Instant Genius - Why we shouldn’t be afraid of personal data collection

Episode Date: June 6, 2024

These days, pretty much every move we make online is tracked in one way or another. Whether it’s through our social media accounts or online shopping habits, algorithms are getting better at painting a picture of who we are and how we think. But why do we let this happen? In this episode we catch up with science broadcaster and writer Timandra Harkness to talk about her new book, Technology is not the Problem. She tells us how various online agencies keep tabs on us, whether we should be worried about it, and why sometimes it can be too hard to resist buying that expensive pair of shoes you keep getting ads for. Learn more about your ad choices. Visit podcastchoices.com/adchoices

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combine handcrafted tradition with cutting-edge innovation and high-end materials, delivering digital precision with analog warmth, so you can experience exceptional sounds. at home. Music just as the artist intended. Visit name audio.com to learn more. Hello and welcome to Instant Genius, a bite-sized masterclass in podcast form. Each week you'll hear world-leading scientists and experts talking about the most fascinating ideas in science and technology today. I'm Jason Goodyear, commissioning editor at BBC Science

Focus. These days, pretty much every move we make online is tracked in one way or another. whether it's through our social media accounts or online shopping habits, algorithms are getting better at painting a picture of who we are and how we think. But why do we let this happen? In this episode, we catch up with science broadcaster and writer Tamandra Harkness to talk about her new book. Technology is not for the problem. She tells us how various online agencies keep tabs on us,

whether or not we should be worried about it, and why sometimes it can be too hard to resist buying that expensive pair of shoes you keep getting asked for. So welcome to the podcast and thanks very much for joining us. Thank you for having me. You're a long-time contributor to our magazine and that brand, but in case the listeners don't know who you are, can you just briefly introduce yourself, please?

I'm Svandra Harkness. I am the author of a couple of books, including the recent one, Technology is not the problem. I am also, as you say, a science technology journalist, among other types of journalism, but particularly that, and a long-time contributor to BBC Science Focus magazine, as it was, I think, when I first started writing for it. And also do present some radio programmes from time to time, and in a past life, was a comedian. So I now describe myself as a lapsed comedian.

But back in the day, with Dr. Helen Pilcher, who's a neuroscientist, we founded the UK's first comedy science double act. All good stuff. So today, we're... we're talking about your new book, as you just mentioned. Technology is not the problem. So this is all about personal data, how it's collected online, what it's used for, etc. So by way of starting, let's say I'm an average internet user, if there is such a thing. You know, I've got a Twitter, I've got a Facebook account, I watch videos on YouTube, do a bit of internet shopping. What data can be collected about me? An extraordinary amount. I mean, can I also assume,

that you have a smartphone. Yes. Because I think a majority of people, certainly who will be listening to this podcast, have a smartphone. So the smartphone tends to go everywhere with you, which means that it not only collects data about the things you do through the phone.

So things like what websites you visited, metadata about your social media activity. So it may not necessarily know exactly what words you put in a text message, for example, but it probably will know who your text. and how often you text them and what times the day and night and so on. But it will also collect things like where you are geographically and even things like how fast you move around. I mean, I don't know if you use the health data things, but sometimes I look at mine and it's telling me how evenly I've been

walking and how fast I've been walking and things like that. So it collects a lot of very characteristic data that can be linked back to you very personally. For example, For example, Spotify do a service where they can make a playlist for your running that matches your gate or you can tweak it so that it will make you speed up slightly. Well, it can do that because your phone picks up your gate, your pacing, like how many steps you take and so on. And those details about us are quite idiosyncratic, so they're quite unique to each one of us. Yeah, so we'll dig into that in a little bit.

But I think the first obvious sign that this is happening that people will be aware of is advertisements. So it's kind of interesting, I think, from a psychological point of view, how different people react to this. So some people are like, well, that's creepy. And others will be perhaps flattered that they're getting some sort of bespoke service or something like that. You know, what can we say about that? Well, sometimes I think it's not even different people. I think the same people can feel both creeped out and also flattered. I mean, certainly, speaking anecdotally for me,

I get annoyed when it's too creepy and it seems to be too closely tracking unrelated things that I've done online. But then I also get annoyed when it gets me wrong. And I think, well, that's not my football team. And you should know this social media platform because I am wearing in my avatar, I'm wearing a hat at the other football team. So I'm really insulted now. But the thing that really surprised me, and I think that's one of the things that made me think, actually, it's not just about the technology, it's actually about us, was I found some research done by advertising professionals,

but it was basically psychology research, and they got people in lab. And very sneakily, they said, oh, well, you can do some free internet browsing before we do the next thing. And then, of course, the next thing was we're going to show you adverts. and we're going to split you to three groups. This group, you're just going to see a random adverts. Group number two, you're going to see adverts targeted at the questionnaire you filled in with your name and your age and where you live and so on. And group number three, you're going to see adverts targeted behaviorally

according to this supposedly free internet browsing you were just doing. And obviously, because they're sneaky psychology researchers, the adverts were always the same adverts. And the people who thought they were targeted because of their own behaviour, liked the advert more than the other two groups. And it was an advert that was for a sophisticated restaurant. So they'd managed to plant the idea, not only that, hey, you're a sophisticated person, so you'd like the sophisticated restaurant.

But the idea really stuck because they thought, oh yeah, look, the algorithm has noticed in my half hour of random internet browsing that I am a sophisticated person. And so now I feel really good about myself and I'm going to go to this restaurant. And that was where I think I really went, okay, so because we tend to know in a very broad way that our data is gathered and we're being profiled, this has become part of our social world now. This is part of how we see ourselves. And so when the algorithm says, hey, Jason, you're a person with discerning and sophisticated tastes. So you'll like this. You go, yeah, thanks, algorithm. I am actually a very discerning person with sophisticated tastes. Yeah, so having said that I'm not immune to this myself. I recently bought an expensive pair of shoes that I didn't really need to buy because I was getting bombarded with adverts for them. You know, I've got to be honest. We all fall for it at some point, don't we? Well, yes, we do, but not in a kind of blind way that it does interact with the things that we're already thinking and doing. So the example I use is Twitter decided, because you don't actually have to put a lot of personal details in when you sign up, it decided that. I was a man. And so I was like, oh, okay, now I know this. This is why I get these adverts for beard products.

And I don't, because obviously the listener can't see me, I don't actually have a beard. And in fact, I'm not a man. But I don't buy the beard products they advertise to me because I haven't got a beard. Whereas they might advertise something else to me. And I think, oh, I wouldn't have thought of that. But actually, now you show it to me, yeah, maybe I will. So I think there is that thing of we don't automatically,

click and buy on the thing that we're shown. There has to be some other stuff going on in our lives. That means that we're in the market for that. I mean, maybe you were actually thinking, I should have some shoes that reflect my exalted status in life so that people will look at me and go, oh, there's, there's a man with expensive shoes. He must be really doing well. So having said all of that, how accurate is this getting? Obviously, I was a sucker for the shoes, but then you've got the beard product. Yeah, exactly. I'm the wrong football team.

So, I mean, I think there's a slightly weird thing going on with the advertising where the advertisers are told that they're getting this really, really bespoke audience of people who are definitely going to be interested or not definitely because it's all statistical, but people who are like, I don't know, 70% likely to be interested. And we are told that the adverts are very, very tailored and personal to us. But actually, they're not that accurate. And the people in the middle who actually do the placing of adverts are slightly selling to the advertisers, a slightly misleading idea of exactly how well selected the audiences are.

And to us, perhaps, a slightly misleading idea of how well the data understands our needs and desires. Because although it's technologically possible to know a lot of. about each of us and to link together all the data points from different sources and make a very detailed profile of each of us, in most cases, it's just not really worth it because it would be very, very expensive to do that. And then you would have to deal with a lot of regulation about are you misusing somebody's personal data? And in most cases, if you're selling someone's shoes, it's not really worth going to all that much trouble. I'm sorry if this makes you feel less special. Well, it does slightly, but I'll get over it.

So that brings me on to another really interesting thing, which is about the kind of black box nature of these algorithms. So recently, a few months ago, I started to try to learn how to play chess, and I was using a chess engine. And it tells you the best moves probabilistically, but it can't tell you why. So you have to sort of figure it out yourself.

And if you're not a very strong player, a beginner like I am, it's incredibly difficult. You know, so it's bad that it still works. Well, I think you've absolutely put your finger on it there. That most of this stuff is probabilistic. So it's not going to say why you might want to wear expensive shoes. It would just say that the data that we have on you in some way resembles data that we have on some other people who went on to buy those shoes. And therefore, statistically, it's more likely than chance that you've,

will also buy those shoes. But it's not like if I know you and I go, well, you know, the thing is I can tell that Jason's feeling a bit like, oh, I need something special maybe to lift my mood or maybe I feel that I've been not showing myself off as well as I could. And then you came back with some expensive shoes. I go, yes, and they say, I know why you bought those. Because I'm a human and you're a human. I can imagine being you and that that's the kind of thing I would do if I was in your state of mind. But the machines can't do any of that. I mean, they literally are looking for statistical resemblances. So this is why Twitter decided I was a man because of the kinds of things that I tweet about and maybe the kind of people I follow, the kind of tweets I like. A lot of them are about engineering and science and maths and motorbikes.

And statistically, probably most people whose Twitter activity resembles mine are more likely to be male. And so it went, okay, well, statistically, this person is male. But it kind of doesn't matter for advertisers because, you know, if you're right often enough, it doesn't really matter if you get it wrong about some individuals. I mean, it matters to us as individuals, but it doesn't really matter to the advertisers. So in the book, you mentioned the notion in the history of statistics of the average man. So, you know, what's the idea behind that? And how has that developed and evolved?

I really loved that when I discovered it. So it's Adolf Ketelae, this Belgian statistician in the 90th century. So this was an era where governments were starting to collect a lot of statistics about their populations. And there was this, I hesitate to call it utopian, but there was this very ambitious idea that if you just collected enough data and did enough scientific research, you could understand everything in the world and then you could fix things. And if you studied your population like that, you could like fix what was wrong with the population. You could fix social problems. And statistics was a slightly utopian thing. point. And he decided that because individuals feel that they have free will, but if you look at

human populations en masse, we do things in quite a predictable way. So for example, if you decide to get married, that's obviously a very, very personal decision. But if you look at the number of people getting married every year, there are kind of regular patterns to it overall. So he said, well, if you think of an average man as this kind of notional sentence, of if you stripped away all the individual things about each of us and all the individual reasons why we do things, then you would get this notional average man who would represent just the kind of broad social forces, if you like.

And then you could see, theoretically, you could see each of us as deviating from that average in some way or another for particular reasons. And that became very influential, partly because we were entering mass society and the people running the society were a bit like, oh, we've got all these masses of people. And with plus sides, like how are we going to make sure

they're all healthy and educated and can make the economy go forward and oh, we might need to fight a war, how could we make sure we've got them for that? And maybe a slightly negative point of view, they were starting to get worried about political unrest because this was the era of Marx, the Communist Manifesto and revolutions all over Europe and so on.

So the people running the countries were a little bit like, we really need to kind of be able to understand and maybe control what these masses of people are up to. So the idea that you have an average man who kind of represents this population that you can then use to study and even control them was very appealing to them. So how does that idea or that concept persist now? I think it's still very central to the way that statistics is used and now, of course, data is used to see us as populations as large groups of people. I mean, interestingly, one of the lesser-known things that Ketelay did was he came up with this idea of the Ketelay index, which compared the square of your height with your weight. And if this sounds familiar, that's because that's the body mass index, which is still used to look at populations and say, oh, no,

look, we're all abyss now because look at the average body mass index. And that was the thing that he came up with. Again, if you look at any individual person, this may not tell you very much, because if you look at a professional rugby player, their body mass index is massive because they're huge and musley, but they're not necessarily unhealthy. But if you look at a population, the idea is this can tell you things and maybe it can tell you that you ought to have a policy on something. But I think there's still the danger, which Ketelae himself warned again, which is that you can look at a population and say, here's a trend, here's something happening.

But you shouldn't then reverse engineer that to look back at any individual because you can't actually tell everything about an individual by using these average measures. And that is still something I think that we tend to forget, especially now we're a bit starstruck by data and computers and AI. And we think, oh, you know, the machines are so, clever and they know everything. But this tendency to apply average findings to individuals,

people, well, machines certainly, and the people writing the programs of machines still do this, I think, when they shouldn't. When you need to build up your team to handle the growing chaos at work, use Indeed sponsor jobs. It gives your job post the boost it needs to be seen and helps reach people with the right skills, certifications, and more. Spend less time searching and more time actually interviewing candidates who check all your boxes. Listeners of this show will get a $75-sponsored job credit at Indeed.com slash podcast. That's Indeed.com slash podcast. Terms and conditions apply.

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Try it for yourself at a focal powered by name boutique. Visit focal powered by name.com for more information. So let's have a look at individuals a little bit more then. So I mentioned the adverts that we get targeted by. Another thing is with music streaming services. So I think this is really interesting because, you know, I'm in my mid-40s. When I was younger, you know, I'd buy a CD, a vinyl record, share it with my friends.

But now it would have blown my mind. Everything's absolutely available. And you get recommendations by the algorithm, which are kind of like you used to get by your friends. Yes, absolutely. But kind of not like as well. I think that's the interesting thing that in some ways it's picking up on the way we used to do things. But it does transform it.

But I think you can combine the two. With Netflix, you can actually connect in your network of friends from social media so that your friends can recommend things to you. So I think probably the recommending systems are quite keen that we should bring in other individual humans as well. But it's a different kind of recommendation, isn't it? Because with a friend, you know them and you know their tastes. And so, you know, I would probably be the really uncool friend that would say,

hey, Jason, you should listen to this. And you'll yeah, yes, thanks to Mandra, but you won't because you know I have terrible taste in music. But then you've got a really cool friend who knows these really obscure records that they get from some little backstreet vinyl shop and you know that everything they recommend will be fantastic. But with a recommendation engine, you don't really know anything like that because it's all entirely based on you and your tastes and what you have previously played and liked and selected. And you don't necessarily know why it's recommending this. Is it because you liked a whole load of other things by the same artist or that were in some other way similar?

Or is it simply that other people who liked the same things as you also like this? And you have no way of knowing really what the connection is supposed to be. And even genre, I mean, the person I spoke to, Dick Sever, who researches this stuff. And he's got its lovely book called Computing Taste. And he said, well, you know, genre used to mean one thing where you knew that if you were into record shop and you said, oh, I'd like heavy metal or scar or something. Not only do you kind of know what you're getting, but the person who made the record will have been conscious that they're making a record within this genre.

But now, Spotify might post hoc allocate a track to some genre, even a new genre it's made up. And that's not something that's been chosen by the artist. So the genre becomes this label that you apply after. They're not even that you apply after. that the machine applies afterwards. And so that changes the relationship between the person making the music and the person listening to the music

because it's no longer, the person making the music is no longer in control of that context in the same way. And I do think that changes the relationship between you and the artist and the recommending, in this case, machine. So we can't really talk about taste without talking about identity,

which is something that you really go quite deeply into in the book. So how does that all fit into this? Well, this is where I ended up. When I started writing the book, I really thought it was going to be much more about technology, really a follow-up to the previous book, which is about big data, and big data does size matter, still available from all good bookshops.

And it was going to be much more about we collect the data, we profile you, we target you with things. But I just started thinking, but why is this something that we're so susceptible to and that we kind of weirdly like, even though we think it's creepy? And where I ended up with was we live in a world where identity is really, really important in all sorts of contexts, like, you know, politically. And in terms of the way we individually relate to each other and in terms of, yeah, our tastes in music or films or whatever, it all comes back to what's your identity and not only who you feel yourself to be, but how you want others to see you.

So then at that point, that's what I thought, oh, well, this is why personalisation in technology is so attractive. Because if you actually feel, and I think we all do to a greater or lesser extent, we feel that the world ought to see us in the way we want to be seen and reflect us back to ourselves so that we can be reassured that that is how we're seen. And if your smartphone says, oh yeah, well, I can do this for you constantly, I can constantly feed you things that have been selected for you and not for anybody else. And social media obviously is all about you project yourself out there, and you not only get yourself reflected back to yourself,

but everybody else responds to you in a way that reassures you, oh yes, I am making the impression that I wanted to make. And this is obviously, I mean, this is something teenagers have done for years and years and years. Like, oh, I've got to experiment with how people see me and, oh, good, this is me. Now I feel reassured about who I am. But I think that's really expanded to take over so much of the way we live these days. And so that for me was the kind of explanation. of this is why we have the technology that we have because we're both kind of obsessed with

our own identities, but also just that little bit insecure about them, which is why we need this constant reassurance that we are seeing the way we want to be seen. So let's move on to another sort of big thread of this, which fits into our identities, etc. And that's how we consume news. So this has changed enormously. It used to be that the only option was you choose a newspaper which perhaps suited your worldview or your political views and you bought that. There are only a few options. But now our sources of news are becoming tailored towards us. And how is that happening? And is that necessarily good? Well, it's happening because we don't tend to get one newspaper or even to watch one television channel anymore. We tend to, I was going to

they shop around, but it's not even like that because we tend to consume news more and more through our devices and through the internet and through social media. Now, this doesn't mean that we only get the news that somebody has posted on social media because often we go back to what they now call legacy media like newspapers and broadcasters through the social media that people will be sharing links and feeds and so on. But it does mean that the selection that each one of us gets is unique to us. So, when Whereas, yeah, as you say, we used to get roughly, once you'd picked your newspaper or maybe your favorite TV channel, you'd roughly get the same as everyone else at the same time in the same order. And now we very much get things at different times in different orders.

But we also get things mixed up much more. So on the one hand, the algorithms are feeding us things that we'll respond to. And our network of contacts will also feed us things that we respond to. and we also will go out and seek things ourselves. So it's the kind of the editors that used to be maybe one or two people at the top would be now us and everyone you follow on social media and the algorithms. So that's bad in a way because we don't have an overall shared worldview. And it is possible to go off down rabbit holes and only actually find things that reinforce your existing worldview. Although the evidence is that most people don't treat it in that way. Most people, in fact,

don't only get things that reinforce their worldview. Because the plus side of it is because you might get something from one contact, something from another contact, something from an algorithm, you might be curious about something and go, okay, well, I want to seek out a reliable source of news here. So I'm going to go to a broadcaster or a newspaper that I think, you know, is just good journalism. Actually, the evidence is, if anything, were exposed to a slightly wider range of sources than people a couple of generations ago who maybe would have only,

read one newspaper and believe everything in that newspaper and don't believe anything in the other newspapers. So we are actually exposed to a slightly wider range of sources than before. But the caveat of that is because our activities online are very tied up with our identities, we may find that we're resistant to things that do challenge our worldview. Because we feel that if our worldview is challenged, it's not just, oh, here's a new idea. rethink this, it's actually, oh, well, if I rethink this, then I might have to rethink my whole identity, because part of my identity is, I belong to this group and we think these things. And if I step out of that, do I have to leave behind this group where I feel I belong and I feel

I'm part of something? And that is harder and harder, I think, because we take even broad political issues much more personally than I think we did in the past. Interesting, yeah. So we've talked about an awful lot over the last 30 minutes or so. So having said all of this, sort of by way of summing up, do you have a sort of ending comment or something that you think we can take away from this and that we can be at least more aware of it or something we can learn from it? I think partly the clue is in the title of the book, which went through a few iterations. So then we ended up saying, well, why don't we just say,

technology is not the problem? Because I think we do tend to treat technology as if it had agency, as if the technology was controlling things in some way. And we go, oh, no, I'm so helpless. The smartphone is controlling all my attention and I can't help myself and I do this and it's controlling what I see. Especially we go, oh, it's controlling what other people see.

And that's why they're all wrong about everything. And I think we have to remember that these are just machines. Humans have made them and humans are using them. And we do actually retain some control. And, you know, if somebody shows you an advert for expensive shoes, but you don't want expensive shoes, you can say, they look nice, but I don't actually need any new shoes. Or you can say, oh, this is a new and interesting thing. I actually, maybe I should rethink what I wear on my feet, that you do have some control over your own life. But it's important to remember that because it, you, it, It's very easy to just go along with picking things off a menu. So I would say, I guess, don't be too quick to blame technology for things that you think are wrong, either with your life or with the world in general. Because the way to use it for what we want to do is to take control and say, I want my life to be something that I'm in control of as much as one can be. And so I'm going to decide what it is I want to do and then use the technology to do that.

and that we should all go out and start some new things and take a few risks and generally not to try to follow where the technology leads, but use the technology to go where we want to go. Thank you for listening to this episode of Instant Genius, brought to you from the team behind BBC Science Focus. That was Science Broadcaster and writer to Mandra Hargis. To read more about the topics we've just discussed, check out her latest book, Technology is Not the Problem.

If you like what you just heard, please do consider subscribing to Instant Genius on your app store of choice. The current issue of BBC Science Focus magazine is out now. Pick up a copy wherever you buy your favourite magazines or download us on your preferred app store. You can also find us online at sciencefocus.com. This podcast is sponsored by Name, Audio and Focal. The texture and emotional depth of music can be lost through digital sources or poor signal. Name Audio believes you can have digital precision with analog warmth. Alongside French acoustic specialist vocal,

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