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Human-Centered AI
The remarkable progress in algorithms for machine and deep learning have opened the doors to new opportunities, and some dark possibilities. However, a bright future awaits those who build on their working methods by including HCAI strategies of design and testing. As many technology companies and thought leaders have argued, the goal is not to replace people, but to empower them by making design choices that give humans control over technology.
In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education,
accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity.
In Human-Centered AI, Professor Ben Shneiderman offers an optimistic realist's guide to how artificial intelligence can be used to augment and enhance humans' lives. This project bridges the gap between ethical considerations and practical realities to offer a road map for successful, reliable systems. Digital cameras, communications services, and navigation apps are just the beginning. Shneiderman shows how future applications will support health and wellness, improve education,
accelerate business, and connect people in reliable, safe, and trustworthy ways that respect human values, rights, justice, and dignity.
390 pages, Kindle Edition
First published February 10, 2022
75 people are currently reading
332 people want to read
About the author
Ben Shneiderman
43 books15 followersAmerican computer scientist, a Distinguished University Professor in the Department of Computer Science.
Born in New York in 1947
Attended the Bronx High School of Science, and received a BS in Mathematics and Physics from the City College of New York in 1968
In 2002 his book Leonardo's Laptop: Human Needs and the New Computing Technologies was Winner of an IEEE-USA Award for Distinguished Contributions Furthering Public Understanding of the Profession.
Born in New York in 1947
Attended the Bronx High School of Science, and received a BS in Mathematics and Physics from the City College of New York in 1968
In 2002 his book Leonardo's Laptop: Human Needs and the New Computing Technologies was Winner of an IEEE-USA Award for Distinguished Contributions Furthering Public Understanding of the Profession.
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Displaying 1 - 8 of 8 reviews
January 13, 2022
Reading some popular science books is like biting into a luscious peach. Others are more like being presented with an almond - you have to do a lot of work to get through a difficult shell to get to the bit you want. This is very much an almond of a book, but it's worth the effort.
At the time of writing, two popular science topics have become so ubiquitous that it's hard to find anything new to say about them - neuroscience and artificial intelligence. Almost all the (many) AI books I've read have either been paeans to its wonders or dire warnings of how AI will take over the world or make opaque and biassed decisions that destroy lives. What is really refreshing about Ben Shneiderman's book is that it does neither of these - instead it provides an approach to benefit from AI without suffering the negative consequences. That's why it's an important piece of work.
To do this, Shneiderman takes us right back to the philosophical contrast between rationalism and empiricism. Rationalism, we discover, is driven by rules, logic and well-defined boundaries. Empiricists drive their understanding from observation of the real world where things are more fuzzy. Shneiderman then expands this distinction to that between science and innovation. Here, science is seen on focussing on the the essence of what is happening, while innovation is driven by applications.
When we get to AI, Shneiderman argues that many AI researchers take the science approach - they want to understand how people think and to reproduce human-like intelligence in computers and human-like robots. The empirical, innovation-driven AI researchers, meanwhile, focus on ways that AI can not duplicate and supplant human abilities, but support them. It's the difference between providing a human replacement and an AI-driven super tool that enables the human to work far better. Although Shneiderman makes an effort to portray both sides fairly, there is no doubt that he comes down strongly on the empirical, innovation-driven side - human-centred AI. It is exploring this distinction that makes the book important. Shneiderman argues convincingly that we need to move from AI taking decisions and actions, replacing humans, to human-centred AI that augments human abilities.
Quite a lot of this is driven by the importance of the human-computer interface. Science-driven AI tends to have poor or non-existent user interface, with the AI's processes opaque and impossible to control, where innovation driven-AI puts a lot of importance on having meaningful controls and interface. It's frustrating, then, that someone so strong on good user interface produces a book that has such a bad one - instead of the narrative structure of good writing, Human-Centred AI has the dire, rigid structure of a business book or textbook. We get sections with an opening summary, then an introductory chapter that tells you what the section is going to tell you, then a bit of useful content, before a closing chapter that summarises the section. There is so much repetition of the basic points that it becomes really irritating. The interface of cameras on smartphones, for example, are used as exemplars almost word for word many times over.
The useful content could be covered in a couple of magazine articles - yet when you hit the good stuff it is really good stuff. This is by no means the best way of putting the information across - nevertheless, by dint of this valuable message, it is one of the most important AI books of the last few years.
At the time of writing, two popular science topics have become so ubiquitous that it's hard to find anything new to say about them - neuroscience and artificial intelligence. Almost all the (many) AI books I've read have either been paeans to its wonders or dire warnings of how AI will take over the world or make opaque and biassed decisions that destroy lives. What is really refreshing about Ben Shneiderman's book is that it does neither of these - instead it provides an approach to benefit from AI without suffering the negative consequences. That's why it's an important piece of work.
To do this, Shneiderman takes us right back to the philosophical contrast between rationalism and empiricism. Rationalism, we discover, is driven by rules, logic and well-defined boundaries. Empiricists drive their understanding from observation of the real world where things are more fuzzy. Shneiderman then expands this distinction to that between science and innovation. Here, science is seen on focussing on the the essence of what is happening, while innovation is driven by applications.
When we get to AI, Shneiderman argues that many AI researchers take the science approach - they want to understand how people think and to reproduce human-like intelligence in computers and human-like robots. The empirical, innovation-driven AI researchers, meanwhile, focus on ways that AI can not duplicate and supplant human abilities, but support them. It's the difference between providing a human replacement and an AI-driven super tool that enables the human to work far better. Although Shneiderman makes an effort to portray both sides fairly, there is no doubt that he comes down strongly on the empirical, innovation-driven side - human-centred AI. It is exploring this distinction that makes the book important. Shneiderman argues convincingly that we need to move from AI taking decisions and actions, replacing humans, to human-centred AI that augments human abilities.
Quite a lot of this is driven by the importance of the human-computer interface. Science-driven AI tends to have poor or non-existent user interface, with the AI's processes opaque and impossible to control, where innovation driven-AI puts a lot of importance on having meaningful controls and interface. It's frustrating, then, that someone so strong on good user interface produces a book that has such a bad one - instead of the narrative structure of good writing, Human-Centred AI has the dire, rigid structure of a business book or textbook. We get sections with an opening summary, then an introductory chapter that tells you what the section is going to tell you, then a bit of useful content, before a closing chapter that summarises the section. There is so much repetition of the basic points that it becomes really irritating. The interface of cameras on smartphones, for example, are used as exemplars almost word for word many times over.
The useful content could be covered in a couple of magazine articles - yet when you hit the good stuff it is really good stuff. This is by no means the best way of putting the information across - nevertheless, by dint of this valuable message, it is one of the most important AI books of the last few years.
June 26, 2022
A practitioner providing specific indications and a simple framework to promote his idea of a better form of AI (“human centered”) that describes what is being discussed around improving AI and suggests the application of standard industrial safety processes to AI. While he recognized risks of AI and its many biases, I feel most of the large and very dangerous problems of AI fly past this approach that ends up being (too) simplistic. But it is pretty thorough and the framework is useful.
February 23, 2022
I was provided a copy of this book.
This is a good book but be prepared, it's a textbook. For students or mid-level tech management trying to understand the titled concept, this is a good text. The latter should skim it. For upper level management and government employees, the main focus should be Part 4, Governance Structures.
This is a good book but be prepared, it's a textbook. For students or mid-level tech management trying to understand the titled concept, this is a good text. The latter should skim it. For upper level management and government employees, the main focus should be Part 4, Governance Structures.
March 1, 2025
I’m probably not the right audience for this book. This book seems hollow to me, loaded with concepts and ideas (some of which I don’t agree with and seem to lack a more detailed treatment) but does not offer anything beyond that (no concrete suggestions, action items etc). In a word, I don’t feel inspired by reading this book. The term HCAI also feel quite loaded with too many things and the book felt to me lacking a central idea and takeaway message. Maybe also my fault because once I discovered I don’t really like it I just skimmed through it very quickly, reading only the images and highlighted bullet points, so I don’t really get the essence of it. Giving it 2 stars because I did have 2 questions that I know I will consult HCI experts I know in the future after quickly skimming through the book (so I got inspired somehow!), which the book doesn’t seem to answer (I could have missed that for sure): 1. Machines in the past work with physics laws and mathematics and are thus reliable (you can quantify them and super predictable because of math equations; eg you type a number in a calculator then you know the number coming out of it is going to be accurate). For AI especially generating e AI, they are stochastic and not entirely reliable and may never be reliable. They also work with human produced data not physics laws most of the time (images, texts etc). So how should we define reliability here, given that these machines will always have built-in unpredictability? 2. What’s the process of developing a system that would appeal to the widest possible audience? Traditional wizard in the oz method is quite limited because there is no real accordance for users to test and we can only test with like a dozen people at best. How to best develop the future systems/interfaces? And how/what will AI change this process, and what will not change?
March 4, 2024
The direct approach (AI is NOT a teammate! AI bears no responsibility for failures!) and abundance of industry examples make this an interesting read. I read it as part of a book club for college professors trying to navigate the world of AI in academics. The book was not a perfect fit for higher ed purposes, but as citizens of the world, we all benefitted from Shneiderman's message. This would be a better fit for business and government leaders in search of a way to understand and embrace human-centered AI.
July 23, 2023
Overview of the need for and how to implement human-centered approaches, which revolutionized computers, to the field of artificial intelligence. A good practitioner's guide and call for broader action.
April 8, 2022
This book seems to be a repackaged PhD dissertation with some more fluff. Completely unreadable.
May 30, 2022
Learned nothing after reading.
Displaying 1 - 8 of 8 reviews