Is AI harvesting your knowledge on the cheap?

Summary

The Interface (BBC)

Episode: Is AI harvesting your knowledge on the cheap?
Date: May 14, 2026
Hosts: Tom Germain, Karen Hao, Nicky Woolf

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Episode Overview

This episode of The Interface explores pressing and sometimes overlooked intersections between technology and everyday life, focusing on three major stories:

1. The rise of highly educated “data workers” who train AI, often in precarious conditions.
2. How viral fear and misinformation still threaten public health, using the recent Hantavirus cruise ship incident as a lens.
3. The alarming scale of personal data collection by modern cars, and where that data is being sold.

The hosts—Tom Germain, Karen Hao, and Nicky Woolf—combine investigative reporting, sharp debate, and dry wit to expose the hidden costs of rapid technological advancement.

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1. AI’s Secret Labor Force: Is Your Expertise Being Weaponized? (02:46–16:40)

Key Discussion Points

• Karen Hao’s Investigation:
  Karen reveals findings from her new exposé on the dual impact of AI on labor: sophisticated “data work” is now among the fastest-growing jobs in the U.S., filled by people with advanced degrees who can’t find traditional work.

  “…these are extremely highly educated individuals. They are people with college degrees, master's degrees, Ph.D. degrees, law degrees… now turning to data work as another job. And part of the reason… is AI slowing down hiring, accelerating layoffs.”
  — Karen Hao (04:24)

• The Gigification of Expert Work:
  Companies use AI as a reason to freeze hiring and lay off staff, then rehire those same people (and others like them) as precarious, gig-based data annotators. Work arrives without warning and vanishes as quickly.

  “An email will just arrive in her inbox: ‘A project is available for whatever—$35 an hour or something—do you want it?’ …When she picked up her first project… [it] lasted for two weeks and then immediately, like overnight, disappeared.”
  — Karen Hao (07:09)

• What Data Workers Actually Do:
  These PhDs and domain experts teach AI models to imitate advanced knowledge, training LLMs to “sound” like PhDs, but without substance.

  “They're training the models to have the trappings of PhD level [knowledge]… giving it PhD vibes.”
  — Karen Hao/Nicky Woolf (09:17–09:24)

• A Vicious Economic Cycle:
  Reduced hiring and mass layoffs caused by AI drive experts into gig data work, which in turn makes AI more competitive and automates yet more work.

  “It's not just the state of the economy for any reason, but specifically because of AI that the state of the economy is this way…”
  — Karen Hao (10:03)

• Long-Term Societal Implications:
  If this trend continues, it may undermine higher education and social mobility, with a tiny elite employing vast numbers in precarious roles.

  “If Silicon Valley… successfully ‘uberizes’ most knowledge work, then the kind of inequality we would experience is something we have never seen before… fundamentally breaking the social contract.”
  — Karen Hao paraphrasing Prof. Daron Acemoglu (13:12)

Solutions & Hope

• Strengthening Worker Rights and Unions:
  Labor organizers advocate collective bargaining for data workers and stronger rights across the tech supply chain.

  “A key way to govern [AI] development… is actually by strengthening worker rights across the economy in general, strengthening the ability of workers to unionize…”
  — Karen Hao (14:20)

• Legislative Action:
  Discussion of California's proposed “Sweat Free AI Procurement Act,” which would require state agencies to use AI systems not built on exploitative labor.

  “…it would require the State of California… to ensure that [AI used in government work] was not built on exploitative labor conditions.”
  — Karen Hao (16:30)

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2. Viral Fear Machines: The Hantavirus Case & Social Media Outbreaks (18:43–32:09)

Key Discussion Points

• The Hantavirus Cruise Outbreak:
  A deadly but unlikely-to-spread virus became a viral misinformation bomb, sparking international panic and conspiracy theories reminiscent of the early COVID-19 era.

  “[This] triggered… everyone’s absolute terror response. All scientists have agreed that it’s very unlikely this turns into… a global pandemic… but it reignited all of these beliefs that people can project onto it.”
  — Nicky Woolf (19:16–21:18)

• Echoes of COVID-19:
  The hosts warn that the information ecosystem is “damaged” from previous pandemics, primed for mistrust and instant amplification of fear-mongering narratives.

  “…millions will encounter [the next outbreak] inside an information environment already primed to distrust public health guidance before it even arrives.”
  — Nicky Woolf quoting epidemiologist Katrine Wallace (21:58)

• Why Conspiracies Go Viral:
  Social media platforms monetize engagement, which means anger and fear-spreading content is algorithmically promoted—regardless of whether the messenger is malicious or merely anxious.

  “Content that gets the most engagement is the most monetizable. And the kind… that gets the most engagement is fear and anger.”
  — Nicky Woolf (22:58)

• Filter Bubbles & Individualized Realities:
  Each user inhabits a unique algorithmic “bubble,” making it nearly impossible to address echo chambers from the outside.

  “People are living in these self-contained information worlds.”
  — Nicky Woolf (27:00)

• Failed Solutions:
  After a brief, heavily criticized experiment during COVID-19, social platforms have largely abandoned fact-checking efforts, leaving the core profit-drivers (algorithmic amplification) untouched.

  “They really do profit in many ways off of the misinformation ecosystem… all of the efforts … fact checking things… was already not actually addressing the root issue, which is the platform design.”
  — Karen Hao (31:03)

Quotes & Notable Moments

• “How do you convince people when in a lot of other ways governments have really not earned their trust? … It’s an information ecosystem problem and it’s a governance problem and it’s a media problem.”
  — Nicky Woolf (27:28)

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3. Your Car Is Spying on You (32:30–46:01)

Key Discussion Points

• Modern Cars as Surveillance Devices:
  Today’s vehicles collect vast volumes of data about your driving, your location, who you know, what you listen to, and even your biometrics.

  “The modern car—this is not an original line—is a smartphone on wheels… there’s sensors everywhere… everything is an opportunity for a data point to be created.”
  — Tom Germain (33:13)

• Disturbing Details in Privacy Policies:
  Some automakers’ privacy statements bizarrely specify the collection of sensitive information like “sex life”—even if claimed to be only ‘for legal compliance.’

  “There was a little note that the car might be collecting information about your sex life… but they did not tell me what kinds of sensitive information they do collect.”
  — Tom Germain (36:34)

• Who Buys This Data?
  The primary market right now is the insurance industry (especially car insurance), with data also funneled to data brokers and potentially law enforcement.

  “Insurance companies… are using it to raise prices for people… Car companies are selling your information to the data broker industry. And… anybody who wants it can get it.”
  — Tom Germain (37:42, 39:43)

• No Easy Opt-Out, No Real Fixes Yet:
  There’s little legal protection or consumer choice—if you drive a recent-model, connected car, your only sure way out is not to use infotainment or connected features. Existing privacy settings are difficult to navigate and not universally available.

  “…there aren't any rules requiring these companies to let you stop this data collection… the sad truth is there isn't a ton you can do about it.”
  — Tom Germain (44:17)

• Future Privacy Risks—Biometric Scanners:
  An upcoming US law will require drunk-driving prevention tech (biometrics) in all new cars—but lawmakers did not specify what happens to this highly sensitive data, multiplying future privacy risks.

  “They did not put any provisions about what happens to this information… car companies could be forced to collect medical information… with no restrictions…”
  — Tom Germain (42:05)

Quotes & Memorable Moments

• “That used to be the place to do it, right? You want some privacy, you go get in your car, have that sensitive conversation. And now, I don’t know.”
  — Tom Germain (45:48)

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Flow, Tone, and Final Thoughts

This episode weaves together exposés on the hidden labor behind AI, the social machinery of viral conspiracies, and the quietly escalating war on privacy inside your car. The hosts’ style is both rigorous and irreverent, with moments of gallows humor (“They're giving it PhD vibes.” “So they're making them more annoying.”) amid sobering takeaways around labor exploitation, algorithmic manipulation, and the steady encroachment of surveillance capitalism.

Whether you’re worried about future job security, online misinformation, or what your car knows about you, the episode calls for vigilance, critical thinking, and political engagement—with special emphasis on reading privacy policies, supporting collective action, and resisting algorithmic tunnel vision.

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Timestamps for Key Segments

• AI Labor & Data Work: 02:46–16:40
• Hantavirus Outbreak & Misinformation: 18:43–32:09
• Car Data Privacy: 32:30–46:01

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Outstanding Quotes

• “If Silicon Valley… uberizes most knowledge work… that would fundamentally break the social contract.”
  — Karen Hao summarizing Daron Acemoglu (13:12)

• “People are living in these self-contained information worlds.”
  — Nicky Woolf (27:00)

• “There was a little note that the car might be collecting information about your sex life.”
  — Tom Germain (36:34)

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For further details, visit BBC.com, search for The Interface podcast, or check the show notes for references and contact information.

Transcript

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[Thomas Germain]

In some privacy policies. There was a little note that the car might be collecting information about your sex life.

[Karen Howe]

What?

[Nikki Wolf]

Welcome to the Interface, the show that decodes how tech is rewiring your week and your world. I'm Nikki Wolf.

[Karen Howe]

I'm Karen Howe.

[Thomas Germain]

And I'm Thomas Germain.

[Karen Howe]

Today on the Interface, the frightening reality behind the AI industry's race to consume the world's knowledge.

[Nikki Wolf]

Hantavirus starts up the viral fear machine

[Thomas Germain]

again and your car's spying on you and it's so much worse than you think.

[Karen Howe]

So, Nikki, Tom, you know we've talked before about the hidden world of human labor behind AI.

[Thomas Germain]

Sounds familiar.

[Karen Howe]

We've also talked before about the impact of AI on work. Well, this week I wanted to tell you about an investigation I published with More Perfect Union, which sits at the intersection of these two trends. And it kind of really freaked me out. So AI is coming for your job, but not exactly in the way that you think. So don't listen to what other people say, listen to what I'm about to say.

[Thomas Germain]

Listen to Karen. Yeah, I've been saying this for years.

[Karen Howe]

So I have been covering the hidden world of human labor behind AI for, for years, for around eight years now. And generally speaking, I go to global majority countries to meet with workers in really impoverished contexts. Communities who are being hoovered up by the AI industry to train their AI models. So this kind of work is content moderation. It's cleaning the data that they use feed into their models. It's also annotating the data. And this category of work is generally called data work. It is literally the work that the AI industry needs to make their technologies exist. The shocking thing that's happening now is that this kind of work has become the number four fastest growing job in the US according to a LinkedIn report from earlier this year in.

[Nikki Wolf]

In the domestic US as well as

[Karen Howe]

in the domestic US yes, like I'm talking about US based people being hired up as data workers. Now these are not any kind of workers. These are extremely highly educated individuals. They are people with college degrees, master's degrees, Ph.D. degrees, law degrees, and also people who have been working within different industries as scientists or lawyers or doctors even for years that are now turning to data work as another job. And part of the reason why this is happening, this is coming in the context of AI also being a reason why the labor market has become a lot worse for people. It is slowing down hiring, it is accelerating layoffs across different companies. So whereas the AI industry tells you that AI is coming for all work and no one will have work anymore. And this is basically just another way of hyping up their technologies to say our models are capable of all work. What's actually happening is this vicious cycle where yes, companies are using AI to justify layoffs, to justify not hiring workers. And those workers who don't have the full time employment opportunities are now getting fed back into the AI industry to work for the industry itself as data workers. So it's not taking away your job totally. The industry is actually inshidifying your job and turning it into a gig work version so that they can take and consume your expertise and your knowledge.

[Nikki Wolf]

And are these long term jobs or is this literally they do that job until they have trained the AI in what it can do and then that job is gone?

[Karen Howe]

So yeah, this is a kind of a complicated question because no, they're not long term jobs in the sense that these are contract based gigs.

[Nikki Wolf]

Yeah, there's no workers rights, these aren't pensions.

[Karen Howe]

Yeah, exactly. It's not short term because the job goes away, it's short term because of the nature of how the industry is structuring the data work. And yeah, these are incredibly awful jobs to work. I interviewed a bunch of data workers for this story and one woman was an Ivy League PhD graduate who, who then literally applied to 200 jobs and didn't get any of them, so ended up in data work. And she described this extremely piecemeal anxious process where she's drip fed this data work and an email will just arrive in her inbox and be like, a project is available for whatever $35 an hour or something like that, do you want it? And they'll have all these terms like, oh, it's going to be for potentially a four, a four week long project, or this is how hours you should be able to work, so on and so forth. But that never actually happens. When she picked up her first project, it started and basically lasted for two weeks and then immediately like overnight disappeared. And she was at her university about to attend her graduation when the work just poof, went into smoke and she thought to herself, should I even celebrate my graduation anymore? And like get this nice dinner because she couldn't afford it. And this is kind of how this work happens is there. You never know when it's going to arrive, you never know when it's going to go away. And it kind of pits different workers against each other because the people who claim the work fastest are the ones that actually get the opportunity.

[Nikki Wolf]

What exactly are these PhDs doing? What knowledge are they?

[Thomas Germain]

What's the work?

[Nikki Wolf]

What's the work here?

[Karen Howe]

When you think about everything that an AI model can do, companies want you to believe that they magically learned it themselves. But actually there are humans involved in teaching literally every single possible task. And so when you think about the fact that ChatGPT can chat, it's because there were people that showed these models examples of dialogue, Person A talks, person B responds, person A talks again, and so on and so forth. And as companies have gone from simply just getting their models to chat about anything to these more sophisticated tasks where they're trying to sell this idea that their models are going to arrive at PhD level intelligence, that is part of the reason why they're phrase you always hear, yeah, yeah, that's why they're now motivated to hire PhD level workers to try and teach this knowledge to the

[Nikki Wolf]

Models, So they're training it to sound like PhDs. They're not giving it an actual PhD.

[Karen Howe]

Like they're, they're training the models to have the trappings of PhD level.

[Nikki Wolf]

Yeah, they're giving it PhD vibes.

[Thomas Germain]

So they're making them more annoying.

[Karen Howe]

Yeah.

[Thomas Germain]

So, because just, just to make sure I'm getting this right, like, because the state of the economy has made it harder to get these jobs that require specialized knowledge. People are out of work and then there's a new kind of work.

[Nikki Wolf]

It.

[Thomas Germain]

You have to be an expert to get it. But it's low paying, it's unreliable, you never know when it's going to come. It disappears overnight. And it's like this new class of, you know, Uber eats style workers. Except they have PhDs.

[Karen Howe]

Yeah, exactly. And, and it's not just the state of economy for any reason, but specifically because of AI that the state of the economy is this way. So in corporate investor calls, CEOs are talking about right now. The reason why they're accelerating layoffs and slowing down hiring is because they are experimenting with AI. This goes back to a previous episode where we were talking about how Meta is laying off some of their employees as they're also really pushing to introduce more and more AI into the workforce and how they were making this future bet essentially that the technology is going to ultimately work out and going to allow them to not have to rehire these workers back. And a lot of other companies are making the same bet. It's not necessarily that they are literally seeing dividends with the adoption of AI right now, but they're hoping that these models will improve to the point that, that they don't have to recall these workers back and they can, they can shrink the workforce in the present day to gain some of those financial efficiencies. And so this is the moment that we're in where it is the very industry that is causing this cascading effect on the economy that is then getting to profit off of the creation of.

[Thomas Germain]

Because these people are available for this kind of work.

[Karen Howe]

Yeah. Yeah.

[Thomas Germain]

Interesting. Yeah.

[Karen Howe]

You would think that this vicious cycle is going to lead to an end state where all of these workers are laid off from full time jobs. They then train these models to have all of their expertise and then they just all basically, by the end there's. There's no work left for humans. But the workers were telling me that the way that the data work is organized is so chaotic and so poor that the AI models are actually not very effectively capturing their expertise they, for example, you know, I Talked with one PhD graduate who, he was mentioning that he has a PhD in philosophy, but then he was overseeing this team of other PhDs in math, chemistry, biology, whatever, and none of them were actually evaluating tasks within their domain of expertise like he was. He was being asked as a philosophy PhD to evaluate math stuff, whether or

[Thomas Germain]

not these jobs are ultimately replaced. Right. Like, maybe it's difficult for people with PhD level education to get a job right now. The fact that those people who've dedicated so much time with the expectation that they were going to achieve high incomes, I mean, depending on what the field is. Right. Like those people are taken out of the labor force and, you know, the economy is built around, you know, different classes of people making different amounts of money. If there's suddenly this shift, you know, even if it is temporary there, the effects could be pretty long term.

[Karen Howe]

Yeah. I think this really hits on a central point. I was interviewing MIT Institute professor Darren Acemoglu, who won the Nobel Prize for economics in 20, and he was saying that if Silicon Valley gets what it wants and successfully uberizes most knowledge work, then the kind of inequality that we would experience is something that we would have never seen before, where most workers are sidelined for meaningful work and only a few companies actually employ most workers as well. And this would, I think, fundamentally break the social contract. Yeah. Why would anyone then pay tons of money to get training in College, in a PhD program in a law degree, when this is what is awaiting for them on the other side? So it would absolutely lead many more people to reconsider whether they should be getting higher education at all. And that in and of itself, whether this moment is temporary or longer term would then have further consequences down the line.

[Nikki Wolf]

So is there any kind of solution here?

[Karen Howe]

So I interviewed this guy named Tim Newman, who heads labor programs at Policy Org Policy nonprofit called Tech Equity. And one of the things that he points out is that when you think about the full AI development supply chain, workers are a crucial part of every single stage of it. Not just data work. Also, you know, the people mining minerals, the people designing computer chips. What he was pointing out is that a key way to govern the development of AI is actually by strengthening worker rights across the economy in general, strengthening the ability of workers to unionize, to be able to hold their companies and their industry accountable through collective action. And specifically, when it comes to data workers, they don't actually have these formal unionization efforts. There are actually efforts on the way in process. I talked with this other researcher named Crystal Kaufman, who was formerly a data worker herself, and she has been for years actually now organizing data workers to form these more collective groups that can engage in this kind of action. And and finally there is when it comes to just improving the conditions of the data workers themselves, there is this new interesting bill that has been introduced in California that Tim told me about called the Sweat Free AI Procurement act, which borrows from the fashion industry, which also had a lot of labor exploitation in that industry, and then used different mechanisms by which to shore it up. And what this bill does is it would require the State of California to, whenever they use any kind of AI model within government work to ensure that it was not built on exploitative labor conditions. And this is a model that could potentially be propagated to institutions, to universities, to other forms of government as a way to create a minimum labor standard across the AI industry.

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[Nikki Wolf]

all right, so coming from that story, Karen, I have a story that is going to sound also kind of like a downer, but actually has a hopeful note that we'll get to.

[Thomas Germain]

So it's okay.

[Nikki Wolf]

So it's okay.

[Thomas Germain]

Okay, good.

[Nikki Wolf]

The whole time go into this story within your mind. Everything is probably going to be all right.

[Thomas Germain]

Okay.

[Nikki Wolf]

Have you been following the story of this cruise ship virus outbreak?

[Thomas Germain]

Hantavirus, it's called, Right? Am I saying that right? Hantavirus.

[Nikki Wolf]

Hantavirus. On a luxury cruise exploration ship traveling from Argentina to Cape Verde, they've had an outbreak of a virus called hantavirus, which is, it's a. It's a deeply unpleasant virus. It's got a mortality rate higher than 30%. I think it's around 35, 40%, which is horrendous, unbelievably dangerous. It's a virus that originally comes from rats. Is it hugely dangerous that people exposed to it? But in terms of the kind of global pandemic that obviously this has triggered, everyone's absolute terror response. It does not spread in the same easy way that the coronavirus did. All scientists have agreed that basically it's very unlikely that this turns into and very unlikely, extremely unlikely. There are some factors of this virus that make it not the kind of virus that turns into a global pandemic. Right. So the situation on the ship is very serious. Three people have died. But then this spun out. All of these viral hoax Covid 26 people were calling it another plandemic, people were calling it Chinese bioweapon, people were calling it. It reignited all of these beliefs that people can project onto it. And one of the things that we saw during the coronavirus pandemic was this backlash against guidance of trusted experts. If there is another global pandemic in the style of the coronavirus, what will make it disastrous is this social media content that turns it into a conspiracy theory and that makes people refuse to get vaccinated, that makes people refuse to follow guidelines, that makes people refuse to wear masks, that makes people refuse to self isolate. The information ecosystem is damaged. And this hantavirus outbreak is showing us that it is still damaged in a way that we really need to address. Because when the next actual danger happens and it might not be a pandemic, something else we have A real information ecosystem problem. I want to read you this quote from Katrine Wallace, epidemiologist. Uh, so when a future outbreak with real pandemic potential eventually emerges and one will, millions of people will encounter it inside an information environment already primed to distrust public health guidance before it even arrives. The narratives are pre written now. The audience already knows the cues. That I think perfectly sums up what the problem is here.

[Thomas Germain]

So it's like we're getting a trial run almost right there. This virus breaks out on a boat. It's very scary. It kind of sounds like the kind of thing that could become another global pandemic, but it's not. That isn't going to happen. People are misinterpreting it. And yet because it sort of sounds that way, people jump straight to, oh, they're lying to us, they're making up an imaginary virus. And pretty soon it will be another Covid and we'll all get locked down under false pretenses. Not going to happen here because this virus, it is real, but it's not going to spread that way. But once we get there, the gears are already ready to start turning that this is fake, it's not happening.

[Nikki Wolf]

And here's what's going on here. Here's the reason those gears exist is that content that gets the most engagement is the most monetizable. And the kind of content that gets the most engagement is fear and anger. Right. They don't want you to know. This is one of the most powerful kind of intros to Internet content. Right?

[Thomas Germain]

Yeah. It's funny. They're trying to scare you is what we're hearing, when in fact the opposite is true. Experts are like, no, no, no, you shouldn't be worried about this. It's not going to be a thing.

[Nikki Wolf]

And people are doing, you end up with kind of ivermectin again, which was this kind of nonsense thing that happened at the beginning of the coronavirus that was a sort of snake oil to cure coronavirus. People are selling things based on this fear because again, if you're afraid, you'll buy stuff. It's a very powerful capitalist imperative.

[Karen Howe]

So you had alluded to earlier this idea that part of the reason why we are seeing this frenzy is a bit of a trauma response. There are people who are genuinely afraid that this is in fact going to be the next pandemic, falsely so. And also you mentioned that there are people who are just trying to profit off of this fear. So what? When talking about this machine that spins up, like how much of that machine is people actually just trying to manipulate information and cash out. And how much of it is average people that are genuinely scared and inadvertently amplifying the conspiracy?

[Thomas Germain]

It's probably both.

[Nikki Wolf]

Yeah, it's, it's definitely both. I think the number of people who are like rubbing their hands with glee and saying, oh, this is another thing that we can make money off, people who are afraid is less than the number of people who have big accounts and are afraid and are making content that just through the process of the platform are monetized. Right. The monetization of content on these kind of platforms is quasi automatic. I don't know Tom, if you can speak to exactly how the kind of monetization works.

[Thomas Germain]

Yeah, I mean you get big enough on certain social media platforms, essentially they just start paying you. Right. If you have a certain number of followers, you're getting a certain kind of engagement. The rules differ depending on what platform we're talking about. But like the incentive structure, whether you're lying and trying to make people upset is the same. Right. You want to make a piece of content that is sensational, that gets a lot of attention. If you're very worried about something sincerely, you still want it to be sensational and scary and attention grabbing. If you're trying to trick people, you do the same thing. The algorithm treats it the same way. And because these systems are in place, this kind of conspiratorial mindset is ready to spread at any given time because that's what the system is designed to do, is to spread this sort of idea.

[Nikki Wolf]

It's really difficult to track because we're now in a short form, video, algorithmic dominated system.

[Thomas Germain]

Yeah.

[Nikki Wolf]

So you have to be within these kind of ecosystems in order to see some of this content.

[Thomas Germain]

So it's like another filter bubble problem that probably, I would assume that makes this harder to address. Right?

[Nikki Wolf]

Yeah, Filter bubble is exactly the right term. We're, we're talking about everyone's social media and information ecosystem experience is unique to them and kind of impossible to see into from outside, like for, for a little while. I, as a reporter, I don't know if you guys have tried this as well, but made specific accounts with specific interests just to try and see what kind of content gets fed to you. And it is completely different from people are living in these self contained information worlds.

[Thomas Germain]

So what could you possibly do about this? Right. You've got this community of people who are like the thing that they distrust is expertise. Right. That like they don't like a bunch of quote, unquote experts speaking down to us, telling us what we have to do and what we have to believe. How do you address this?

[Nikki Wolf]

I mean, this is, this is a colossal problem. And it's not just an outbreak like this where we're seeing what kind of problem this is. We were seeing massive resurgence of measles, for example, in a lot of places where it had been essentially eradicated because people are refusing to get vaccines, refusing to get their children vaccinated. This is a ginormous problem for healthcare because if people don't trust the very concept of vaccines, that by definition is rolling back, you know, a century of extremely brilliant kind of medical advances. How do you convince people when in a lot of other ways governments have really not earned their trust? It's the incentives online that make this kind of fear and conspiracy, fear mongering information spreadable in this kind of way. But they're not based on absolutely nothing. It's an information ecosystem problem and it's a governance problem and it's a media problem. There's all of these things happening at

[Thomas Germain]

once and they all feed into each

[Nikki Wolf]

other, and they all feed into one

[Thomas Germain]

makes the other a bigger problem. The people are primed to feel this way, right? And then social media is ready to pick that up and elevate it and make it bigger and spread it to more and more people, spreading more distrust. The feedback loop continues. During the COVID 19 pandemic, this was obviously a huge issue. And one interesting thing that we could look to history for is how the social media industry tried to address it. So there was a period where, you know, institutions of power, you know, within business and within government and, you know, other parts of the, you know, society, we're all kind of on the same page, like, we have to do something about this misinformation. And all the social media platforms, all the big ones anyway, launched these programs for different ways that they were going to address misinformation, health misinformation in particular. So like a lot of accounts that promoted Covid, conspiracy theories got banned from YouTube and from Facebook and Twitter, right? Or they did these things where they would have these labels, where they would have like fact checking, where you'd post something about COVID that wasn't true and then there would be this little thing underneath it that says, actually experts say that this is a lie, but we're leaving the post up because we don't want to censor people. It was a huge effort on behalf of the tech industry and it caused this big backlash, particularly among conservatives. And, you know, people, you know, regardless of their political ideology, were, you know, buying into the conspiracy that Big Tech was censoring us. And because of that, basically all those efforts have been abandoned. The social media industry as a whole basically said, we're not doing this anymore. We're not doing the fact checking thing. We're dialing this back. This was a mistake. Mark Zuckerberg apologized for what he called, you know, like, errors in judgment. The way that he addressed this misinformation. Things might change if we had a problem that was as big as Covid. Again, maybe the political winds would blow the social media industry back into addressing it, but they never really figured out an answer.

[Karen Howe]

Yeah, I mean, I would argue that social media actually has long figured out the answer. They. There's, you know, there's been a lot of investigative journalism that has found internal documents within all the social media companies that show that they've done internal tests that have determined that the way that their algorithms incentivize information to flow is the core problem. And yet they would never change that, because that is also the crux of how they make money is by spreading information in these kinds of ways. So they really do profit in many ways off of the misinformation ecosystem. So even all of the efforts that they were working on to try and fact check things, to try and put on these labels, was already not actually addressing the root issue, which is the platform design. And more a cosmetic afterthought of let's keep the root problem and just slap on some stickers to tell people that this information is wrong.

[Nikki Wolf]

I think all you can do as a consumer of content is try to get out of the algorithmic bubble, at least read widely. Don't just trust everything that the algorithm feeds you, especially if it's trying to make you afraid.

[Thomas Germain]

All right, so let's. Let's switch gears here. My story this week, this is something that I've been reporting on for years now, but there's reason to think the problem is getting a lot worse. And that is, I hate to be the one to break it to you if you haven't heard it before, but your car is spying on you. Used to be that cars represented freedom, right? That, like, I remember, you know, my dad handed me the keys to the old Toyota, and it was like, you know, finally I get to go out and build my own life. Like, I'm escaping from the eyes of my parents and the overseers and my decisions and my time or my mine and mine alone. That is not what being A car

[Nikki Wolf]

is like, that was the American dream. The American dream is on the open road.

[Thomas Germain]

Yeah. The modern car. This is not an original line of mine. It is a smartphone on wheels, right? Cars have computers in them. That comes at no surprise, right? There's sensors built into every part of it that you can imagine. And increasingly, more of the cars on the road have Internet connections that are shipping that data off to the cloud. And perhaps you go, well, you know, I've heard this one before. Yeah. Everything in my life is spying on me. But the details here, I think, are truly shocking. I want to tell you guys about what kind of data we're talking about here and where it's going. And unlike a lot of other privacy problems where it's like, the consequences are kind of vague, this one costs you money directly in a very immediate way. So think about what you do in your car, right? You're using the infotainment system. You're driving around, you got your gps, you're hooking your phone up to it. There's all these different apps. Everything that you're doing are when you hit the brakes, when you turn the wheel, when you buckle your safety belt. Everything is hooked up to a computer. There's sensors everywhere. Every single move is an opportunity for a data point to be created. And we know this in part because the car companies will tell you if you bother to read the privacy policies. There was a study a couple years ago that was done by Mozilla, which is the company that makes the Firefox browser. They also do a lot of, you know, kind of let's keep the Internet healthy, privacy stuff, advocacy work. They looked at 23 major car brands and found that every single one failed to meet Mozilla's pretty basic privacy and security standards. Cars are collecting precise location data about everywhere that you go. But there's all kinds of other stuff, like, you know, depending on, you know, what apps you're hooking up to your phone, like, what information you're putting in there. You know, your financial information could go in if you're making purchases, right?

[Nikki Wolf]

You could.

[Thomas Germain]

You're putting in your address, all your contacts. So it's where you're going, it's who you're hanging out with. It's everything you're listening to on your infotainment system, right? All those, like, not that surprising. But then there's weirder stuff, like your weight, What? And this actually, you know, this kind of a cool thing. Volvo has this new feature where they've, like, reinvented the seat belt the they say that it like adjusts on the fly depending on, you know, like your physical body, you know, how, how much you weigh, how big you are, things like that. So the seat belt works more effectively. But that's kind of the interesting thing here. I talked to a couple of experts who study car privacy and they say that a lot of the most invasive data practices came from a feature that began or was branded as like a safety thing. The strangest by far is in some privacy policies. There was a little note that the car might be collecting information about your sex life, which a lot of people latched onto. Really strange.

[Nikki Wolf]

Specifically said that.

[Thomas Germain]

Specifically says the word sex life. I mean, if you think about it, a lot of cars now have cameras on the outside and the inside. Some cars have cameras pointed at the driver's seat for features like, you know, detecting are you keeping your eyes on the road if you're using like the self driving stuff. Right. I reached out to Kia, who has this particular clause in their privacy policy. A couple other Kirk companies do too. And they said that's just because we collect sensitive information according to, you know, the definitions of the law in California. And we're listing everything that qualifies as sensitive data, which includes stuff about your sex life. But we're not actually collecting that information. It's just like an example of bad stuff that could happen. But we're not doing it. We would never collect. They told me explicitly, Kia has never and will never collect information about your sex life. That's not a thing we do. You don't have to worry about it. But they did not tell me what kinds of sensitive information they do collect.

[Karen Howe]

What?

[BritBox Advertiser]

Why?

[Thomas Germain]

Because this stuff is incredibly valuable for a number of reasons. The one that I think matters the most though is insurance companies. That is the biggest customer for this information, right? Because your car is measuring everywhere you go. It knows how fast you're driving. It knows if you brake too hard or, you know, you took that turn a little too quick. This data is being sold to the insurance industry and they are using it to raise prices for people. Right. Like you probably. You've heard if you have a car your insurance company might be offering you, they call these like telematics services. It's basically like put this thing, use this app, or install this little widget and we'll spy on you. And if you're a good driver, we'll give you a discount. And sometimes they'll like just install this thing and for the first year, a hundred dollars off or whatever it is, the People that I spoke to say this is not a good investment. It's probably not cutting down your insurance bill and it might make it go up. There've been a bunch of lawsuits against General Motors. For example, the US Federal Trade Commission came after GM saying that it was harvesting location data about its drivers without letting them know, without getting consent, and selling it to the insurance business. GM denied this. They said this is a big, you know, misunderstanding of our business practices. Everybody knows, everybody reads the privacy policy, right? Like, what do you mean you don't know? You clicked I consent. There was a settlement. Right. They paid out a lot of money. But what we know is this information is out there, it's for sale, and it's causing real world consequences.

[Karen Howe]

When you say so I've always thought when people say insurance companies that they mean car insurance companies, but something like your weight, I mean, that could also go to a health insurance company. So are we, what other actors are we talking about here that this data ends up getting sold to? Can it in fact be something as seemingly unrelated as health insurance?

[Thomas Germain]

So we have some idea about where the information is going. Right. We know insurance companies are buying it. We like, there's, you know, big loud announcements that the car makers are putting out about how they're partnering with, you know, companies that sell information to the insurance business or directly with the insurance business themselves. We also know that car companies are selling your information to the data broker industry. Right. Data brokers are companies that package up and resell information about consumers to anyone who wants it. And then from there it's like, who can get it? Well, anybody with a credit card who wants to know something about, you know, a huge swath of drivers, the government, we've seen cases where law enforcement where it, they can't get a search warrant or it's just like too much of a pain. You can just buy a location data that shows you where someone was going and then you can get around legal protections that are supposed to, you know, keep you safe from unlawful searches. Much beyond that, we have no idea. Right. The car companies don't have to tell us what they're doing with this information. They are required by some, you know, state and, you know, depending on where you are. Some countries require you to tell your customers whether you're selling information, but you don't have to say who you're selling it to or who's getting it. So it's, you know, the answer is really anybody who wants it. And we have no idea. And more and more of the cars on the road are part of this problem. That's probably something people are wondering, like, well, like, what about my car? Like, if you've got A, you know, 1975, I was going to say the name of a car brand, but I can't think of what's a good car. Nikki, you got name a car for me.

[Nikki Wolf]

When I first moved to America, I bought a 79 Corvette Stingray.

[Thomas Germain]

A 79 Corvette. That one's probably safe. If your car is relatively new, probably you should be concerned, right? Like, if it's sold after, like 2020, then chances are pretty good that it might be involved. If there is an infotainment system in your car, like, if there's a computer. Computer with a little screen, if there's an option to hook your car up to the Internet, then you should be worried. Or it. Assuming this is something that you have a problem with, you should be worried. Like, maybe you don't care. There's benefits, right? Having all these sensors, having all this data. You know, there's a promise that we're going to create a world where, you know, cars are constantly talking to each other and sending information back and forth for safety reasons. Like, oh, there's a deer up ahead. You know, the car three cars down can know that in advance and, you know, put in some automatic braking or something like that. So this has been happening for a while, right? Like, since cars have had Internet connections in them, there's been a data collection problem. But there's reason to think that this might be about to get a whole lot worse. There is a law that was passed in the US that will require car companies to put in biometric scanners that use infrared cameras or other systems to scan your body for the laudable goal of trying to do something about drunk driving. Right? Where the idea here is, like, they would somehow look at what's going on with your body posture and your eyes, or it's not completely fleshed out yet. And then if you appear to be drunk or impaired or too sleepy or something, the car won't start. Or if you're in the middle of the drive, it might enter something called limp mode, they're calling it, where it, like, slows down and forces you to pull over. And this could save lives. If they figure it out, that would be great. The problem is they did not put any provisions about what happens to this information. So in the near future, car companies could be forced to begin to collect what amounts to medical information. Right? They're scanning your body with no restrictions on what they do. With that information afterward, which would open up a whole new trove of privacy concerns that a lot of people I talked to say are very serious. It's unclear when this is happening. That was supposed to go into effect by 2027. It seems the tech isn't quite ready for that, so it could get delayed. But this is a really important moment to be having this conversation because there's a lot of reason to think that the data collection, I'm just going to go for the punt. It's going to get kicked into overdrive, it's going to be bad. So now's the time to be thinking about it.

[Karen Howe]

So is there a way for consumers that want new cars to opt out?

[Thomas Germain]

That's a good question. And the answer is probably kind of frustrating. I reached out to a couple car companies. I wrote an article about this. You can go read it on BBC.com and what they told me, some of them is like, well, we have clear opt out systems. It is worth your time to go in to your car's settings, if there's a screen, see if there's something about privacy, and if you find settings, turn them off. There are certain programs that you can opt into where like, they're like, well, we won't collect the information unless you specifically agree to use this feature. In some cases you can opt back out. But there really aren't any rules requiring these companies to let you stop this data collection in the United States, right? There is no privacy law at the federal level. Depending on where you are, you might have certain complicated rights that are difficult to exercise. The sad truth is there isn't a ton you can do about it. It's worth your time to go look if you have an app that hooks up to your car, if your car has a computer in it, you know, go see what's available to you. But you probably agreed to a privacy policy if your car has a little screen in it when you first set the thing up, maybe you don't remember, you are now abiding by it and it may be difficult for you to do anything to claw those rights back. If you want to use the full

[Nikki Wolf]

features of the car next time, next time you're up to no good in a car, just remember that you don't know who might be watching you.

[Thomas Germain]

Yeah, it's funny, that is the. That used to be the place to do it, right? You want some privacy, you go get in your car, you have that sensitive conversation, right? And now, I don't know.

[Nikki Wolf]

Conversation. Yeah, that's what I meant.

[Karen Howe]

Yeah,

[Thomas Germain]

all right, we gotta wrap up here. But whether you listen to our show in the car or anywhere else, you can find us on BBC Sounds. If you're in the UK or if you're anywhere else, you can listen wherever you get your podcasts or just search for the Interface podcast on YouTube. If you want to get in touch with us, you can send us an [e-mail] or you can find us on WhatsApp at 443-332-072472. Or you can follow us all on social media. And why not? You can find all of the links to our handles down there in the show. Notes if you're into tech, you will love this. TikTok is a live lab where users post instant reviews of the latest trends. Download TikTok and check it out.

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