As artificial intelligence gets better at predicting human behavior, a business psychologist encourages people to strengthen the uniquely human skills that machine learning has yet to tap.
In this edition of Author Talks, McKinsey Global Publishing’s Raju Narisetti chats with Tomas Chamorro-Premuzic about his new book, I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique (Harvard Business Review Press, February 2023). Chamorro-Premuzic explains why some AI algorithms model humanity as a simple species, how attention has become commoditized, and why the right questions are now more valuable than the right answers. An edited version of the conversation follows.
Why did you write this, your 12th book, now?
I’m a professor of business psychology at Columbia University and UCL [University College London] and the chief innovation officer at ManpowerGroup. I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Unique is a book about the behavioral consequences or impact of artificial intelligence, including the dark side of human behavior and what we should do to upgrade ourselves as a species.
The book is written at a time that, in my view, could only be described as the AI age. Humans have always relied on technological inventiveness and innovation to shape their cultural and social evolution, and I think there can be very little doubt that the definitive technology of today is artificial intelligence, or AI.
Now, even the wider public is talking about things like ChatGPT and other conversational interfaces, and the tech giants are described mostly as data companies and as algorithmic prediction businesses.
The book was very much written in the midst of the AI age, or under the influence of AI, because I wrote the bulk of this at the height of the pandemic when we had very little physical interaction or contact with other people outside of our nuclear families. This means I was heavily influenced by hyperconnectedness and the datafication of me. Everything I did was being datafied and subjected to the predictive powers of AI during 2020 and 2021.
I can’t say that there won’t be a better era to read the book, but it certainly wouldn’t have had the same connotation and impact if we had published it five or ten years ago.
Haven’t humans always blamed technology for every problem they face?
There is a common tendency for people to overreact to things that are novel, whether in a good way or in a bad way, and technologies are a very good example of this.
Perhaps the best example is how, when the written newspaper first scaled up and productized, people feared that humans would never meet in person ever again because there would be no information or even gossip to exchange if all the news was in written form. Also, from the 1950s onward, people showed concern that television would lead to less intellectual activities, but I don’t think they were wrong because reading habits went down since mass TV was introduced.
What humans do when they’re at their best is focus. I don’t even know that I need to give a lot of examples of distractibility because the audience right now may not be able to focus 100 percent on what I’m saying. It’s likely that they’re also looking at another screen or device.
Attention is finite. There is increasing competition for it, and what happens when you have more companies, vendors, devices, and technological tools competing for our attention? It becomes commoditized, and then we’re left with very, very little [attention], which in turn values the little we have left even more.
We deceive ourselves into thinking that we can do multiple things at once, when in fact all the signs suggest that multitasking is a myth, and we’re just splitting the resources we have between lots of different activities.
I think the dominant feature of the AI age is that life in itself—if not the world in itself—has turned into a big distraction, but we’re only focused on what algorithms and artificial intelligence want us to focus on.
Has AI made people dumb?
This is a question that can only be answered with some nuance. It is important to highlight the main nuances without seeming like we’re sitting on the fence but can’t take a position. I compare it with other technological devices or inventions.
For example, we can talk about the smartphone as something that makes people smarter because when we have that device, and so long as that device is connected to the internet, we are wiser, smarter, and more adaptable than the average human today or even smart humans of the 1960s or 1760s, given that they lacked smartphones.
At the same time, if you measure how smart we are by the actual behaviors that we engage on a typical level—not by the best we can do through the device but by what we mostly do—there is very little indication that we’re being creative, curious, smart, or otherwise exercising our higher-order mental capacity in any way or form.
I think this happens with the AI age too. Just because we can ask Chatbot or open-source AI lots of questions and get the answer doesn’t mean that we’re going to spend a lot of time doing this. In fact, most technologies—and AI is no exception—are invented and optimized for efficiency. One of the qualities of efficiency is that it makes us lazier because being lazier is actually smarter than having to work hard.
So what happens when we automate our most impactful and superior cognitive capacity—thinking—and we don’t think for ourselves? I think we end up not acting in very smart ways, and then the algorithms are trained by behaviors that have very little to do with intelligence. Most of the stuff we spend doing on a habitual basis is quite predictable and monotonous and has very little to do with our imagination, creativity, or learnability—which is how we refer to curiosity.
What is learnability?
The essence of learnability is intellectual curiosity. It has to do with having a hungry mind. It’s your desire and propensity to want to understand things, to go beyond superficial answers, and to dig deeper to understand the causes of things, deep down.
It makes sense, as I highlight in the book, that in an age where all of the knowledge of the world—which seems very hard to quantify or even grasp—has been outsourced and can be crowdsourced, accessed, and retrieved on an on-demand, 24/7 basis, there is really no advantage in being knowledgeable. Rather, the advantages come from asking questions and being hungry enough for knowledge that you actually leverage access to this information.
Interestingly, way before the recent phase of the AI age, if you go back to the 1950s and 1960s a lot of scholars and researchers in the area of creativity noted that one of the main differences between creativity and expertise was that, whereas expertise is the ability to understand something and be in possession of knowledge of information, creativity consists not of having the answers to questions but in asking the right questions.
That is an integral part of learnability. Access to knowledge and information has been democratized, but the ability to utilize it in a smart way has become the essence of expertise and intellectual competence.
Why do you believe that humanity downgrades itself as AI gets better?
We spend a lot of time thinking about the limits of artificial intelligence and how much machines can upgrade themselves, especially given that one of the critical features of AI is its ability to get better and learn in an autonomous way. It’s not about how accurate or smart machine learning programs are, but how good they can get if we feed them the right data and if they have the ability to autocorrect and develop.
But when we do that we miss the fundamental point, which is: What happens to us humans while machines are getting better?
A lot of times we pay attention to this at the level of professional jobs or careers. In previous technological revolutions, people were able to create technologies that made doing certain things easier or enabled them to do more with less, but that also rendered those people irrelevant in those areas, which forced humans to reinvent themselves, upskill, and reskill.
If we take that same logic to the current age, the AI age, the main question that I try to answer in the book is, “What are we doing with ourselves while machines are getting so good at understanding us that they can basically emulate or replicate most behaviors? What should we be doing now that we have created technologies, machines, and computers that can do all these things?”
I don’t know the definitive answer to this question, but I can tell you it’s probably not staring at your screen or phone for most of your day, clicking on boxes, and reacting to algorithmic recommendations to train AI to get even better.
If we think of humanity as the model that algorithms and artificial intelligence try to imitate, we’ve diluted ourselves to create a model of humanity that is too simple. Artificial intelligence has already managed to do most of the things we do. Instead of pushing ourselves to create, be curious, learn, and do things that are beyond AI’s capabilities, it’s almost like we’ve thrown in the towel and have little hope in our capabilities just because we created something that manages to emulate what we do most of the time.
That is the ask at the end of the book: to reclaim our humanity and find ways to be more than what AI thinks we are and more than what the algorithms can predict in our everyday life.
ENDS
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This article first appeared https://www.mckinsey.com
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