- Level Foundation
- المدة 17 ساعات hours
- الطبع بواسطة Duke University
-
Offered by
عن
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 usersالوحدات
Course Overview
1
Discussions
- Introductions (Optional)
3
Videos
- Specialization Overview
- Instructor Introduction
- Course Overiew
1
Readings
- About the Course
Designing AI Products
1
Discussions
- Task Analysis Example
3
Videos
- Introduction and Objectives
- Design Thinking
- Task Analysis
3
Readings
- Download Module Slides
- An Introduction to Design Thinking Process Guide
- Human-Centered Machine Learning
AI Product User Interface Considerations
1
Discussions
- The Cold Start Problem
5
Videos
- AI User Experience Design Considerations
- User Inputs
- Transparency
- Communicating Uncertainty
- Feedback Loops
1
Readings
- ML has uncertainty. Design for it.
Review
1
Assignment
- Module 1 Quiz
1
Videos
- Module Wrap-up
Protecting Data Privacy
1
Discussions
- HIPAA
5
Videos
- Introduction and Objectives
- Introduction to Data Privacy
- Fair Information Practices (FIPs)
- U.S. Privacy Regulation
- E.U. General Data Protection Regulation (GDPR)
2
Readings
- Download Module Slides
- Your apps know where you were last night. And they're not keeping it a secret
Privacy and AI
2
Videos
- Privacy Challenges in AI
- Protecting Privacy in AI
1
Readings
- Federated Learning: Building better products with on-device data and privacy by default
Review
1
Assignment
- Module 2 Quiz
1
Videos
- Module Wrap-up
Ethics and Bias
1
Discussions
- Sources of bias
3
Videos
- Introduction and Objectives
- Fair, Accountable & Transparent AI
- Types & Sources of Bias
2
Readings
- Download Module Slides
- Machine Bias
Mitigating Ethical Risks
2
Videos
- Mitigating Potential Ethical Risks
- Detecting & Resolving Fairness Issues
1
Readings
- Datasheets for Datasets
Review
1
Assignment
- Module 3 Quiz
1
Videos
- Module Wrap-up
Human - AI Collaboration
1
Discussions
- AI augmentation of human decision-making
3
Videos
- Introduction and Objectives
- AI and Human Intelligence
- Automation vs. Augmentation
2
Readings
- Download Module Slides
- Cognitive Collaboration: Why Humans and Computers Think Better Together
AI Product Adoption
2
Videos
- Inspiring Model Trust
- Change Management
Review
1
Assignment
- Module 4 Quiz
1
Videos
- Module Wrap-up
Course Wrap Up
1
Peer Review
- Course Project
1
Videos
- Course Wrap-up
Auto Summary
Discover the essential human elements in AI development with "Human Factors in AI," the final installment of Duke University's Pratt School of Engineering AI Product Management Specialization. This course delves into human-centered design and user experience tailored for AI products, emphasizing ethical considerations and data privacy. You'll explore strategies to identify and mitigate biases, ensuring fairness in AI systems. Additionally, the course highlights the synergy between human and artificial intelligence, focusing on the dual roles of AI in automating and supporting human decision-making. Guided by Coursera, this foundational level course spans 17 hours and offers both Starter and Professional subscription options. It's perfect for those in data science and AI looking to build AI systems that enhance human intelligence and foster user trust.

Jon Reifschneider