How to Know What's Real: How to Keep Watch

Summary

Podcast Summary: "How to Know What's Real: How to Keep Watch"

Hosted by Andrea Valdez and Megan Garber from The Atlantic
Release Date: June 3, 2024

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Introduction: The Modern Surveillance Landscape

In this episode, Andrea Valdez and Megan Garber delve into the intricate world of surveillance, exploring both its overt and covert forms in contemporary society. They discuss how technology, particularly wearable devices and facial recognition systems, has blurred the lines between personal autonomy and constant monitoring.

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The Quantified Self Movement and Smartwatches

The conversation opens with a reflection on personal experiences with wearable technology. Andrea shares her obsession with the data her smartwatch collects, highlighting the dual nature of these devices as tools for self-improvement and instruments of self-surveillance.

• Andrea Valdez [02:35]: “Have you ever heard of the quantified self movement? It aspires to 'self-knowledge through numbers.'”

• Megan Garber [02:22]: “I'm realizing I'm having these intensely conflicting emotions… I appreciate the reminder, but I also love the feeling of just sort of being casually judged by a piece of technology.”

Key Points:

• The evolution from basic fitness trackers to advanced smartwatches that monitor various health metrics.
• The balance between accountability for personal health and the discomfort of being constantly monitored by one's own devices.
• The concept of "self-surveillance" and its psychological implications.

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Surveillance Technologies: Physical and Online

The hosts transition to a broader discussion on surveillance infrastructure, both visible and hidden. They emphasize that while many are aware of public surveillance cameras, less attention is given to online surveillance mechanisms.

• Deb Raji [05:03]: “There's also a lot of other tools for surveillance that I think people are less aware of, like Ring cameras… it ends up becoming part of a broader network of surveillance.”

Key Points:

• The prevalence of physical surveillance in urban areas, especially post-9/11.
• The integration of home security devices into larger surveillance networks.
• The pervasive nature of online surveillance and its often unnoticed impact on everyday life.

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Facial Recognition and Its Societal Impacts

Deb Raji, a computer scientist and Mozilla fellow, provides expert insights into facial recognition technology. She discusses the biases inherent in these systems and their implications for marginalized communities.

• Deb Raji [07:08]: “We realized that these systems weren't working very well for minority populations… like, darker skinned female faces.”

• Deb Raji [08:30]: “Some of the information we had about how well these systems worked so far was on data sets that were disproportionately composed of lighter skinned men.”

Key Points:

• The development and deployment of facial recognition technologies by law enforcement and private companies.
• The accuracy disparities across different demographics, leading to wrongful identifications.
• The ethical concerns surrounding the use of biased data sets in training AI models.

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Deepfakes and the Erosion of Trust

The discussion shifts to the rise of deepfakes and their role in creating an "infopocalypse," where distinguishing real from fake becomes increasingly challenging.

• Andrea Valdez [19:08]: “Don Fallis talked about the epistemic threat of deepfakes… we're beginning to maybe distrust what it is that we're seeing.”

• Megan Garber [20:21]: “If it doesn't feel quite right, it probably isn't. A lot of these images don't have a good sense of spatial awareness.”

Key Points:

• The distinction between deepfakes (AI-generated) and cheap fakes (easily manipulated media).
• Real-world examples, such as manipulated audio of public figures, causing misinformation and reputational harm.
• Strategies for identifying and combating fake media, including watermarking and personal vigilance.

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Privacy Implications and Regulatory Challenges

Deb elaborates on the privacy concerns arising from data collection practices essential for AI and surveillance systems, highlighting the fragmented approach to privacy regulation in the U.S.

• Deb Raji [09:32]: “Privacy is one of those things that we just don't… we haven't been able to get to federal level privacy regulation… it's very state based.”

• Deb Raji [10:56]: “The way surveillance and privacy works is that it's not just about the information that's collected about you… it's defining surveillance.”

Key Points:

• The lack of comprehensive federal privacy laws, leading to a patchwork of state regulations.
• The role of the Federal Trade Commission (FTC) in conducting privacy audits and enforcing regulations.
• The collective impact of individual data points in constructing extensive surveillance profiles.

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Personal Responsibility vs. Structural Surveillance

Andrea and Megan explore the tension between individual actions to safeguard privacy and the broader systemic forces that perpetuate surveillance.

• Andrea Valdez [25:24]: “I'm very careful about which apps I allow to have location sharing on me… I make sure that location sharing is something that I'm opting into on the app while I'm using it.”

• Megan Garber [26:26]: “It's not some cinematic dystopia… it's sure the camera's on the street, but it's also the watch on our wrist… we can affect those decisions if we choose to, in part just by paying attention.”

Key Points:

• Strategies individuals employ to limit data collection, such as managing app permissions and using privacy settings.
• The concept of "conscious consumption" as a means to signal values and influence corporate practices.
• The realization that personal choices are intertwined with collective surveillance systems, necessitating a balance between individual agency and advocacy for systemic change.

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Mitigating Surveillance and Protecting Privacy

The episode concludes with actionable advice on how to navigate a surveilled environment, emphasizing both personal vigilance and collective action.

• Deb Raji [22:52]: “If it doesn't feel quite right, it probably isn't… trust your intuition. If you're noticing weird artifacts in the image, it probably isn't real.”

• Deb Raji [23:19]: “Mute very liberally, like on Twitter… don't engage with those kinds of content. Deplatforming is really effective if someone has sort of three strikes in terms of producing a certain type of content.”

Key Points:

• Practical tips for identifying fake media and reducing exposure to malicious content.
• The importance of not engaging with or amplifying harmful or fake content to prevent its spread.
• The role of platform-level interventions, such as deplatforming, in curbing the proliferation of deepfakes and other manipulated media.

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Conclusion: Vigilance in the Age of Surveillance

Andrea Valdez and Megan Garber wrap up the episode by reiterating the pervasive nature of surveillance and the necessity for both personal and collective efforts to navigate and mitigate its effects. They underscore the importance of being informed and proactive in safeguarding one's privacy amidst evolving technological landscapes.

• Andrea Valdez [27:03]: “Who watches the watcher? And the answer is us.”

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Notable Quotes:

• Andrea Valdez [02:35]: “Have you ever heard of the quantified self movement? It aspires to 'self-knowledge through numbers.'”

• Deb Raji [07:08]: “We realized that these systems weren't working very well for minority populations… like, darker skinned female faces.”

• Andrea Valdez [19:08]: “Don Fallis talked about the epistemic threat of deepfakes… we're beginning to maybe distrust what it is that we're seeing.”

• Megan Garber [26:26]: “It's not some cinematic dystopia… it's sure the camera's on the street, but it's also the watch on our wrist… we can affect those decisions if we choose to, in part just by paying attention.”

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Key Takeaways:

1. Surveillance is Multifaceted: It encompasses both visible physical infrastructure and covert online mechanisms, impacting various aspects of daily life.

2. Technology as Double-Edged Sword: While devices like smartwatches aid in self-monitoring, they also contribute to self-surveillance and data collection.

3. Bias in AI Systems: Facial recognition technologies exhibit significant biases, leading to mistrust and potential misuse, especially among minority communities.

4. Erosion of Trust through Deepfakes: The rise of both deepfakes and cheap fakes poses a substantial threat to the credibility of media, necessitating new methods for verification and authentication.

5. Privacy Regulation Lagging: The absence of comprehensive federal privacy laws in the U.S. leaves individuals vulnerable, relying instead on fragmented state regulations and agency interventions.

6. Balancing Personal and Collective Action: Individual efforts to protect privacy are important but must be complemented by systemic changes and collective advocacy to address pervasive surveillance.

7. Vigilance and Education are Crucial: Staying informed about surveillance technologies and their implications empowers individuals to make conscious choices and push for necessary reforms.

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This episode of "How to Age Up" provides a comprehensive exploration of surveillance in the modern age, blending personal anecdotes with expert insights to illuminate the complexities and challenges of maintaining authenticity and privacy in an increasingly monitored world.

Transcript

[Narrator]

Saint John's College is the nation's great books. College students explore 3,000 years of human thought in just four years or two. For graduate students, encounter history's most influential works, where students engage in group discussions about the fundamental questions underlying human society. Learn more about St. John's undergraduate and graduate programs in Santa Fe, New Mexico, Annapolis, Maryland, including online options at sjc.edu.

[Andrea Valdez]

We'Re so done with New Year, new you. This year, it's more you on Bumble. More of you shamelessly sending playlists, especially that one filled with show tunes. More of you finding Geminis because you know you always like them. More of you dating with intention because you know what you want. And you know what?

[Deb Raji]

We love that for you, someone else will too.

[Andrea Valdez]

Be more you this year and find them on Bumblebee. You know, I grew up as a Catholic, and I remember the guardian angel was the thing that I really loved that concept when I was a kid. But then when I got to be, I don't know, maybe around seven or eight, like, your guardian angel's always watching you. At first it was a comfort, and then it turned into kind of like a. Are they watching me? If I pick my nose, do they watch me?

[Megan Garber]

And are they watching out for me or are they just watching me?

[Andrea Valdez]

Exactly. Like, are they my guardian angel angel or my surveillance angel?

[Megan Garber]

Surveillance angel.

[Andrea Valdez]

I'm Andrea Valdez. I'm an editor at the Atlantic.

[Megan Garber]

And I'm Megan Garber, a writer at the Atlantic.

[Andrea Valdez]

And this is how to know what's real.

[Megan Garber]

I just got the most embarrassing little alert from my watch, and it's telling me that it is, quote, time to stand.

[Andrea Valdez]

Why does it never tell us that it's time to lie down?

[Megan Garber]

Right? Or time to just, like, go to the beach or something? And it's weird, though, because I'm realizing I'm having these intensely conflicting emotions about it, because in one way, I appreciate the reminder. I have been sitting too long. I should probably stand up, but I don't also love the feeling of just sort of being casually judged by a piece of technology.

[Andrea Valdez]

No, I understand. I get those alerts, too. I know it very well. And, you know, it tells you, you stand up, move for a minute, and you can do it. You know, you can almost hear it going like, bless your heart.

[Megan Garber]

Bless your lazy little heart. The funny thing, too, about it is, like, I find myself being annoyed, but then I also fully recognize that I don't really have a right to be annoyed because I've asked the watch to do the judging.

[Andrea Valdez]

Yes, Definitely. I totally understand. I mean, I'm very obsessed with the data my smartwatch produces. My steps, my sleeping habits, my heart rate, you know, just everything about it. I'm just obsessed with it. And it makes me think. Well, I mean, have you ever heard of the quantified self movement?

[Megan Garber]

Oh, yeah, yeah.

[Andrea Valdez]

So quantified self, it's a term that was coined by Wired magazine editors around 2007. And the idea was it was this movement that aspired to, quote, unquote, self knowledge through numbers. And, I mean, it's worth remembering what was going on in 2007, 2008. You know, I know it doesn't sound that long ago, but wearable tech was really in its infancy. And in a really short amount of time, we've gone from, you know, our Fitbit to, as you said, Megan, this device that not only scolds you for not standing up every hour, but it tracks your calories, the decibels of your environment. You can even take an EKG with it. And, you know, when I have my smartwatch on, I'm constantly on guard to myself, did I walk enough? Did I stand enough, Did I sleep enough? And. And I suppose it's a little bit of accountability, and that's nice. But in the extreme, it can feel like I've sort of opted into self surveillance.

[Megan Garber]

Yes. And I love that idea in part because we typically think about surveillance from the opposite end. Right. It's something that's done to us rather than something that we do to ourselves and for ourselves. Watches are just one example here. Right. There's also smartphones, and there's this broader technological environment and all of that, that whole ecosystem. It all kind of has question of who's really being watched and then also who's really doing the watching. So I spoke with Deb Raji, who's a computer scientist and a fellow at the Mozilla foundation, and she's an expert on questions about the human side of surveillance and thinks a lot about how being watched affects our reality. I'd love to start with the broad state of surveillance in the United States. What does the infrastructure of surveillance look like right now?

[Deb Raji]

Yeah, I think a lot of people see surveillance as a very sort of out there in the world, physical infrastructure thing, where they see themselves walking down the street and they, like, notice a camera and they're like, yeah, I'm being surveilled. Which does happen if you live in New York, especially post 9 11. Like, you are definitely physically surveilled. There's a lot of physical Surveillance infrastructure of cameras out there. But there's also a lot of other tools for surveillance that I think people.

[Megan Garber]

Are less aware of, like ring cameras and those types of devices.

[Deb Raji]

I think when people install their ring product, they're thinking about themselves. They're like, oh, I have security concerns. I want to just have something to be able to just like, check who's on my porch or not. And they don't see it as surveillance apparatus, but it ends up becoming part of a broader network of surveillance. And then I think the one that people very rarely think of, and again, is another thing that I would not have thought of if I wasn't engaged in some of this work is online surveillance. Faces are sort of the only biometric. You know, it's not like a fingerprint. Like, we don't upload our fingerprint to our social media. Like, we're very sensitive about, like, oh, this seems like important biometric data that we should keep guarded. But for faces, it can be passively collected and passively distributed without you having any awareness of it. But also we're very casual about our faces, so we upload it very freely onto the Internet. And so, you know, immigration officers, ice, for example, has a lot of online surveillance tools where they'll monitor people's Facebook pages and they'll use sort of facial recognition and other products to identify and connect online identities, you know, across various social media platforms, for example.

[Megan Garber]

So you have people doing this incredibly common thing, right, Just sharing pieces of their lives on social media, and then you have immigration officials treating that as actionable data. Can you tell me more about facial recognition in particular?

[Deb Raji]

So one of the first models I actually built was a facial recognition project. And so I'm a black woman, and I noticed right away that there were not a lot of faces that look like mine.

[Megan Garber]

Yeah.

[Deb Raji]

And I remember trying to have a conversation with folks at the company at the time, and it was a very strange time to be trying to have this conversation. This was like 2017. There was a little bit of that happening in the sort of like natural language processing space. Like people are noticing, you know, stereotype language coming out of some of these models, but no one was really talking about it in the image space as much that, oh, some of these models don't work as well for darker skinned individuals or other demographics. We audited a bunch of these products that were these facial analysis products, and we realized that these systems weren't working very well for those minority populations, but also definitely not working for the intersection of those groups. So, like, darker Skinned female faces. Wow. Some of the ways in which these systems were being pitched at the time were sort of selling these products and pitching it to immigration officers to use to identify suspects.

[Narrator]

Wow.

[Deb Raji]

And, you know, imagine something that's not 70% accurate and it's being used to decide, you know, if this person aligns with the suspect for deportation. Like, that's so serious. You know, since we've published that work, we had just this. You know, it was this huge moment in terms of. It really shifted the thinking in policy circles, advocacy circles, even commercial spaces around how all those systems worked. Because all the information we had about how well these systems worked so far was on data sets that were disproportionately composed of lighter skinned men.

[Megan Garber]

Right.

[Deb Raji]

And so people had this belief that, oh, these systems work so well, like, 99% accuracy. They're incredible. And then our work kind of showed, like, well, 99% accuracy on lighter skinned men.

[Megan Garber]

And could you talk a bit about where tech companies are getting the data from to train their models?

[Deb Raji]

So much of the data required to build these AI systems are collected through surveillance. And this is not hyperbole. Right. Like the facial recognition systems, they're built on top of, you know, millions and millions of faces in these databases of millions and millions of faces that are collected, you know, through the Internet or collected through identification databases or through, you know, physical or digital surveillance apparatus. Because of the way that the models are trained and developed, it requires a lot of data to get to a meaningful model. And so a lot of these systems are just very data hungry, and it's a really valuable asset.

[Megan Garber]

And how are they able to use that asset? What are the specific privacy implications about collecting all that data?

[Deb Raji]

Privacy is one of those things that we just don't. We haven't been able to get to federal level privacy regulation in the states. There's been a couple states that have taken initiative. So California has the California Privacy Act, Illinois has bipa, which is sort of a biometric information privacy act. So that's specifically about, you know, biometric data, like faces. In fact, they had a really. I think BIPA's biggest enforcement was against Facebook and Facebook's collection of faces, which does count as biometric data. So in Illinois, they had to pay a bunch of Facebook users a certain settlement amount. Yeah, interesting. Interesting, huh? You know, there are privacy laws, but it's very state based. And it takes a lot of initiative for the different states to enforce some of these things versus having some kind of comprehensive national approach to privacy. That's why Enforcement or setting these rules is so difficult. I think something that's been interesting is that some of the agencies have sort of stepped up to play a role in terms of thinking through privacy. So the Federal Trade Commission, FTC, has done these privacy audits historically on some of the big tech companies. They've done this for quite a few AI products as well, sort of investigating the privacy violations of some of them as well. So I think that that's something that, you know, some of the agencies are excited about and interested in, and that might be a place where we see movement, but ideally we have some kind of law.

[Megan Garber]

And we've been in this moment, this, I guess, very long moment where companies have been taking the ask for forgiveness instead of permission approach to all this, you know, so erring on the side of just collecting as much data about their users as they possibly can, while they can. And I wonder what the effects of that will be in terms of our broader informational environment.

[Deb Raji]

The way surveillance and privacy works is that it's not just about the information that's collected about you. It's like your entire network is now, you know, caught in this web and it's just building pictures of entire ecosystems of information. And so I think people don't always get that. But yeah, it's a huge part of what defines surveillance.

[Narrator]

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[Andrea Valdez]

Do you remember Surveillance Cameraman Megan?

[Megan Garber]

Oh, no. But now I'm regretting that I don't.

[Andrea Valdez]

Well, I mean, I'm not sure how well it was known, but it was maybe 10 or so years ago. There was this guy who. He had a camera, and he would take the camera and he would go and he'd stop and put the camera in people's faces.

[Megan Garber]

Oh, wow.

[Andrea Valdez]

And they would get really upset, and they would ask him, why are you filming me? And, you know, they would get more and more irritated, you know, and it would escalate. And I think the meta point that Surveillance Cameraman was trying to make was, you know, we're surveilled all the time, so why is it any different if someone comes and puts a camera in your face when there's cameras all around you filming you all the time?

[Megan Garber]

Right, that's such a great question. And, yeah, the sort of difference there between the active act of being filmed and then the sort of passive state of surveillance is so interesting there.

[Andrea Valdez]

Yeah. And, you know, that's interesting that you say active versus passive. You know, it reminds me of the notion of the panopticon, which I think is a word that people hear a lot these days. But it's worth remembering that the panopticon is an old idea. So it started around the. The late 1700s with the philosopher named Jeremy Bentham. And Bentham, he outlined this architectural idea, and it was originally conceptualized for prisons. You know, the idea was that you have this circular building, and the prisoners live in cells along the perimeter of the building, and then there's this inner circle, and the guards are in that inner circle, and they can see the prisoners, but the prisoners can't see the guards.

[Megan Garber]

Oh, my goodness.

[Andrea Valdez]

And so the effect that Bantham was hoping this would achieve is that the prisoners would never know if they're being watched, so they'd always behave as if they were being watched. Mm.

[Megan Garber]

And that makes me think of the more modern idea of the watching eyes effect. This notion that simply the presence of eyes might affect people's behavior. And specifically images of eyes, simply that awareness of being watched does seem to affect people's behavior.

[Andrea Valdez]

Oh, interesting.

[Megan Garber]

You know, beneficial behavior, like collectively good behavior, you know, sort of keeping people in line in that very Bentham like way.

[Andrea Valdez]

We have all of these, you know, eyes watching us now. In, I mean, even in our neighborhoods and you know, at our apartment buildings in the form of, say, ring cameras or other, you know, cameras that are attached to, you know, our front doors. Just how we've really opted into being surveilled in all of the most mundane places. I think the question I have is where is all of that information going?

[Megan Garber]

And in some sense that's the question, right? And Dev Raji has what I found to be a really useful answer to that question of where our information is actually going. Because it involves thinking of surveillance not just as an act, but also as a product.

[Deb Raji]

For a long time when you, I don't know if you remember those, you know, complete the picture apps or like spice up my picture, they would use generative models. You would kind of give them a prompt which would be like your face and then it would modify the image to make it more professional or make it better lit. Like sometimes you'll get content that was just, you know, sexualizing and inappropriate. And so that happens in like a non malicious case. Like people will try to just generate images for benign reasons and if they choose the wrong demographic or they frame things in the wrong way, for example, they'll just get images that are denigrating in a way that feels inappropriate. And so I feel like there's that way in which AI for images has sort of led to just like a proliferation of problematic content.

[Megan Garber]

So not only are those images being generated because the systems are flawed themselves, but then you also have people using those flawed systems to generate malicious content on purpose. Right.

[Deb Raji]

One that we've seen a lot is sort of this deepfake porn of young people, which has been so disappointing to me. Just, you know, young boys deciding to do that to young girls in their class. Like it really is a horrifying form of sexual abuse. And I think like when it happened to Taylor Swift, I don't know if you remember, someone used the Microsoft model and you know, generated some non consensual sexual images of Taylor Swift. I think it turned that into like a national conversation. But months before that there had been a lot of reporting of this happening in high school. Anonymous young girls dealing with that, which is just another layer of like trauma because you're like, who? You're not Taylor Swift, Right? So people don't pay attention in the same way. So I think that that problem has actually been a huge issue for a very long time.

[Megan Garber]

Andrea, I'm thinking of that old line about how if you're not paying for something in the tech world, there's a good chance you are probably the product being sold.

[Andrea Valdez]

Right.

[Megan Garber]

But I'm realizing how outmoded that idea probably is at this point point. Because even when we pay for these things, we're still the products, and specifically our data are the products being sold. So even with things like deepfakes, which are typically defined as, you know, using some kind of machine learning or AI to create a piece of manipulated media, even they rely on surveillance in some sense. And so you have this irony where these recordings of reality are now also being used to distort reality.

[Andrea Valdez]

Yeah. You know, it makes of Don Fallis, this philosopher who talked about the epistemic threat of deepfakes and that it's part of this pending infopocalypse.

[Megan Garber]

Oh, my goodness.

[Andrea Valdez]

Which sounds quite grim, I know. But I think the point that Fallis was trying to make is that with the proliferation of deepfakes, we're beginning to maybe distrust what it is that we're seeing. And we talked about this in the last episode. You know, seeing is believing might not be enough. And I think we're really worried about deep fakes. But I'm also concerned about this concept of cheap fakes or shallow fakes. So cheap fakes or shallow fakes, it's, you know, you can tweak or change images or videos or audio just a little bit, and it doesn't actually require AI or advanced technology to create. So one of the more infamous instances of this was in 2019, maybe you remember there was a video of Nancy Pelosi that came out where it sounded like she was slurring her words. Oh, yeah, yeah. But really, the video had just been slowed down using easy audio tools, and just slowed down enough to create that perception that she was slurring her words. So it's a quote unquote cheap way to create a small bit of chaos.

[Megan Garber]

And then you combine that small bit of chaos with the very big chaos of deepfakes.

[Andrea Valdez]

Yeah. So one the cheap fake is it's her real voice. It's just slowed down again using, like, simple tools. But we're also seeing instances of AI generated technology that completely mimics other people's voices. And it's becoming really easy to use. Now, there was this case recently that came out of Maryland where there was an athletic director at a high school, and he was arrested after he allegedly used an AI voice simulation of the principal at his school. And he allegedly simulated the principal's voice saying some really horrible things. And it caused all this blowback on the principle before investigators. You know, they looked into it, they determined the audio is fake. But again, it was just a regular person that was able to use this really advanced seeming technology that was cheap, easy to use, and therefore easy to abuse.

[Megan Garber]

Oh, yes. And I think it also goes to show how few sort of cultural safeguards we have in place right now. Right. Like, the technology will let people do certain things, and we don't always, I think, have a really well agreed upon sense of what constitutes abusing the technology. And, you know, usually when a new technology comes along, people will sort of figure out what's acceptable and, you know, what will bear some kind of social cost and will there be a taboo associated with it. But with all of these new technologies, we just don't have that. And so people, I think, are pushing the bounds to see what they can get away with.

[Andrea Valdez]

Yeah. And we're starting to have that conversation right now about what those limits should look like. I mean, lots of people are working on ways to figure out how to watermark or authenticate things like audio and video and images.

[Megan Garber]

Yeah, yeah. And I think that that idea of watermarking, too, can maybe also have a cultural implication. You know, like, if everyone knows that deepfakes can be tracked and easily, that is itself a pretty good disincentive from creating them in the first place, at least with an intent to fool or do something malic.

[Andrea Valdez]

Yeah. But in the meantime, there's just gonna be a lot of these deep fakes and cheap fakes and shallow fakes that we're just gonna have to be on the lookout for.

[Megan Garber]

Is there new advice that you have for trying to figure out whether something is fake?

[Deb Raji]

If it doesn't feel quite right, it probably isn't. A lot of these images don't have a good sense of, like, spatial awareness, like. Cause it's just pixels in, pixels out. And so there's some of these, like, concepts that we as humans find really easy, but these models struggle with. I advise people to be aware of is like, sort of trust your intuition. If you're noticing weird artifacts in the image, it probably isn't real. I think another thing as well is, like, who posts?

[Megan Garber]

Oh, that's a great one.

[Deb Raji]

Yeah, I mute very liberally, like on Twitter, any platform. I definitely mute a lot of accounts that I notice. Be either caught posting something, either like a community note or something will reveal that they've been posting fake images, or you just see it and you recognize the design of it. And so I just mute that kind of content. Don't engage with those kind of content. Creators at all. And so I think that that's also, like, another successful thing on the platform level. Deplatforming is really effective if someone has sort of three strikes in terms of producing a certain type of content. That's what happened with the Taylor Swift situation, where people were disseminating this, you know, Taylor Swift images and generating more images, and they were. They just went after every single account that did that, you know, completely locked down her hashtag, like that kind of thing, where they just really went after everything. And I think that that's something that, like, we should just do in our personal engagement as well.

[Megan Garber]

Andrea, that idea of personal engagement, I think, is such a tricky part of all of this. I'm even thinking back to what we were saying before about Ring and the interplay we were getting at between the individual and the collective. In some ways, it's the same tension that we've been thinking about with climate change and other really broad, really complicated problems. This connection between personal responsibility, but also the outsized role that corporate and government actors will have to play when it comes to finding solutions. And with so many of these surveillance technologies, we're the consumers with all the agency that that would seem to entail. But at the same time, we're also part of this broader ecosystem where we really don't have as much control as I think we'd often like to believe. So our agency has this giant asterisk, and, you know, consumption itself in this. This networked environment is really no longer just an individual choice. It's something that we do to each other whether we mean to or not.

[Andrea Valdez]

Yeah, you know, that's true, but I do still believe in conscious consumption so much as we can do it. Like, even if I'm just one person, it's important to me to signal with my choices what I value. And in certain cases, I value opting out of being surveilled so much as I can control for it. You know, maybe I can't opt out of facial recognition and facial surveillance, you know, because that would require a lot of obfuscating my face. And I mean, there's not even any reason to believe that it would work. But there are some smaller things that I personally find important. Like, I'm very careful about which apps I allow to have location sharing on me. You know, I go into my privacy settings quite often. You know, I make sure that location sharing is something that I'm opting into on the app while I'm using it. I never let apps just follow me around all the time. You know, I think about what chat apps I'm using, if they have encryption, you know, I do hygiene on my phone around what apps are, you know, actually on my phone because they do collect a lot of data on you in the background. So if it's an app that I'm not using or I don't feel familiar with, I delete it.

[Megan Garber]

Oh, that's really smart. And it's such a helpful reminder, I think, of the power that we do have here and a reminder of what the surveillance state actually looks like right now. It's not some cinematic dystopia. It's sure the camera's on the street, but it's also the watch on our wrist, it's the phones in our pockets, it's the laptops we use for work. And even more than that, it's a series of decisions that governments and organizations are making every day on our behalf. And we can affect those decisions if we choose to, in part just by paying attention.

[Andrea Valdez]

Yeah, it's that old adage, who watches the watcher? And the answer is us.

[Megan Garber]

That's all for this episode of how to Know what's Real. This episode was hosted by Andrea Valdez and me, Megan Garber. Our producer is Natalie Brennan. Our editors are Claudina Baid and Jocelyn Frank. Fact checked by Enna Alvarado. Our engineer is Rob Smirciak. Rob also composed some of the music for this show. The executive producer of Audio is Claudina Baid and the managing editor of Audio is Andrea Valdez.

[Andrea Valdez]

Next time on how to Know what's Real. And when you play the game multiple times, you shift through the roles and so you can experience the game from different angles. You can experience a conflict from completely different political angles and re experience how.

[Narrator]

It looks from each side. Which I think is something like this.

[Andrea Valdez]

Is what games are made for, what.

[Megan Garber]

We can learn about expansive thinking through play. We'll be back with you on Monday.

[Narrator]

St. John's College is for students who seek meaning in their lives, who ask hard questions of themselves and their world, and who dare to free their mind. In vigorous discussion based classes, students grapple with fundamental ideas by engaging with works by some of the world's greatest writers and thinkers, from Homer, Plato, Seneca and Euclid to Nietzsche, Einstein, Wolff and Baldwin. St. John's program of study includes over 200 great books from across 3,000 years of history, including philosophy, literature, politics, math, science and music. At St. John's faculty are known as tutors and not professors. This is because they will never profess to know anything. Rather than telling you what to think, they will help you ask great questions. Questions like how can human flourishing be maximized? What kinds of leaders do we want and need? Can personal peace exist when social chaos reigns? At St. John's you will learn to listen deep and across perspectives, to speak and reason with precision, and to honor the value that each student brings to the conversation. St. John's graduates, who are systems thinkers in a world of specialists, go on to become writers, judges, diplomats, lawyers, school leaders, ethicists, linguists, scientists, researchers, and more. Explore 3,000 years of human thought in just four years or two for graduate students on St. John's two campuses in Santa Fe, New Mexico, and Annapolis, Maryland. Learn about our robust financial aid and our academic programs at sjc. Edu. That's sjc, Eduardo.