A Psychologist Explains How AI and Algorithms Are Changing Our Lives

In an age of ChatGPT, computer algorithms and artificial intelligence are increasingly embedded in our lives, choosing the content we’re shown online, suggesting the music we hear and answering our questions.

These algorithms may be changing our world and behaviour in ways we don’t fully understand, says psychologist and behavioural scientist Gerd Gigerenzer, the director of the Harding Center for Risk Literacy at the University of Potsdam in Germany. Previously director of the Center for Adaptive Behaviour and Cognition at the Max Planck Institute for Human Development, he has conducted research over decades that has helped shape understanding of how people make choices when faced with uncertainty.

In his latest book, “How to Stay Smart in a Smart World,” Dr. Gigerenzer looks at how algorithms are shaping our future—and why it is important to remember they aren’t human. He spoke with the Journal for The Future of Everything podcast.

The term algorithm is thrown around so much these days. What are we talking about when we talk about algorithms?

It is a huge thing, and therefore it is important to distinguish what we are talking about. One of the insights in my research at the Max Planck Institute is that if you have a situation that is stable and well defined, then complex algorithms such as deep neural networks are certainly better than human performance. Examples are [the games] chess and Go, which are stable. But if you have a problem that is not stable—for instance, you want to predict a virus, like a coronavirus—then keep your hands off complex algorithms. [Dealing with] the uncertainty—that is more how the human mind works, to identify the one or two important cues and ignore the rest. In that type of ill-defined problem, complex algorithms don’t work well. I call this the “stable world principle,” and it helps you as a first clue about what AI can do. It also tells you that, in order to get the most out of AI, we have to make the world more predictable.

So after all these decades of computer science, are algorithms really just still calculators at the end of the day, running more and more complex equations?

What else would they be? A deep neural network has many, many layers, but they are still calculating machines. They can do much more than ever before with the help of video technology. They can paint, they can construct text. But that doesn’t mean that they understand text in the sense humans do.

Does being able to understand how these algorithms are making decisions help people?

Transparency is immensely important, and I believe it should be a human right. If it is transparent, you can actually modify that and start thinking [for] yourself again rather than relying on an algorithm that isn’t better than a bunch of badly paid workers. So we need to understand the situation where human judgment is needed and is actually better. And also we need to pay attention that we aren’t running into a situation where tech companies sell black-box algorithms that determine parts of our lives. It is about everything including your social and your political behaviour, and then people lose control to governments and to tech companies.

You write that “digital technology can easily tilt the scales toward autocratic systems.” Why do you say that? And how is this different from past information technologies?

This kind of danger is a real one. Among all the benefits it has, one of the vices is the propensity for surveillance by governments and tech companies. But people don’t read privacy policies anymore, so they don’t know. And also the privacy policies are set up in a way that you can’t really read them. They are too long and complicated. We need to get control back.

So then how should we be smart about something like this?

Think about a coffee house in your hometown that serves free coffee. Everyone goes there because it is free, and all the other coffee houses get bankrupt. So you have no choice anymore, but at least you get your free coffee and enjoy your conversations with your friends. But on the tables are microphones and on the walls are video cameras that record everything you say, every word, and to whom, and send it off to analyze. The coffee house is full of salespeople who interrupt you all the time to offer you personalised products. That is roughly the situation you are in when you are on Facebook, Instagram or other platforms. [Meta Platforms Inc., the parent company of Facebook and Instagram, declined to comment.] In this coffee house, you aren’t the customer. You are the product. So we want to have a coffee house where we are allowed again to pay [for] ourselves, so that we are the customers.

We’ve seen this whole infrastructure around personalised ads be baked into the infrastructure of the internet. And it seems like it would take some pretty serious interventions to make that go away. If you’re being realistic, where do you think we’re going to be headed in the next decade or so with technology and artificial intelligence and surveillance?

In general, I have more hope that people realise that it isn’t a good idea to give your data and your responsibility for your own decisions to tech companies who use it to make money from advertisers. That can’t be our future. We pay everywhere else with our [own] money, and that is why we are the customers and have the control. There is a true danger that more and more people are sleepwalking into surveillance and just accept everything that is more convenient.

But it sounds so hard, when everything is so convenient, to read privacy policies and do research on these algorithms that are affecting my life. How do I push back against that?

The most convenient thing isn’t to think. And the alternative is start thinking. The most important [technology to be aware of] is a mechanism that psychologists call “intermittent reinforcement.” You get a reinforcement, such as a “Like,” but you never know when you will get it. People keep going back to the platform and checking on their Likes. That has really changed the mentality and made people dependent. I think it is very important for everyone to understand these mechanisms and how one gets dependent. So you can get the control back if you want.

This interview has been condensed and edited.

There is ample and alarming evidence that artificial intelligence can help humans do bad things, such as committing cyberattacks, building weapons and even killing themselves or others. The Hugging Face hacking episode—and a growing list of others by poorly constrained swarms of agents—indicate just how powerful and potentially dangerous AI has quickly become.

Yet many inside the U.S. AI industry insist far worse is coming, on account of the imminent arrival of AI with superhuman and self-improving abilities.

Plenty of experts, including many who study AI harms for a living, are skeptical. The so-called doomers’ assertion that AI might decide to wipe out all of humanity—or even “just” topple human civilization—is contingent on it achieving a pace of development not yet seen.

And if the assumptions behind this global-doomsday scenario are wrong, it could lead us to curb or regulate AI in ways that don’t address its real harms.

At the center of this debate is the claim that current AI systems might take over the job of training their next versions, a process called “recursive self-improvement.” Think of it like evolution on steroids—billions of years happening at light speed within vast AI supercomputers. Anthropic Chief Executive Dario Amodei recently proposed a global agreement to slow down the pace of releasing new AI models, with the goal of delaying the arrival of recursive self-improvement.

“I don’t think we’re anywhere near ‘artificial general intelligence,’” says Melanie Mitchell, a professor at the nonprofit research group Santa Fe Institute who studies AI. She said AI is making impressive strides but thinks claims by engineers that they’ve achieved recursive self-improvement don’t stand up to scrutiny.

She is hardly alone. A recent paper by two dozen academics at Princeton, Stanford and other institutions found that even the most cutting-edge AIs are incapable of doing the original research required to advance the AI frontier.

Two of the authors involved in that paper also threw cold water on the idea that the Hugging Face swarm hack by OpenAI agents occurred because of a breakthrough in intelligence. The attack succeeded primarily because of a lack of basic technical guardrails, not an unmanageable explosion in AI capability, they wrote.

Yann LeCun, former chief AI scientist at Meta, posted that this analysis was “a welcome dose of sanity in an otherwise insane debate.”

OpenAI and Anthropic didn’t respond to several requests for comment.

The people who disagree with the doomers still consider AI to be dangerous, and point out that such systems don’t have to be particularly capable to be powerful. Some argue that AI should undergo regular evaluation by outsiders and that the companies that make it should be held responsible when their systems do harm. AI should also be treated the same as airplanes and elevators, and should be designed to do the least harm possible, they say.

“If you believe that technology is powerful enough to create novel, dangerous viruses, or to essentially take over the whole planet for some reason, then it must also be strong enough to create cures for cancer, to cure aging, to fix socio-economic or political problems,” says Christopher Canal, CEO of EquiStamp, a company that helps companies and governments evaluate AIs.

AI has shown an ability to rapidly advance because it is matching the abilities of humans who are constantly feeding it their knowledge. Sometimes it can recombine that knowledge and exceed what people have been capable of, through a kind of post-training known as reinforcement learning, as we’ve seen in mathematics.

Today’s LLMs are “models of knowledge” rather than actually intelligent, wrote Yi Ma, professor of AI at Hong Kong University.

Researchers at universities and commercial AI research labs in China wrote in a recent paper that autonomous, self-improving AI is likely to be a long way off, due to the sheer number of breakthroughs required. They also argue that humans will probably remain in the loop, supervising that process—and gating how fast it can occur.

Vals AI, a company that evaluates today’s AI models, maintains an RSI Index that benchmarks whether models can “do the research that builds the next model.” So far, no publicly released model is even close.

Yet Rayan Krishnan, CEO of Vals AI, says his team projects models will exceed humans’ ability to improve the next generation of AIs by August 2027, or sooner, and at that point could start building their successors all on their own.

“Once we get to recursive self-improvement, the fear is that all bets are off,” he says. “You could end up with a ‘fast takeoff’ situation, where the models quickly acquire skills and eclipse humans across every possible domain.”

In a reply to the resignation tweet heard round the world from Jacob Coxon, another Anthropic engineer declared his belief that those odds were at least 10% over the next decade. Many others in the industry chimed in to say they thought the percentage was even higher.

Some who argue the end is nigh say they calculate their personal p(doom) based on a chain of conditional probabilities—a bit like the Drake equation for calculating the likelihood of intelligent alien life. Since all those probabilities are based on speculation, estimates range from 0% to nearly 100%. Many land around 10%.

“The weird thing is that if you go back and look at the predictions on this over the last 10 years or more, it’s always been 10%—it’s just a nice round number,” says Mitchell. “I think it’s all vibes, and there’s no actual evidence or calculation.”

One argument against worrying about superintelligent AI is that the world is full of unlikely humanity-ending disasters, and trying to avert them all can make it impossible to prioritize, says Canal.

This has led AI experts and the policymakers who listen to them to propose remedies that don’t get at its real and present dangers.

AI companies’ proposals to “pace the frontier” aren’t addressing the problem in the right way, argues Stuart Russell, a computer-science professor at the University of California, Berkeley, and the president of the International Association for Safe and Ethical Artificial Intelligence.

“It’s like saying we’re driving toward the cliff at 60 miles per hour and we’re going to drive toward it at 40 miles per hour instead, and everything will be OK,” he says.

Russell and his peers have proposed that AI companies should have to meet the same standards that govern other areas of everyday life, from air travel and buildings to food and drugs. They highlight the “behavioral red lines” AI should not be allowed to cross. Breaking into other computer systems, stealing information or advising terrorists on how to build biological weapons are all illegal for a human to do, and should be illegal for companies’ AIs as well, he argues.

The challenge for AI companies in such a proposal, he adds, is that it would be a de facto ban on today’s advanced AI systems, since the companies behind them don’t know how to make them respect such boundaries all of the time.

Others have proposed something like the Food and Drug Administration, but for AI, but setting up a new agency has so far been a nonstarter in Congress. And some prominent voices in tech have said such a structure would give up America’s AI edge to China.

Given the bipartisan groundswell of support for curbing AI companies and their creations, however, that may soon change.

“The tech industry has had this mantra for decades that regulation is bad,” says Russell. “They don’t accept the liability for any harm, and they hide behind free speech. That, I think, has to change.”