Human Factors in AI

Human Factors in AI

This course is part of AI Product Management Specialization

Instructor: Jon Reifschneider

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4 modules

Gain insight into a topic and learn the fundamentals.

210 reviews

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

210 reviews

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

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

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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 videos6 readings1 assignment3 discussion prompts

12 videos Total 68 minutes

Specialization Overview 4 minutes
Instructor Introduction 1 minute
Course Overiew 3 minutes
Introduction and Objectives 2 minutes
Design Thinking 15 minutes
Task Analysis 8 minutes
AI User Experience Design Considerations 4 minutes
User Inputs 6 minutes
Transparency 6 minutes
Communicating Uncertainty 9 minutes
Feedback Loops 7 minutes
Module Wrap-up 2 minutes

6 readings Total 130 minutes

About the Course 5 minutes
Report a problem with the course 5 minutes
Download Module Slides 30 minutes
An Introduction to Design Thinking Process Guide 30 minutes
Human-Centered Machine Learning 30 minutes
ML has uncertainty. Design for it. 30 minutes

1 assignment Total 30 minutes

Module 1 Quiz 30 minutes

3 discussion prompts Total 50 minutes

Introductions (Optional) 10 minutes
Task Analysis Example 20 minutes
The Cold Start Problem 20 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 videos Total 42 minutes

Introduction and Objectives 1 minute
Introduction to Data Privacy 6 minutes
Fair Information Practices (FIPs) 4 minutes
U.S. Privacy Regulation 10 minutes
E.U. General Data Protection Regulation (GDPR) 6 minutes
Privacy Challenges in AI 5 minutes
Protecting Privacy in AI 9 minutes
Module Wrap-up 2 minutes

3 readings Total 90 minutes

Download Module Slides 30 minutes
Your apps know where you were last night. And they're not keeping it a secret 30 minutes
Federated Learning: Building better products with on-device data and privacy by default 30 minutes

1 assignment Total 30 minutes

Module 2 Quiz 30 minutes

1 discussion prompt Total 20 minutes

HIPAA 20 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 videos Total 41 minutes

Introduction and Objectives 2 minutes
Fair, Accountable & Transparent AI 9 minutes
Types & Sources of Bias 13 minutes
Mitigating Potential Ethical Risks 7 minutes
Detecting & Resolving Fairness Issues 7 minutes
Module Wrap-up 2 minutes

3 readings Total 90 minutes

Download Module Slides 30 minutes
Machine Bias 30 minutes
Datasheets for Datasets 30 minutes

1 assignment Total 30 minutes

Module 3 Quiz 30 minutes

1 discussion prompt Total 20 minutes

Sources of bias 20 minutes

1 plugin Total 15 minutes

YouTube Video: How I'm Fighting Bias in Algorithms 15 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.

What's included

7 videos3 readings1 assignment1 peer review1 discussion prompt

7 videos Total 39 minutes

Introduction and Objectives 1 minute
AI and Human Intelligence 8 minutes
Automation vs. Augmentation 10 minutes
Inspiring Model Trust 10 minutes
Change Management 4 minutes
Module Wrap-up 2 minutes
Course Wrap-up 3 minutes

3 readings Total 70 minutes

Download Module Slides 30 minutes
Cognitive Collaboration: Why Humans and Computers Think Better Together 30 minutes
Share your learning experience 10 minutes