goals of ethical AI: fairness, accountability and transparency. We will identify common sources of bias in modeling projects and discuss approaches to detecting and mitigating bias, including organizational, process, and technical components.
  • Human and Societal Considerations
    • In this module we will begin with differentiating between human intelligence and artificial intelligence, and then examine ways that they can compliment each other. We will conclude the course by learning about approaches to encourage adoption and inspire trust among users in your model.

Taught by

Jon Reifschneider

Related Courses

Reviews

Start your review of Human Factors in AI

Never Stop Learning!

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free