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About this book

Shine a light on how user experience (UX) will play a significant role in adoption of AI technologies across industries. This book explores how AI must be developed within a UX framework.

The authors start by asking the question “how do we make AI smarter?” You will then quickly realize that the tried and true techniques of AI have been all about getting smarter. While we are making massive inroads, these may not be making AI more successful. It is not just about getting a product to market; it is—as always—about getting the product into people’s hands in a form that will be used. That demands examining the product from the perspective of the user—a stage of product development we have come to accept as fundamental. But the new world of AI will necessitate a new “UX lens” for us to see through. We should be asking the questions that will get us to that new lens.

This book is based on interviews on technologists in the field of AI and UX. It is comprised of three core areas: 1) The history of AI, 2) Product examples and failures, and 3) A UX framework to make AI successful.

What You'll Learn

Understand how the usage and success of AI depends on a great user experience Discover how technology can advance beyond “it works” to “it works well,” which subsequently increases its adoptionDetermine what ways can we let the users enhance the data to make AI better attuned to their needsRealize how you can make humans smarter in their interactions with AI

Who This Book Is For

Those interested in AI and future implications; these can be futurists, technophiles, or product designers and product managers working on AI products

Table of Contents


Chapter 1. Introduction to AI and UX

There and back again
Name any field that’s full of complex, intractable problems and that has gobs of data, and you’ll find a field that is actively looking to incorporate artificial intelligence (AI). There are direct consumer applications of AI, from virtual assistants like Alexa and Siri to the algorithms powering Facebook and Twitter’s timelines, to the recommendations that shape our media consumption habits on Netflix and Spotify. MIT is investing over a billion dollars to reshape its academic program to “create a new college that combines AI, machine learning, and data science with other academic disciplines.” The college started September 2019 and will expand into an entirely new space in 2022. Even in areas where you’d not expect to find a whiff of AI, it emerges: in the advertising campaign to its new fragrance called Y, Yves Saint Laurent showcased a model who is a Stanford University graduate and a researcher in machine vision. The commercial showcases AI as hip and cool—even displaying lines of Python code, as well as striking good looks to sell a fragrance line. AI has truly achieved mainstream appeal in a manner not seen before. AI is no longer associated with geeks and nerds. AI now sells product. 
Gavin Lew, Robert M. Schumacher Jr.

Chapter 2. AI and UX: Parallel Journeys

In this chapter, we’re going to take you through some key milestones of both AI and UX, pointing out lessons we take from the formation of the two fields. While the histories of AI and UX can fill entire volumes of their own, we will focus on specific portions of each.
Gavin Lew, Robert M. Schumacher Jr.

Chapter 3. AI-Enabled Products Are Emerging All Around Us

Technology is everywhere
Access to computing power is at our fingertips. In the palm of our hands, the mobile phones we hold are smarter than desktop machines that sat on our office spaces a decade ago. Still in our lifetime, the small screen that we stare at to read news, check email, or play a game has the computing power that used to take up an entire room as a mainframe computer. With this power comes connectivity that gives us access to information. The convergence of computing power, connectivity, and data opens the doors to so much more. Simplistically, these connected devices form what is called the Internet of Things (IoT). And now, companies are embedding some of these devices with AI. What started with voice-enabled platforms connecting to IoT devices now has broader implications of bringing intelligence to what would be ubiquitous computing.
Gavin Lew, Robert M. Schumacher Jr.

Chapter 4. Garbage In, Garbage Out

Doing a disservice to AI
Given the ever-evolving nature of AI, programmers need to continuously improve and refine their algorithms. In Chapter 1, we saw how algorithms are improved and often repurposed for different tasks, such as the credit card fraud detection system called Falcon that Craig Nies described had its roots in a visual system to detect military targets. Essentially, the foundation for pattern recognition to differentiate battlefield equipment from surrounding landscapes was applied to recognize patterns of fraud in credit card data.
Gavin Lew, Robert M. Schumacher Jr.

Chapter 5. Applying a UX Framework

A pathway for AI’s success
When we look at the ground we have covered so far in this book, we see how AI and UX have some common DNA. Both started with the advent of computers and both with a desire to create a better world. We saw how UX evolved from a need to bring the information age closer to everyone. , AI grew similarly—with some fits and starts—and is now in the mainstream of conversation.
Gavin Lew, Robert M. Schumacher Jr.


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