Sergey Tarasov – stock.adobe.com
Human-Centered Artificial Intelligence (HCAI) is a concept that seems to put human usage and access of AI technology at the forefront. To me, it seems in opposition to the “data driven” vision of some pundits, though there is the ability to differentiate between goals and development. “Human-Centered AI,” by Ben Schneiderman, is an excellent introduction to the concepts of HCAI. Be aware, though, that this isn’t a breezy, short, book aimed at quick review. This is a business school textbook. For management interested in governance and control, focus on part four of the book, discussed towards the end of this article. The section should be a must-read, even if you skim the rest.
That point is important so as not to surprise people. The book’s audience should be business personnel and students wanting a strong introduction to the issues of HCAI showing concepts that should then be drilled down into practice. It is for upper- and middle-management in the CIO, CTO, R&D and other more technical realms of an organization. The text is 376 pages in a font smaller than the usual business book. Give that content, this review will remain at a higher level than many of the book reviews in this column.
There’s an important thread running through the book. The author differentiates two different research lenses that can be used, that of science and innovation. The science approach is focused on what is possible from a technical view. Why it is being done doesn’t matter. On the other hand, Ben Schneiderman points to the innovation view, that of understanding how a technology can provide innovation in the real world. HCAI is driven from the innovation perspective.
As much as I like this book, it isn’t perfect. The big problem early is in chapter four, and that chapter should be skimmed or skipped. In it, the author presents another academic technologist’s view that the AI revolution is similar to the industrial revolution and makes the same mistake many do in claiming jobs won’t be lost. The industrial revolution took people from farms and crafts into simple shop floors. Over generations, those shop floors became more complex, but it was a stepwise advancement. Artificial intelligence isn’t that. It will take over jobs with no similar positions to fill. The gap between many of the disappearing jobs and the remaining ones are much larger than during the industrial revolution.
He also states that automation lowers cost and improves quality. The first, yes. The second is very arguable. That, however, is a discussion for another day.
Back to what I like. Chapter eight focuses on the author’s two dimensional view of human and automation controls, how they will overlap. There are some excellent examples.
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Chapter 12 is a key for understanding the science v innovation views mentioned above. While the discussion runs through the book, this chapter focuses on it in a clear way. It also discusses why the innovation view requires understanding and explainability of AI systems.
Social robots are described and discussed in detail in chapter 16. While it is a good survey of options, I do think the author misses one critical point. He points out that surveys over the years show people interested in anthropomorphic robots, with the feedback implying those robots aren’t yet good enough. Then he states, in softer words, the opinion that they will never be good enough. Too many opinions over the years have stated because AI hasn’t yet reached point X, they’ll never reach point X. That’s a stretch.
The same chapter points to what the author describes as supertools, functional devices as the alternative. They avoid the anthropomorphic trap to create usable devices with responses that are accepted. They are useful, and clearly a segment of devices that will remain; but chatbot research has also shown improving technology, creating more acceptance – as long as people know they are talking with a chatbot.
For business managers and government employees who wish to better understand the organizational impact of AI at multiple levels, part four is the meat of the matter. The author defines four levels of governance:
· Software development
· Corporate policy
· Industry & trade standards
· Governmental regulations
While the entire book is a good overview of HCAI, much of it is aimed at a mid-tier management, and even development manager, level of focus. The explanation of the four levels is something that is important to all levels in companies, industry and government. The types of governance aren’t independent, and people must be aware of how they integrate. For instance, if software developers aren’t paying attention to social demands, they won’t be prepared for government that could lay waste to expenditures of time and money. In the opposite direction, there is not much use of creating industry standards and government regulations to aren’t directly applicable to the technology.
That means government officials hiring people to better educate them in what’s possible. Long before the recent hearings on social media, and even before the famous statement by Senator Ted Stevens about the internet being “as series of tubes,” legislators have wanted to help citizens but not understood the implications of the technology and how best to address it.
It also means that corporate policy is critical, as it must be the bridge between development and the real world. Human-centered AI is an important concept. This book is a heavy introduction, and many parts of it will be useful to different audiences. Students, in academia and business, can read it all, but it is still valuable to management who need to both understand how to better direct AI development and to require appropriate AI to solve market and social challenges.