Privacy, Power, and Prediction: Reclaiming Control of Your Personal Data, with Carissa Véliz | Lawyerist Podcast

Speaker A: Hi, I'm Debbie.

Speaker B: And I'm Zach. And this is the Lawyers Podcast, one of the many ways that we help lawyers build healthier firms, better businesses, and more sustainable lives. Today I talk with Carissa Valise about the power of privacy, um, actually your personal privacy, and how it's, uh, massively, massively beneficial to be thoughtful about your own personal privacy in this, um, data enabled world. Um, so, but before we get to that, though, Debbie, you and Stephanie have been working on a thing kind of in the, in the background, um, just really, really cranking away at something that I think is absolutely amazing. Y' all have, um, published a new book on leadership that came out two days ago, um, on the 28th. Tell me a little bit about that.

Speaker A: Yeah, yeah, so it's so exciting. I don't think. It's definitely a different kind of hard than launching a child into the world, but it feels a little bit like that. Right? I mean, I have been talking about law firm leadership for two decades and how critically important it is. And you know, one of the phrases that I started using a little over a year ago is rainmakers built the last era of successful law firms and leaders will build the next one. Because I really do believe that you think about the wild success that law firms have had because they're filled with great lawyers who attract great clients and they bring in great business. And law firms have been able to rely on that as the key to their success since the beginning of time.

Speaker B: And what we're.

Speaker A: Right, like literally since the beginning of time.

Speaker B: 1066. Yeah, 1066.

Speaker A: Right. But now the world has changed. The pace of change that we are dealing with is fundamentally different. And, uh, being a great lawyer and bringing in a great amount of business, the best clients that pay their bills isn't enough to get you where you need to go because of this rapid pace of change in the world. And we need leaders who can look at the operational chaos and say, how do we make this better? We need to pay more attention to our people. We're not hiring lifers anymore. We need to make great places to work. We need to think about how to attract people to stay longer. We need to think differently about the 10 year challenges and the attracting and retaining talent challenges. We need to think differently about how we develop people. And AI is part of that. But there's also a generational difference that is part of that. Right. Uh, when we think about hiring lawyers, a long time ago, they were people who might work at your firm for 10 or 20 or 30 or 40 years now we bring people in, and they don't think about the world the same way. And the way that we lead them is different. And it's not just lawyers. It's our support staff. It is our shared services staff, the people who are doing billing, the people who are on your HR team. Across the board. We have seen how tenure differences, people work there for three years or five years or 10 years instead of 10 or 15 or 20, have really impacted the culture of a law firm. And that's where leaders come in. Leaders come in because they can really impact operationally from a, uh, talent perspective, from a finance perspective, from a profitability perspective, and just, you know, overall business strategy leaders can really help lawyers, and some of those leaders could even be lawyers, but they can really help think about how the business needs to be shaped differently in the future.

Speaker B: That is. I hesitate to say that is a lot, because then people think, oh, my God, that's a lot. Um, but that all kind of focuses around this one thing that you were talking about at the beginning. At the beginning, which is we just can't rely on the brains of the people that have been there for 35 years and the status of the people who have been there for 35 years to move the law firm forward because of, uh, Honestly, umpteen different reasons at this point.

Speaker A: Right. Yeah, it's a lot.

Speaker B: Well, so I guess, um, you wrote a book about this. We did. You've been thinking about that. And. And then you and Stephanie sat down and wrote a book. What is the book? I mean, obviously that's what it's about, but what does it do? What does it, you know, where does it kind of fit in? How you. How we train these leaders? Then?

Speaker A: Um, the book is about the four cornerstones of leadership. And so that's something that Stephanie and I really worked on to make sure that we were thinking about every category of things that make someone an excellent leader in a law firm. And I mean, the truth is, it's really, really things that make people excellent leaders. But there is a nuance to leading in a law firm, and we talk about that in the book about what that nuance is, that many law firm leaders have a lot of responsibility, but they don't have matching authority. And so what we decided to do is kind of like take a step back and say, what are those four cornerstones? So let's talk about them really quickly. The first one is leading self. If you can't figure out and control how you show up as a leader, it's hard to do all the rest of it.

Speaker B: You're not going to get started, right?

Speaker A: You're not going to get started. So it starts with you leading yourself. And then the second piece, which is where a lot of leadership books and podcasts and people who talk a lot about leadership talk about the leading people aspect. That's a big part of it. And that's cornerstone number two. How do we lead people? How do we develop them? How do we give feedback? How do we coach people? And that's the second pillar. And then the third pillar is leading operations. And that's all about, you know, I talked about the operational chaos, like how do we bring order and systems so everybody doesn't feel like they have to strap on a cape when they get in in the morning and be a hero throughout their day. Right, that sounds familiar, right?

Speaker B: Yeah. And I'm sure it does for the, for the, uh, people listening. I don't know that before looking at this that I had thought about separating those two things out. The people and the operations.

Speaker C: Mhm.

Speaker A: Yeah, I think it's really important.

Speaker B: But that's still only two and three.

Speaker A: That's only two and three. And then four is leading forward. Leading forward is all about strategy. It's about the future. It's about thinking about where you're going and what it's going to take to get there. And unfortunately, Zach, we spend a lot of time on the people side. We don't spend as much time as we should on leading self because you can't pour from an empty cup. You've heard that before. So, uh, leading self is about how we show up, but it's also about taking care of ourselves. Leading operations. We all have a long list of SOPs or processes that we want to get to. Redrafting and rewriting and documenting. But it often comes last. And then what comes last? Last. Even more last than operations.

Speaker B: Last of last is leading.

Speaker A: The last of the last is leading forward. Because who has time for strategy when you're spending all of your time as a firefighter or a person who's holding that whack a mole mallet. You know, the person who's just like smacking down on.

Speaker B: Just not in my law office.

Speaker A: Yeah, exactly, exactly, exactly. So that's it. Leading self, leading people, leading operations and leading forward. That's really what the book is about. Those four cornerstones, but very specifically what they mean inside of a law firm.

Speaker B: Got you. Okay. Because like you said, there's just some different, we all know there's just some different nuances. To operating inside a law firm. There's just different personalities. There's, you know, at the very least, different business structures, um, different requirements for our business structures. So, um, I love that. Leading self, leading people, leading operations, leading forward. Well, Debbie, we're also, over the next few weeks, we're going to have individual episodes for the podcast on those specific foundations, those specific, um, four cornerstones. So I look forward to hearing you and Stephanie talk about those here in a little bit. Uh, in the next couple of weeks. Um, people can find the book on Amazon as of two days ago. Just I don't know that we've even said the name of it. It's Be a Next Level Leader. Be a Next Level Leader. We'll drop the link to, um, the Amazon store in the show notes. But yeah, be a Next Level Leader with, uh, Debbie Foster and Stephanie Everett.

Speaker A: That URL is also live beanextlevelleleader.com and there, there are leadership resources, there's a leadership assessment. We actually have a foundations course that is a four week cohort that goes over all of the concepts in the book. Uh, Be a Next Level Leader. So it's a little bit more, uh, hands on experience. It has an amazing workbook which you can see more about on the URL if you go to beanextlevelleleader.com so much more to come on that I'm excited to record episodes too.

Speaker B: Yes. And I love that aspect of this that it's not just, um, A, it's not just here's a book. Good luck. Um, B, it's not just. Here's a workshop. Good luck. Um, it is, you know, we recognize that leaders take different shapes and they learn in different ways. Um, and so you can do all or some of those things as well. So. Yeah. Thank you, Debbie. It is beanextlevelleleader.com and I really hope to, really hope to see people go there. Well, Debbie, thank you. And I, I look forward to your, um, your episodes with Stephanie coming up. But now we've got my conversation with Carissa.

Speaker C: Hi, I'm Carissa Veliz. I'm an associate professor at the Institute for Ethics in AI at the University of Oxford. And I'm the author of Privacy is Power and Prophecy.

Speaker B: And just to be clear, Privacy is power. Separate book than prophecy. Privacy is Power is, uh, uh, 2022, something like that. You were writing that during, uh, the pandemic.

Speaker C: Yeah, that's a pandemic book. Um, and Prophecy has just been published this year.

Speaker B: Okay. I have started to read it. Um, Be honest, I haven't completely finished it yet, but I'm excited about it. It's a. It's a really good one. Um, Krista, thank you so much for being with me. To talk to our audience about really, we can't really say privacy writ large. We can't just say. But the idea of privacy, um, being power. And that's kind of the flip side of the coin of knowledge being powerful. Um, let's start there, though, with your first book of Privacy is Power. Talk to me about the idea of privacy being power.

Speaker C: Yeah. Thank you so much for having me, Zach. The idea that privacy is power comes, um, from the idea that knowledge is power. And that's a very old idea, and it's very intuitive, and it's very easy to understand. The more somebody knows about you, the more you are vulnerable to them. And that's why we don't give people our Social Security number if we don't trust them, or our credit card number or our address. Um, and that insight came from, among others, Francis Bacon, a long, long time ago. But after that, a philosopher like Michel Foucault also said that the converse is also true. The more power you have, the more knowledge you have. Because if you have power, you have the means to acquire knowledge, such as buying books, um, or going to university. But it's also the case that if you have enough power, you get to decide what counts as knowledge. So if you're a big tech company, you get to decide what categories people are put in. And all of this suggests that privacy is a kind of power, not only because the more companies know about you, the more they can influence your behavior, the more you are vulnerable to them. But it's also the case that, uh, they decide what counts as knowledge about you. And, um, you don't get to choose these categories. Often you don't even get to see them, and you can't even contest them.

Speaker B: The idea that knowledge is power and privacy is power, I don't know that we completely make the connection of having our information be private. Is our power our way to wield our own power? What do you mean by that? Um, in this book.

Speaker C: So part of what I mean, um, when I write about how when we give up our privacy, we give up our power, is partly just an individual matter of when you give out your information, you make yourself vulnerable to people impinging on your autonomy. And that's in a philosophical sense of autonomy, of your ability and right to lead the life that you want to have to have power over your own life to decide what values you endorse and to live according those values. And why is that? Well, for the very simple reason that when people know about you, um, eventually somebody tries to exploit that knowledge to their interest and not in your interest. Um, so, for example, we in liberal democracies, um, have the secret ballot to protect people from undue influence. Because if people know who you're going to vote for, they can, of course, try to either persuade you or seduce you into trying to vote for someone else, or in an extreme case, trying to, um, force you to vote for whoever they want you to vote for. So threaten you or coerce you in some ways. Um, and that very important principle of the secret ballot applies to most of life because democracy isn't only about voting. It's also about participating in your inner society and having an opinion not only about politicians, but about companies and products and where are we going and how should we do this? Um, and so it has this double edge of being an individual kind of power, but also the power of the citizenry. When we, as a citizenry give up our power, we make ourselves vulnerable to political manipulation in a way that is very corrosive.

Speaker B: And so, in a way, giving, uh, away my privacy, my information, my information about me that we get on our smartphones or we get through kind of interacting with the Internet is giving organizations, let's say, whether it's large corporations or governments or individuals or anything like that, it's giving them more fodder for being able to exercise power over me. Um, is kind of what we're saying,

Speaker C: exactly, to influence our behavior, but also to decide what counts as knowledge about us, what counts as the categories that apply to us, even when we might not be in agreement with those, or even when they might be actually inaccurate, because these are companies. The ultimate goal is not precision or accuracy. It's profit. And so as long as it's profitable, and usually if it's accurate enough, it's profitable, but it doesn't have to be super accurate. That's good enough, but it might be very unfair.

Speaker B: And so some ways it's the power to influence us, um, directly, which is the power to convince us to do something. I liked your definition of power, um, in the book Privacy is Power. It's basically the ability to get somebody else to do something, um, whether it's through influence or potentially force, um, and one kind of leading to the other. But it could be, you know, it's the power to influence us, but it's also then the power to kind of like secondarily influence our lives is what you're saying, by putting us into particular categories. If we think about, um, Equifax. And I like using Equifax as an example because I always want to keep reminding people that in the 70s, Equifax changed their name to Equifax in order to make them look better because they had data issues. And then in 2020, something, Equifax had one of the largest and most damaging breaches in the history of. I'm just going to kind of keep it there because I don't want to get sued for too much. But Equifax is not the greatest player as a. Um, I think you say data vulture. Um, I think other people would say a data collector. Um, but Equifax, in saying I have X score on my credit, is influencing my life, is affecting where my life can go. So that's. The other side, is being able to categorize you as straight, white male or likes baseball also.

Speaker C: Yeah, um, and those categories might seem quite innocuous, but when you actually look at the categories that these companies use, they're quite frightening. They include things like people who have lost a child, um, people who have suffered rape, um, people who suffer from all kinds of sexual, um, conditions. Uh, it gets really personal. They look at your vulnerabilities and exploit them. And, um, you're right. We see this pattern over and over again, and it just seems like we don't quite learn the lesson because collectively we have very short memories. But companies often have data scandals, and then they change their name, as if a change of name is anything other than a change of name. We saw this with Meta. Meta changed their name in a very crucial moment in which they were being attacked and criticized. Then they have, lo and behold, more data scandals. One of the things that concerns me is not only the very poor practices that we have in general regarding personal data, which is something that is very sensitive, and that it's in the Declaration of Human Rights for good reason. Because during the Second World War, people learned that personal data is sensitive because the Nazis used it to find people, but that even worse, that we're trusting our most sensitive data to companies that have proven themselves to be untrustworthy time and again.

Speaker B: Yeah, I want to think about, or at least, uh, ask, uh, you about this idea of, you know, we think of private data or personal data being my address, my sexuality, my gender, you know, things like that. But when we're talking about private data in this sense, and I really want to harp I really want to make sure that we make this point. We're not just talking about things that would be considered personally identifiable information or medical records. We're talking about the stuff that we create by interacting with the apps on our phone and our computer. Um, can you talk to me a little bit about some of that data being personal to us?

Speaker C: It's surprising the degree to which your data can identify you, and data you couldn't imagine might be sensitive can be sensitive. For example, it turns out that only you in the world has the kind of phone that you have and the exact apps that you have and uses those apps in the exact way that you do. That creates a pattern such that just by analyzing how fast your battery drains, you can be identified. Something that might seem like it's not interesting data, because it's not interesting to you can be very interesting to the right institution or the right company. Um, and part of the challenge is that sometimes we give up data that we intuitively think is not that sensitive, and we don't realize the inferences that are being made from that data. So, for example, you can share the kind of music you like, and that sounds pretty innocuous, but when companies use that to try to infer your sexual orientation, which you might have not wanted to share, then it gets tricky. That's why I wrote Prophecy, because I realized that the whole machinery of surveillance is at the service of a machinery of prediction. If we didn't want to predict, for example, people's sexual orientation or how they're going to vote or what they're going to buy, or where do they hurt in general, and how, uh, they're going to react, in some cases, um, we wouldn't be surveilling them. That's why it's so important to keep these two concepts, uh, in mind. And I think, you know, you haven't gotten to that point. But at the end of the book, I write about surveillance and prediction as the two original sins of digital tech. They were, these systems were designed to surveil and predict, which are two fundamental methods of social, uh, control.

Speaker B: Because, I mean, that's kind of the point of collecting this data. Why would we collect any of this data? Why would anybody care about what cell phone tower, my computer, my cell phone connects to at a particular point in time if they don't want to surveil me, if they don't want to. And then in surveillance, and I think that is an important point here in surveillance is obviously, kind of, again, two sides of the coin is prediction and Then influence. We want to predict what somebody's going to do, and then we want to influence what somebody's going to do.

Speaker C: Exactly. And going back to the case of companies that, for example, score people on financial matters, as you said, this is really important for someone's life. Your life can go a lot better or a lot worse depending on that score. And we don't know what data they use. We can't contest it. Not really. Um, one important legal concept that I want to talk about here, because this podcast is kind of the perfect place for it, is the idea of fiduciary duties. So already these are a very well known legal mechanism to protect citizens. And in cases of medicine, um, so of course, doctors and lawyers and financial advisors, we ask these people that they put the interest of their client first because there is a financial conflict of interest. So a doctor might want to perform surgery on you because they'll win a bonus. A lawyer might want to advise this or another because they have another client or because they have their own agenda. Or a financial advisor m might want you to invest in something very, very risky. Because if you lose money, you lose money. But if you earn money, they win a bonus. Right? And we don't allow this. We ask them to put your interest first. And it seems to me that the same should be true of personal data in general, because it's so sensitive, but especially with these kinds of scores and with these kinds of decisions that are so important to people's lives. And by the way, they're all based on prediction. It's not about facts. It's about the prediction about whether, for example, you will pay back a loan. And, um, one of the problems with prediction is that if I deny you an opportunity, say a loan on the basis of a fact, because, Zach, you don't have X amount of dollars in your bank account, and I tell you this is what you need to have, then either I'm right or I'm wrong. If I'm right, I'm right. And you know what to do to change my decision. And if I'm wrong, you can contest it. You can tell me, no, I actually do have this money and it's here and you can show me. But if I deny you an opportunity on the basis of a prediction, how are you going to argue against that? There's no way. Predictions are unverifiable. Unfalsifiable. And we are going against the very important tenant of due process in democracy. When we make decisions on the basis of predictions, which are in turn based

Speaker B: on Surveillance, Uh, and just to kind of push that even further, um, predictions have been unverifiable since the beginning of time. They have. I think that's one of the interesting. That's the point I'm at in your book right now is talking about the, the Oracle of Delphi and the history of predictions. But I want to talk about how predictions aren't the power that is prediction. The idea of prophecy, the reason that we use prophecy, the reason that we use predictions is not to generally, um, tell the future in a benevolent way. It is also used to influence the future here. Um, and so what you're saying, I guess, and maybe I'm getting pretty long winded on this one, but what you're saying is essentially that the people who have this data, the people who have the power to predict, we could, um, potentially put some sort of fiduciary duty on them, on these data brokers. Um, I do like to call them data vultures. We could put some sort of fiduciary duty on them, as we do lawyers and doctors and financial advisors.

Speaker C: Yeah, essentially, if an institution wants to use my data, um, first of all, I should consent and I should have knowledge of exactly what data they're using and for what and how. Um, but secondly, they should only be allowed to use that data for my benefit, um, because otherwise it's just too easy to use it against my interests. And it's bad for democracy because it jeopardizes democracy in the same way that buying or selling votes would. So the two measures I think that are most important is to ban the trade in personal data because it shouldn't be the kind of thing that you can buy or sell for the same reason that we don't buy or sell votes or organs or many other things or people for that matter. And, um, um, whenever that personal data has to be used, for example, in medicine, that it only be used for the benefit of the patient. If you're not going to use it for the benefit of the patient, then that's a problem. Um, then there's a further issue of prediction, because even if you have those, there's still an open debate to be had about what kinds of predictions are acceptable and what kinds of predictions are unacceptable. And it's incredible to me that we're not having that debate, that we've been using predictions since the Oracle of Delphi to make the most important decisions of our life. And there are thousands of books about how to predict and there are academic journals and there's not one book on the ethics of prediction? Are you kidding me? It's kind of incredible. But in history you see these blind spots that we have. So um, researching this book was fascinating and one of the things that blew my mind was that it took us hundreds if not more years, um, to realize that when we play dice, seven is the most common number. Just nobody had noticed. And sometimes that happens. We have blind spots. But yeah, as you were saying, we tend to associate predictions with knowledge and they have very good PR predictions. But actually when you analyze predictions you realize that they're never facts. A prediction can be a lot of things. In the best case scenario they're an educated guess. But more often than not they're power plays in disguise or wishful thinking or marketing. But whatever they are, they are never a fact. And that is crucial to understand because democracy in some areas depends on facts. And we should be rely on facts like in the justice system and in contexts in which fairness is paramount. And in those contexts statistics is just not good enough.

Speaker B: I think one of the things that struck me so far again in the prophecy book is this idea that we didn't have statistics at some point. You know, this idea that if, if a, if the gods were making everything work in um, in the way that they want it, it's either going to happen or it's not. And so all we could do was read into prophecy. And that kind of takes the wind out of statistics in a way. It kind of takes that divineness to statistics, um, out of it. Which I thought was fascinating.

Speaker C: Yeah. And later on, even when we develop statistics, for a long, long time, the most knowledgeable people in society tended to think that statistics was just the second best. It's what you use when you actually don't know what you should know. And eventually statistics became an explanation. Before it was never an explanation, it was a suggestion. So it was a correlation. And then we had to look for causes and now we've just become in some ways lazy and sloppy. We just stop at correlation and don't go further. And um, in some cases that's good enough when it comes to marketing, but in cases of justice, that is not good enough. And I don't think society has reflected on the implications of the overuse of

Speaker B: prediction for democracy, which I think is really relevant now because of artificial intelligence, because of the fact that artificial. When you wrote privacy is power, we were talking about um, Alpha 0, we're talking about AlphaGo. We were talking about the ability to feign intuition in certain scenarios, provided we created the algorithm, um, very specific. So Winning at go. Okay, but now we're using Claude Cowork. Now we're using AI for facial recognition all over the place when people go to protests. And so I think this has become more important to have this conversation, uh, at this time. And I think your books are even more pressing, or what you talk about in these books is even more pressing, just because of the heightening advancement of what we can do with this data and with these prophecies.

Speaker C: Yeah, that's absolutely right. And, um, on the one hand, you might think that privacy is power is outdated because it was written before ChatGPT, but actually, sadly, it's not, because ChatGPT, for all its disruption, is just more of the same. It's a different scale, but it was trained on vast amounts of data. Huh. Much of which was arguably acquired in unlawful ways and certainly questionable ways.

Speaker B: Certainly questionable.

Speaker C: And it is great as a tool for surveillance because you might think that ChatGPT is giving you information, but actually you're giving it much more information than you can imagine. And it's profiling you, and it's inferring things from you, from how you use language, and at what times do you connect and what are you asking? And so, in a way, nothing has changed, and it's just more of the same. But what makes large language models very dangerous is that, uh, they just make it that much easier to sift through large quantities of data and surveil people. But it's the same pattern, and I make this point in Privacy as Power, that if you go back to East Germany, about between a third and a fourth of the population were being surveilled. But it was very effortful to surveil someone. You actually had to hire them to follow them or to listen to their conversations. And today it's become so cheap that it's almost cheaper to surveil by default than not to.

Speaker B: Yes, yes. And that's frustrating. So frustrating is, um, too light of a word for that, frankly. Um, when I was about to do this podcast, my wife asked me what my favorite part of privacy is power, um, is. And I told her hope. Getting into this book, reading it, it scares the shit out of you. And it should. Um, it really does, because it does a very good job of detailing how minuscule data about us can be and still be important. But I want to talk about the hope. Um, talk to me a little bit about how we can push back against this and ways that we could potentially take our power back as individuals, um, with our own privacy and our data.

Speaker C: Yeah. There's a lot we can do, and we tend to forget that these companies depend on our cooperation for their thriving, that if we stop cooperating, they really don't have much to do about it. And the first thing to note is that there are alternatives. Instead of using Google search, use DuckDuckGo or StartPage or there are others. Uh, instead of using WhatsApp, use Signal. Instead of using, um, the usual suspects for email, use protonmail. And full disclosure, I'm part of the board of the Proton foundation, which is the majority stockholder of Proton, the company. But I'm there because I believe in it and I'm not paid for it.

Speaker B: To add to that, I have my email is Proton.

Speaker C: Oh, fantastic. Yeah. Use a vpn and more generally, just try to look for opportunities. And remember that everything that matters is analog and that if you want to have a really good conversation, then drop the phone and keep the microphones and cameras off. If you want to have a really good party, ask your friends not to take photos or upload them. It makes a difference to have spheres and spaces in which we are free from surveillance. If you like Hope in Privacy is Power, you're going to love prophecy, because there's a lot more of that in there, because privacy is power. I wrote at a time when people were still excited about technology, and the book was like, hey, but have you thought about this? There's a really dark side of this that, uh, I don't think we're seeing. And this book is the opposite in a way. I wrote it in a moment in which there's a lot of doom and gloom and people are afraid of AI for good reason. And it's partly a book to remind us that there is still a lot of democracy to recover and to cherish and to take care of and to grow. And that, uh, it isn't a coincidence. I think that democracy is faltering at the same time as the rise of digital tech, but that, uh, most of what matters is analog, and the analog world is still there. The trees outside your window and your favorite bar and your favorite coffee shop and the people you love and the roof that is protecting you from the rain and the natural world and everything in between, all of these things are analog. And, um, we should do much more to value, but also to take care of that world.

Speaker B: I love that. But one thing I do want to highlight here that I got out of your book as well, uh, out of privacy as power, specifically, is that when I just being very candid here, when we talk about Things that are bad in the world we talk about. And in your book, you talked about CFCs, uh, and the ozone, uh, deteriorating, things like that. A lot of times we have to take giant steps that are extremely uncomfortable to right the ship. And it would be really easy to think that I have to go get a flip phone and I have to completely unplug in order to protect my data. But that's not really what you're saying. You've said there are other ways to protect your data because these companies don't need your data in order to survive, they just want it.

Speaker C: Yeah. So it depends. I mean, there are companies that do need your data in order to survive, and we should not give it to them because then they will figure out a way to survive that is with a less toxic business model.

Speaker B: Right, but.

Speaker C: But there are the function that you're looking for, whether it's email or search.

Speaker B: Right. Thank you. Yes.

Speaker C: Had without surveillance. And that's the important thing. And you're right. You don't have to be perfect and you won't be perfect, and that's fine. But this idea that if you're not perfect, then it's not worth making an effort is a bit like saying, like, well, you know, I know sugar is bad, but there's no way I'm going to quit sugar, so I'm just going to eat buckets of sugar from the moment I wake up to the moment I go to sleep. There's a difference. The more sugar you eat, the more you risk your health. And in the same way, you don't have to be perfect, but every time you choose privacy, it matters. It matters because you send the right signal. It matters because companies and governments are much more sensitive than you might imagine to what people want and what they signal. And Even when just 5 to 10% of the population change their pattern slightly, it gets noticed. And it matters because you never know which data point is the one that's going to lead to identity theft or to a different kind of bad experience. Um, and the more you protect your privacy, the more your chances that you'll

Speaker B: be safe and the more we protect other people's privacy as well.

Speaker C: That's right. And also, it depends on who you are. So I'm an author, and, um, I wish I lived in the times when an author could get away with only writing. But today, if I want my book read, I need to share more than I'm comfortable with.

Speaker B: You have to do this type of thing and get on TikTok.

Speaker C: Exactly. Um, but if you're I don't know, an accountant or something. Or whatever. If you have a career in which you don't need this kind of public exposure, then you might have the luxury of being a lot more private. Or if you're a journalist and you are researching dangerous topics in dangerous countries, then it's a different kind of setting and you better protect your privacy for your own safety and the safety of your sources. So it depends a lot on context and who you are.

Speaker B: Well, um, I could, as you can imagine, talk to you about both of these books forever. I would sit down and pick your brain for a very, very long time. But, uh, unfortunately I can't do that. Um, if listeners remember one thing from this conversation though, about their privacy, power and prediction, what would it be? What would you like to leave them with?

Speaker C: So let me leave them with a couple of ideas for privacy. That privacy matters because it protects us from possible abuses of power. And as long as people are people and institutions are institutions, there will always be the temptation to abuse power. So it's a matter of power for the citizenry to be strong. In democracy, we need privacy. And for prediction, this idea that predictions are never facts and more often than not they're power plays in disguise. M. Uh, but they only work if we believe the prediction. A prediction can only become a self fulfilling prophecy if we buy it. If we don't buy it, it has no effect. So if Oedipus hadn't freaked out about the prophecy that he would marry his mother and murder his father and try to avoid it, he would have never made it come true. So next time you hear a prophet, one of these tech executives telling you that the future is going to be so and so, just pause for a second, realize that it's a prediction, it's not a fact, and say, no, I don't think so. That might be your future, but it's not going to be mine. Because this is what I'm going to do to make sure that that's not the future of my society and my community. We have the power to write our own future because the future is unwritten.

Speaker B: I love that. Let's leave it there. Um, Carissa, thank you so much for being with me and for talking to me about these things. I think it's very important. I know we're talking about priv, um, but where can people find you, um, in order to connect on like LinkedIn and places like that?

Speaker C: The most important place is a bookshop. Um, because when you buy a paper book, there's no surveillance. Nobody knows what you're reading, nobody knows how fast you're reading. They're not collecting your data. And it's just you and me having a conversation, human to human in the world of ideas. But failing that, um, BlueSky, LinkedIn. And as an author, I also want to thank readers who read books and who reach out and who, you know, tell you a story about their lives or, um, their opinion about books, but also who leave reviews. It's kind of an irony, but there's no other way to beat the algorithm. So if you like a book, please support authors and leave a review. Talk to your friends about the book. Give it as a gift, uh, because it's partly how we build democracy together. Democracy is a conversation.

Speaker B: I love that. Democracy is a conversation. That's fantastic. Well, Carissa, thank you so much for being with me. I really appreciate it. Thank you so much, Zachary.