AI and UX: Why Artificial Intelligence Needs User Experience, by Gavin Lew and Robert M. Schumacher Jr. 1st edition 2020, Apress. EBSCOhost, search.ebscohost.com/login.aspx?direct=true&AuthType=ip,shib&db=cat06559a&AN=ggc.996791144102945&site=eds-live&scope=site.
Why did I choose this book?
My current research interests revolve around all things generative AI, especially its applications for writing and teaching. I am trying to learn as much as I can, and I think an understanding of the UX aspects related to AI is beneficial. This led me to seek a UX book that might provide insight into how AI systems and products are designed with end users in mind.
Because the release of ChatGPT in November of 2022 drastically altered the entire AI landscape, I was hoping to find a recently released book on UX and AI, but I was unable to find one that looked like legitimate scholarship. This is not entirely surprising since it takes a while to research, write, and publish. So instead, I settled for the most recent book on the topic I could find that looked reliable. There were not many options to choose from, but this book, despite its 2020 copyright date, proved a good read for someone like me who is neither an expert in UX or AI (yet).
Who are the authors?
The authors for this book are Gavin Lew and Robert Schumacher Jr. Instead of trying to summarize their already relatively short biographies in the book, I will include them in full:
Gavin Lew has over 25 years of experience in the corporate and academic environment. He founded User Centric and grew the company to be the largest private UX consultancy in the United States. After selling the company, he continued to lead a North American UX team to become one of the most profitable business units of the parent organization. He is a frequent presenter at national and international conferences and the inventor of several patents. He is an adjunct professor at DePaul and Northwestern universities. Gavin has a Masters in Experimental Psychology from Loyola University and is currently the Managing Partner of Bold Insight, part of ReSight Global, a globally funded UX consulting practice across North America, Europe, and Asia.
Robert M. Schumacher Jr. has more than 30 years of experience in academic, agency, and corporate worlds. He co-owned User Centric with Gavin from its early stages until it was sold to GfK in 2012. While at User Centric, Bob helped found the User Experience Alliance, a global alliance of UX agencies. Also, he founded User Experience Ltd, a UX agency in Beijing. He is co-founder, co-owner, and Managing Partner of Bold Insight, part of ReSight Global, a global UX company. Bob was the editor of and contributor to The Handbook of Global User Research (2009). He has several patents and dozens of technical publications, including user interface standards for health records for the US government. He also is an Adjunct Professor at Northwestern University. Bob has a Ph.D. in Cognitive and Experimental Psychology from the University of Illinois at Urbana-Champaign.
Basically, we have two writers with plenty of UX experience (related to technology and other fields) and backgrounds in psychology. It might have been nice to have an author with more of a computer-science background paired up with someone who knows the psychology behind UX, but these two authors also have a long-established working relationship which enhances their ability to communicate throughout the book.
Summarizing the Chapters and Some Highlights:
In the preface, the authors state the following:
“Our perspective on how AI can be more successful is admittedly and unashamedly from a UX point of view. AI needs a focus on UX to be successful.”
This is a central theme in the book. The authors recognize the role UX must play in the development of AI systems, tools, and interfaces. Having now had some experience myself with a few of the generative AI platforms, I think the authors are correct, and an emphasis on UX for AI tools won’t just make those tools easier and more pleasant to use, but a better UX experience can actually save these tools from being written off by the general public as novelties or passing fads. The failure of AI to live up to hype in past decades did lead to these kinds of dismissals, but the latest wave of advancements may have reached a tipping point that insulates AI from another major cultural setback or lengthy pause.
Chapter 1: Introduction to AI and UX
This chapter does a respectable job of making the important connections between UX and AI. The authors prove that they know enough about these connections to be credible voices from which the reader can learn.
Drawing from their significant UX work, Lew and Schumacher tell us that “For any product, whether it has AI or not, the bare minimum should be that it be usable and useful. It needs to be easy to operate, perform the tasks that users ask of it accurately, and not perform tasks it isn’t asked to do. That is setting the bar really low, but there are many products in the marketplace that are so poorly designed where this minimum bar is not met” (16).
Throughout the book, the authors make a good case for the application of pretty much all general UX principles to AI products. Chapter 1 just lays out the landscape and major connections.
Chapter 2: AI and UX: Parallel Journeys
As the title implies, Chapter two provides a nice historical walk through AI and UX development. Particularly interesting is the focus on “AI winters” that followed periods of overhyped AI performance in the 1960s and again in the 1980s. Also, they mention the “domain-specific AI winter” for AI personal assistants which followed the overhyping of Siri in the early 2010s.Part of the reason for these AI winters is that the developers of the systems were not focused enough on user experience.
I appreciate the differentiation the authors try to make between HCI (human-computer interaction) and UX in chapter 2:
“Where HCI was originally focused heavily on the psychology of cognitive, motor, and perceptual functions, UX is defined at a higher level—the experiences that people have with things in their world, not just computers. HCI seemed too confining for a domain that now included toasters and door handles. Moreover, Norman, among others, championed the role of beauty and emotion and their impact on the user experience. Socio-technical factors also play a big part. So UX casts a broader net over people’s interactions with stuff. That’s not to say that HCI is/was irrelevant; it was just too limiting for the ways in which we experience our world” (50).
The way I interpret this is that HCI is akin to a substrata of UX.
Chapter 3: AI Enabled Products are Emerging All Around Us
And
Chapter 4: Garbage In, Garbage Out
These two chapters are where the book shows its age a bit as a pre-ChatGPT publication. Although there are some interesting examples of AI systems discussed in Chapter 3, the next chapter disconnects enough from the user experience that I did not find it valuable as a UX text. The focus of Chapter 4 is the data that AI runs on. The authors are correct that without quality data, the user experience of any AI product will suffer, but since current AI systems are such black boxes when it comes to their training data, this is somewhat of a moot point for me right now.
I will say that I perked up a bit reading about voice assistants and Grice’s four maxims for communication (67). Anyone studying generative AI could benefit from using those maxims as a starting point for evaluating what our machines are capable of. Current LLMs and systems based on LLMs seem to handle the three of the maxims with relative ease much of time (quantity, relevance, and clarity), but the truthfulness of LLM’s communication is where many people are finding the most problems. One could argue that truthfulness is the most important of the four, but it is obvious that advances in the other three areas have come quickly and impressively. I think it is entirely possible that AI systems make progress on that fourth maxim in the near future. And if things in the AI world are not interesting enough for someone yet, they will be once the programs are more reliably accurate purveyors of information.
Chapter 5: Applying a UX Framework
This final chapter is still relevant in the post-ChatGPT world. It ties the idea of UX and AI back together (whereas they diverged a bit in the previous two chapters). This quote at the beginning of the chapter seems especially relevant:
“For many people, there’s still a hesitance, a resistance, to adopt AI. Perhaps it is because of the influence of sci-fi movies that have planted images of Skynet and the Terminator in our minds, or simply fear of those things that we don’t understand. AI has an image problem. Risks remain that people will get disillusioned with AI again” (109).
I think the authors are correct that people could become disillusioned with AI again, but this will probably be less about the UX dimension and more about the existential threats, security concerns, and intellectual property issues that accompany 21st century AI. Either way, since AI is becoming so ubiquitous, I would not predict another AI winter like authors detail in Chapter 2.
One of the most interesting points in Chapter 5 regards the purpose of a product. As they lay out the case for applying a UX framework to AI, the authors pose the following questions:
“Probably the most important thing that defines any application is what it does—we call this “utility” or “functionality” or “usefulness.” Basically, is there a perceived functional benefit? In more formal terms, does the application (tool) fit for the purpose it was designed for?”
The reason this is interesting is because I am not sure the creators of ChatGPT and the other generative AI systems (or any of the precursors dating back to the 1960s) really had a specific end user functions in mind—at least not as the driving motivation for their creations. It seems like the systems have all been designed just to see if the creators could make a machine that could communicate like a human and display some level of “intelligence.” Along the way, clever people have figured out how to leverage this technology for different purposes, and profit-driven people have too, but I really don’t think that thoughts of the usefulness of LLMs weighed heavily on the creator’s minds. Evidence for this exists within the current user experience of ChatGPT. When users first access this application they see an interface with suggestions for how the app could be used. That is weird.
When we buy tools or access technologies, we typically already have the function in mind; that’s why we sought the tool to begin with. Generative AI companies are almost saying to the user, “Here it is. Figure out for yourself what purpose it has for you.” For the time being, that is the user experience for many users of generative AI.
As for the user experience of Lew and Schumacher’s book, I think they did a decent job of connecting two fields that need to be connected. A reader with a good grasp on AI could probably skip chapters 3 and 4, but there is plenty of helpful information and background in Chapters 1,2, and 5 that still holds up well in this four-year-old title from Springer/Apress.