Why does uncertainty bother us so much? | Adam Kucharski

0:06You're listening to TED Talks Daily, where we bring you new ideas and conversations to spark your curiosity every day. I'm your host, Elise Hu. Where do conspiracy theories come from and how do they spread? For mathematician Adam Kaczarski, this question is key, especially as we navigate a world full of complex and overwhelming things, from climate and health to AI. In his talk, he shares why he finds comfort in leaning into the unknown, but asks us to consider why it's crucial that we find better ways to trust the things we cannot explain and to explain the things that we do not trust.

0:48It's not easy to explain why aeroplanes stay in the sky. A common explanation is that the curved shape of the wing makes airflow faster above and slower beneath, creating lift. But this doesn't explain how planes can fly upside down. Another explanation is that the angle of the wing pushes air downwards, creating an equal and opposite upwards force. But this doesn't explain why, as the angle gets slightly steeper, planes can suddenly stall. The point is, aerodynamics is complex. It's difficult to understand, let alone explain in a simple, intuitive way. And yet, we trust it. And the same is true of so many other useful technologies in our lives.

1:34The idea of heart defibrillation has been around since 1899. but researchers are still working to untangle the biology and physics that means an electric shock can reset a heart. Then there's general anesthesia. We know what combination of drugs will make a patient unconscious but it's still not entirely clear exactly why they do and yet you'd probably still get that operation just like you'd still take that flight. For a long time this lack of explanation didn't really bother me. Throughout my career as a mathematician, I've worked to separate truth from fiction, whether investigating epidemics or designing new statistical methods.

2:19But the world is complicated, and that's something I'd become comfortable with. For example, if we want to know whether a new treatment is effective against a disease, we can run a clinical trial to get the answer. It won't tell us why the treatment works, but it will give us the evidence we need to take action. So I found it interesting that in other areas of life, a lack of explainability does visibly bother people. Take AI. One of the concerns about autonomous machines like self-driving cars is we don't really understand why they make the decisions they do. There will be some situations where we can get an idea of why they make mistakes.

2:59Last year, a self-driving car blocked off a fire truck responding to an emergency in Las Vegas. The reason? The firetruck was yellow and the car had been trained to recognise red ones. But even if the car had been trained to recognise yellow firetrucks, it wouldn't go through the same thought process we do when we see an emergency vehicle. Self-driving AI views the world as a series of shapes and probabilities. With sufficient training, it can convert this view into useful actions, but fundamentally, it's not seeing what we're seeing. This tension between the benefits that computers can bring and the understanding that humans have to relinquish isn't new.

3:41In 1976, two mathematicians named Kenneth Apple and Wolfgang Harkin announced the first ever computer-aided proof. Their discovery meant that for the first time in history, mathematicians had to accept a major theorem that they could not verify by hand. The theorem in question is what's known as the four-colour theorem. In short, this says, if you want to fill in a map with different colours so that no two bordering countries are the same colour, you'll only ever need four colours to do this. The mathematicians had found that there were too many map configurations to construe by hand, even if they simplified things by looking for symmetries.

4:22So they used a computer to get over the finish line. not everyone believed the proof initially maybe the computer had made an error somewhere suddenly mathematicians no longer had total intellectual control they had to trust a machine but then something curious happened while older researchers have been skeptical younger mathematicians took the opposite view why would they trust hundreds of pages of handwritten and hand-checked calculations. Surely a computer would be more accurate. Whether we're talking about anesthesia, self-driving cars or mathematical proofs, perhaps we don't need to fully understand something as long as the accuracy is high enough for what we need.

5:08Let's go back to self-driving cars. A common thought experiment when it comes to AI is what's known as the trolley problem. Suppose we have a heavy trolley or a big car and it's going to hit a group of people. But you have the option of pulling a lever to divert the vehicle so it hits only one person? Would you pull that lever? And would it matter whether the people are old or young? These kinds of decisions can sometimes crop up in real life with human drivers. In 2020, a car in Michigan swerved to avoid a truck and hit a young couple walking on the pavement, putting them in hospital for several months.

5:43Would AI have reacted differently? Well, it turned out that the car was also racing side by side with another vehicle at the time, and the driver didn't have a valid license. Before we get too deep into theoretical dilemmas, we should remember that humans often aren't very good drivers. If we could ensure there were far fewer accidents on our roads, would you mind being unable to explain the ones that did happen. In this complex world of ours, maybe we should just abandon the pursuit of explanation altogether. After all, many data-driven areas of science increasingly focus on prediction because it's fundamentally an easier problem than explaining.

6:27Like anesthesia, we can often make useful predictions about what something will do without fully understanding it. But explanation can sometimes really matter if we want a better world. The focus on prediction is particularly troubling in the field of justice. Increasingly, algorithms are used to decide whether to release people on bail or parole. The computer isn't deciding whether they've committed a crime. In effect, it's predicting whether they'll commit one in future. But ideally, we wouldn't just try and predict future crimes using an opaque algorithm. We'd try and prevent them. And that means understanding why people re-offend and what we can do to stop that happening.

7:10A lack of interest in explanation leaves a gap that in this situation creates room for injustice. But it's not the only thing that can emerge in the gap between what is happening and why it's happening. The desire for explanation can, in some cases, drive people to extremes, particularly if the science behind what they're seeing is patchy or complex. Events must have a cause, goes their logic. Something or someone must be behind them. Karl Popper, who popularised the term conspiracy theory, once talked about conspiracy theories of society. Rather than events being random or unlinked, believers develop a narrative in which all of history is mapped out by shadowy influences.

7:56Nothing is a coincidence. In some ways, conspiracy theorists are similar to scientists. They want to explain the patterns they see in the world, and they want to share those explanations with others. and they'll put a lot of effort into doing so. Because I work in health and I've appeared in the media, I've ended up interacting with quite a lot of conspiracy theorists. And one of the things you'll notice if you try and debate a conspiracy theorist is they'll usually have a mountain of scientific-looking data and papers ready to argue their point. The key difference, though, is that science frequently requires that we update our beliefs about the world rather than just double down on them.

8:42The point of evidence is to get us closer to the truth, not just pull us further into a theory. You can always tell quite quickly in a discussion when someone's trying to defend the position rather than actually discover the reality. One of the most popular conspiracy theories currently is the idea of chemtrails. This is the false claim that aeroplane vapour trails are actually a deliberate attempt to drug populations or control the weather with chemicals. Unlike the science of aeroplane wings, it's actually pretty straightforward to explain where vapour trails come from. Jet engines produce water vapour in their exhaust.

9:23When this hot vapour hits the very cold air outside, it freezes, creating a streak of tiny ice crystals in the sky. So why do claims like this persist? It's partly down to trust. unless you want to brush up on thermodynamics or buy a jet engine, at some point you're going to have to take someone's word that this is how the science works. But conspiracy theories are also about community. If people go against scientific consensus, it can make them feel like an independent thinker and part of a resistance. Then there's that crucial element, the need for an explanation beyond simple coincidence. Whether we want to push the boundaries of science or push back on conspiracy theories, we need to appreciate this very human desire to explain.

10:11I've made the mistake sometimes of neglecting this in the past. I've given people an oversimplastic explanation for a complex process and created even more confusion than there was before. Or in a situation with limited time, I've told people, it's not possible to properly untangle the complexity involved. and in doing so I failed to acknowledge that very deep-rooted need to explain. I now notice other scientists making the same mistake. They might say the evidence is clear when it isn't to a lot of people or they might say it's well established this is true without saying why it's true. This matters because increasingly we have to navigate a world that most of us struggle to fully understand.

10:58From climate and health to finance and AI, there often isn't a simple intuitive logic behind what we're seeing. But there are lots of catchy false explanations ready to lead us astray. As science becomes more advanced and more reliant on opaque or counterintuitive technologies, these challenges will only grow. I've got a PhD in maths and I still don't fully understand the details of every climate simulation or AI algorithm. So like many others, I've had to find other ways to evaluate published claims. I've turned to experts with good track records. I've sent check sources. I've looked for inconsistencies and I've tried to explain as much as I can.

11:41In this changing world, we're going to have to close this gap between knowing what is happening and wanting to know why it's happening. That means finding better ways to trust the things we can't explain and better ways to explain the things that we don't trust. Thank you.

12:07That was Adam Kaczarski at TEDxLondon in 2025. If you're curious about TED's curation, find out more at ted.com slash curationguidelines. And that's it for today's show. TED Talks Daily is part of the TED Audio Collective. This episode was produced and edited by our team, Martha Estefanos, Oliver Friedman, Brian Green, Lucy Little, Alejandra Salazar, and Tonsika Sarmar-Nivon. It was mixed by Christopher Faze-Bogan. Additional support from Emma Taubner and Daniela Ballarezo. I'm Elise Hu. I'll be back tomorrow with a fresh idea for your feed. Thanks for listening.