Human Factors in AI

Human Factors in AI

This course is part of AI Product Management Specialization

Instructor: Jon Reifschneider

What you'll learn

Identify and mitigate privacy and ethical risks in AI projects
Apply human-centered design practices to design successful AI product experiences
Build AI systems that augment human intelligence and inspire model trust in users

Skills you'll gain

Machine Learning

Details to know

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Assessments

4 assignments

Taught in English

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This course is part of the AI Product Management Specialization

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There are 4 modules in this 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.

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.

What's included

12 videos5 readings1 assignment3 discussion prompts

12 videosTotal 68 minutes

Specialization Overview4 minutesPreview module
Instructor Introduction1 minute
Course Overiew3 minutes
Introduction and Objectives1 minute
Design Thinking15 minutes
Task Analysis7 minutes
AI User Experience Design Considerations4 minutes
User Inputs6 minutes
Transparency6 minutes
Communicating Uncertainty9 minutes
Feedback Loops7 minutes
Module Wrap-up1 minute

5 readingsTotal 125 minutes

About the Course5 minutes
Download Module Slides30 minutes
An Introduction to Design Thinking Process Guide30 minutes
Human-Centered Machine Learning30 minutes
ML has uncertainty. Design for it.30 minutes

1 assignmentTotal 30 minutes

Module 1 Quiz30 minutes

3 discussion promptsTotal 50 minutes

Introductions (Optional)10 minutes
Task Analysis Example20 minutes
The Cold Start Problem20 minutes

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.

What's included

8 videos3 readings1 assignment1 discussion prompt

8 videosTotal 41 minutes

Introduction and Objectives1 minutePreview module
Introduction to Data Privacy5 minutes
Fair Information Practices (FIPs)3 minutes
U.S. Privacy Regulation9 minutes
E.U. General Data Protection Regulation (GDPR)5 minutes
Privacy Challenges in AI4 minutes
Protecting Privacy in AI8 minutes
Module Wrap-up2 minutes

3 readingsTotal 90 minutes

Download Module Slides30 minutes
Your apps know where you were last night. And they're not keeping it a secret30 minutes
Federated Learning: Building better products with on-device data and privacy by default30 minutes

1 assignmentTotal 30 minutes

Module 2 Quiz30 minutes

1 discussion promptTotal 20 minutes

HIPAA20 minutes

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.

What's included

6 videos3 readings1 assignment1 discussion prompt1 plugin

6 videosTotal 40 minutes

Introduction and Objectives2 minutesPreview module
Fair, Accountable & Transparent AI8 minutes
Types & Sources of Bias12 minutes
Mitigating Potential Ethical Risks7 minutes
Detecting & Resolving Fairness Issues7 minutes
Module Wrap-up1 minute

3 readingsTotal 90 minutes

Download Module Slides30 minutes
Machine Bias30 minutes
Datasheets for Datasets30 minutes

1 assignmentTotal 30 minutes

Module 3 Quiz30 minutes

1 discussion promptTotal 20 minutes

Sources of bias20 minutes

1 pluginTotal 15 minutes

YouTube Video: How I'm Fighting Bias in Algorithms15 minutes

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.