Science Friday
Hosted by Ira Flatow
In a new book, mathematician Adam Kucharski traces our relationship to truth, from the ancient Greeks to our AI reality.
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Science Friday is supported by MathWorks, creator of MATLAB and Simulink software for technical computing and model-based design. MathWorks, accelerating the pace of discovery in engineering and science. Learn more at mathworks.com. Hey, it's Ira Flatow, and this is Science Friday. Today on the podcast, the uncertain science of what is truth?
COVID was just this situation where a lot of this knowledge we, to some extent, had to build from scratch. You didn't get the benefit just to wait and see for a few years.
One of the foundational qualities of the human race is a search for truth. We can trace this through history, from the ancient Greeks and their belief in a universal truth, to early scientists like Isaac Newton uncovering core concepts in physics and math, to our founding fathers writing, "We hold these truths to be self-evident, that all men are created equal." It feels like, these days, our relationship to the truth is a little different. In a world of disinformation, people who have their own different set of facts, the rising influence of artificial intelligence, where does the truth fit in? And how do we determine what it is?
Joining me to discuss is my guest, Adam Kucharsky, author of "Proof: The Art and Science of Certainty." He's based in London. Welcome to Science Friday.
Thank you for having me.
You're quite welcome. What's your personal relationship with uncertainty? Are you comfortable with it?
I think it's something I've become increasingly comfortable with. So my my background was in mathematics, which is obviously this world of supposed certainty and and proofs that last forever. But increasingly, I've moved into the world of real world data, real world problems. And I think that's really made me much more aware, not only of the the uncertainty we have in trying to work out what's happening, but also the subjectivity that people can have in the level of evidence you need to eventually act on something.
And where do we set that line?
It it varies quite a lot. I mean, one of the things that has kind of become a bit of a tradition in statistics, and it can be traced back about a hundred years, is this idea that, you know, you don't want to set the bar too high, because then things that might be interesting or useful, you you'll ignore, but you don't want to set it too low and let a lot of things that are false through. And one of the things we've seen is convergence in statistics, a lot of the medical literature of around this this five percent value of you want there, if you're doing a study, to be a less than five percent chance that you'd you'd get a result as unusual or as extreme as that.
So it's it's the ninety five percent certainty.
Exactly, yeah. So you see that, you know, in medical papers, in trials, you see that in experiments. But it's become this kind of hard cutoff, and it was it was fairly arbitrary how it's defined. It was actually just because it made the maths a bit easier. But of course, the the the type of certainty we need varies a lot with the problem. And other statisticians in the twentieth century, particularly William Gossett, who was working at Guinness, took a far more pragmatic outlook. And they said, well, actually, that there shouldn't be this magic value above which we were convinced and below which we're not, because it will depend on, you know, well, what's the benefit of the change we're potentially going to make? What's the cost it might have? You know, how hard is it to go out and get more evidence?
I think we see that even in our daily lives, that something that isn't particularly costly for you to experiment with, you're probably happier to make that change with with lower levels of evidence. Whereas if something's going to be enormously difficult or cause you a lot of headaches if you get it wrong, you're going to want a high level of certainty.
But people have a lot of difficulty with uncertainty, don't they?
I think it's something that humans love to go out of their way to avoid. There's even this nice study, it was internally in the CIA in the 1950s. And someone had, Sherman Kent, one of their analysts, had written a report that concluded there was a serious possibility that the then USSR might try and invade what was then Yugoslavia. And when he was talking to others, he realized that everybody had a different notion of what that term meant. And people talk about probable and possible. And he got quite frustrated because he said that actually people will kind of go out of their way to avoid being pinned down on something. So I think one of it is if there's uncertainty about the world, we don't like having to kind of deal with that.
I think also you can get situations where things can be very counterintuitive. Even in terms of how you balance different types of errors or how you might want to have to, say, update your belief based on different information. That can often create a lot of tension because it might not behave in the way we expect it to. Either whether we're trying to, for example, convince others or if in the case of some medical tests, you know, if you have things where there's a small force positive and not many people have a disease, your interpretation of that test result might be very different. You might think, oh, I've got a positive test, that means I've probably got a disease. But actually, the numbers can combine in slightly non-intuitive ways.
Right. Not only are you a mathematician, but you're also an epidemiologist. And that opens up a whole bunch of stuff to talk about, especially the first few years of the COVID pandemic. Tell me how you understood what truth certainty looked like in those early days.
Yeah, I think, I mean, COVID was just this situation where a lot of this knowledge we to some extent had to build from scratch. We had examples of it from other pathogens, from other situations. But it was something where you didn't get the benefit just to wait and see for a few years. I mean, in that sort of situation, not making a decision is making a decision. So a lot of the work that we did was building up that early evidence around everything from the severity, the extent of super-spreading, the characteristics of early variants. I think one of the things, working a lot with Evidence to inform policy I became very aware of is that in some cases you might
have quite a lot of uncertainty, but you can still say something useful as a conclusion. So to take the emergence of the alpha variant or the the delta variant is extremely hard early on to pin down exactly how much more transmissible it is. You know, it might be twenty percent, it might be forty percent, it might be sixty percent. But actually, all of those give you the same conclusion that you're going to be seeing a rising epidemic and you're going to be getting into trouble. And so I think for that, again, it was that conversion between something that might feel like quite an uncertain problem, but actually saying, can we at least know which side of the fence we're on and simplifies i- in a way that's still useful for politicians.
I think also, though, it because it was, you know, it was such a public crisis that affected so many people and your knowledge did emerge over time. I think we saw some examples in real time where there was overconfident statements about certain features of of the pandemic that worked badly, perhaps because governments, you know, didn't want to acknowledge the unknowns. But on the other hand, sometimes communicating that uncertainty was important because the situation was going to change.
Was that, though, the case? Did public officials communicate this uncertainty successfully or were was there a better way to do this?
I think we saw some countries do it a bit better, particularly, I mean, places like Denmark and Singapore stood out where your policy was going to have to change. And i- if you had like an emerging variant, for example, you might not know the exact risk of that, you have to decide what you're going to do about it and then you might want to modify that. And I think they were much better in communicating that this is what we're going to do at this point in time and this is what we might update. I think other places were. A bit more focused on saying this is the policy, this is what we're going to have to do. And then when they pivot, people get a little bit kind of confused about that.
Do you think this ultimately led some people to lose trust with science?
We're seeing a bit of a mixed picture because, you know, under certain metrics, trust in science is still high, you know, relative to a lot of other industries. I think what we've seen though in recent years is a lot of things kind of intertwined together. I mean, there was, there's been useful research in recent years that often these things are not just in isolation, that, you know, your relationship with science or your relationship with public health authorities is going to be interlinked with your relationship with, you know, other institutions, judicial systems, governments and so on. I think it also links a bit, and I've, I've seen it kind of firsthand a lot from, you know, people who've sent scientists angry messages
in, in kind of your relationship with kind of wider consensus. So I gave a talk on, on conspiracy theories early in the year and it was really striking. How many of the comments, yeah, first of all, there's, there was a lot of detail. This wasn't just, you know, one off random comments, but it was also, it, it, there was this very strong idea of we're seeing a truth that other people aren't seeing. It's very, almost kind of a feeling of community underlying it. So I think there, there's a lot of these other dynamics in terms of how it influences your relationship with power, institutions, authority. Are you actually this community that have this hidden truth? I think there's, there's a lot of other dimensions beyond that, just pure trust in a scientific fact.
Well, you as a scientist, are you frustrated with the politics of truth here?
I think one of the things that becomes, I think, I think very challenging is where scientific evidence and political choices get very intertwined. And I think, yeah, there, there's, there's kind of an obvious example of that is where science is either constrained or undermined to, to kind of seek a, a sort of political or commercial goal. I think that's what, what we've seen, you know, if you look back at things like smoking, for example, a huge effort to, to kind of undermine a lot of the scientific evidence. I think it also becomes challenging. I think we saw, you know, during COVID, this, this follow the science was this, this mantra. And I think a lot of it was. Essentially politicians using it as cover for not having to make very difficult decisions. I mean, the science could only take you so far.
And I think one of the things that we were always thinking a lot about how to present is, you know, you have essentially a series of bad options. And I think anyone who says that there was a simple solution to the pandemic is wrong, that there was a bunch of very difficult trade offs. And actually, even if you look back to the 1918 pandemic, it's fascinating how many of those newspaper quotes you could have just pulled out of 2020, you know, it's just arguing about which bits of society should be valued in different ways. And then it's very much on politicians to make those decisions. And yeah, as a society to weigh those things up. And I think increasingly, a lot of that has kind of got embedded more on that was a scientific choice. When actually, you know, science can't weigh up all of those features of society in those kind of ways. And yeah, in my view, I don't think it should.
I think it's something like epidemiology is one thread contributing to that, that wider, very difficult decision.
Well, we're seeing that not only medically, but we're seeing that with the environmental situation, with the denial of climate change and the climate crisis, like, oh, don't, don't look over there. You know, let's just ignore that and do away with it like it doesn't exist.
Yeah, it's also just really striking that the kind of the levels that the disagreements have. And I think if you look at something like climate change, yeah, I think the healthy debate is where you have the documented evidence about the situation we're facing. You have the evidence, which has a lot more uncertainty about what we might do about it. And then you have the policy decision about what we should do about it. I think that was one of the things that was striking. I talked to quite a lot of climate scientists for the book, that there is very good scientific consensus on the kind of the what is happening. There's less consensus on of all these policy levers, exactly which one's going to have which effect, what should we be doing about it? But I think what's, what's been happening, as you say, is that people have kind of gone back to those fundamentals.
And rather than having debates, and perhaps because they don't like some of those changes that can be required, they go down more fundamentally. And we see that with other interventions as well, that. I think understandably for Covid, there were some people who were not making out things like vaccine mandates because it was seen as infringement of freedom. But rather than just talking about the policy, it went back to, okay, well, vaccines don't work, or, you know, claims that the pathogen itself isn't very severe at all. And I think ultimately, it's dislike for the policy, which then you get people kind of trying to target the system further down instead, even though for something, whether we're talking about the severity of something like Covid or the extent of hazard that climate change is facing, that foundational evidence is very strong.
We have to take a break, and when we come back, how technology is fogging the lens through which we see truth.
Growing up with science, you have this idea that there's a really elegant theory, and you can kind of understand it. I think even the mathematician in me, you know, you want to get pen and paper out, and that era has kind of moved on.
Stay with us.
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There are a lot of things about science and medicine that we don't know the truth behind. Simple things like anesthesia. We know it works, we don't know how. Same goes for some medicines. And in physics, we know quantum physics works, but not why? Richard Feynman, a very well-known and respected Nobel Prize scientist, said, "If any scientist tells you why, they know why it happens, they're lying." So we know it works, but we don't know why. I mean, should we still consider these things the truth, even if we can't verify the why?
I think that's a really good point and it's I think it gets to the heart actually a lot of tension with our interactions with with kind of technology and particularly in the modern era, the extent to which it's important to have confidence something works versus that understanding of why something works. And it also really just gets to the heart of what is science in in the modern era. As you said, there's elements of medicine where we don't understand exactly all the underlying processes, but we have confidence if we do this, we'll get this effect. And even if you run a clinical trial, it will tell you with your often good confidence whether or not something has the effect you're you're testing, but it won't necessarily give you that why you need to get that from other sources. And I think
you're in other fields, it is that AI, for example, people are much more uncomfortable with say self-driving cars that that have accidents that we can't explain. So even if a self-driving car was on average much safer than a human, which isn't, let's be honest, massively difficult because humans, you know, do do a lot of very strange, unhelpful things when they're driving. I think there would still be that discomfort about the that that kind of lack of understanding. I think we've also seen it, you're talking to scientists who've worked on a lot of AI discoveries, so some of the kind of protein prediction work, where I think, you know, and I've certainly had it myself, that growing up with science, you have this idea that there's a really elegant theory and you can kind of understand it. I think even the the mathematician in me, you know, you want to get pen and paper out and and solve it, or you want to, you know,
be able to do the tangible experiment. Abraham Lincoln, for example, taught himself all the the Greek mathematical proofs because he wanted to get better at demonstrating things. And that that era has kind of moved on and we're very much going to in an era, both in maths, a lot of the proofs that involve computers now and can't really be easily verified by hand, or in AI discovery in science where there isn't that simple explanation, but we can have predictions that can be enormously powerful and still having to come to terms with, well, this is science. And, you know, we have, just as we have these medical tools that are very valuable, we can have scientific discoveries, we can have confidence in, but we lose perhaps a little bit of that kind of romanticism around the elegance of our explanations.
Right. Well, as we go forward into this technological future, are there any lessons we can take from our predecessors?
I think one of the things that that really stands out for me is the dangers of assuming certainty. I think we've seen it in recent years with this assumption that certain algorithms have reached superhuman status. For example, in games which are very well defined, should in theory be a perfect starting point for AI to reach that kind of mastery, that actually people who've poked around have found that that's not true. And I think there's an analogy there, even going back about 150 years with that assumed idea that maths had solved it all. We had these universal truths and people start to poke around and find examples where that doesn't work. I think also being really aware of those balances we've got to strike.
I mean, even in discussion of misinformation, we focus a lot on not wanting people to believe falsehoods. But there's two errors you can make with information. One is believing things that are false, but another is not believing things that are true. And so there's, I think, increasing awareness that interventions that focus just on false information might work, but they do it by just reducing belief in all information. So we need to focus on the fact that for many of these problems, there's actually two errors in many cases that we need to be balancing. And we don't inadvertently want to intervene on one, but also undermine our trust in other information systems along the way.
Well, Adam, we have run out of time talking about so many things. I want to thank you for taking time to be with us today.
Yeah, great to chat. Thank you.
Adam Kucharsky, author of Proof, a really, really good book, a good read for the summer. Proof, the art and science of certainty. He's based in London. And you can read an excerpt from the book. Head over to our website, sciencefriday.com/prove.
Hey, thanks for listening. This episode was produced by Kathleen Davis. See you next time. I'm Ira Flatow.
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