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Speaker 1: Well boy, this episode is worth a re Listen, you

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Speaker 1: guys are classic episode this week is about an early

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Speaker 1: Facebook scandal. Isn't it weird that we have to say

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Speaker 1: an early Facebook scandal?

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Speaker 2: Yeah, it's just become one in a laundry list of scandals.

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Speaker 2: But this is one of the ogs.

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Speaker 3: Yeah. It has to do with an outfit called Cambridge

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Speaker 3: Analytica that had a little something to do with an

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Speaker 3: election a couple of years before. This episode was made

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Speaker 3: in twenty eighteen, and it is Hmmm, it's creepy because again,

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Speaker 3: it's all about what happens when your personal information is

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Speaker 3: just available for a private company to be used and

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Speaker 3: then sold.

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Speaker 1: What could go wrong? We're about to find out. From

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Speaker 1: UFOs to psychic powers and government conspiracies, history is riddled

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Speaker 1: with unexplained events. You can turn back now or learn

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Speaker 1: this stuff they don't want you to know.

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Speaker 3: Hello, welcome back to the show. My name is Matt,

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Speaker 3: my name is no.

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Speaker 1: They called me Ben. We are joined with our super

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Speaker 1: producer Paul Mission controlled decand you are you. You are

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Speaker 1: here that makes this stuff they don't want you to know.

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Speaker 1: As you're listening to today's show, feel free to uh

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Speaker 1: drop in your own emoji reaction, whether it's a wow,

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Speaker 1: a sad, I'm mad. I don't know the names of

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Speaker 1: those reactions available on Facebook.

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Speaker 2: Call one of them is the O face. It's just

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Speaker 2: the open mouth. I guess that's wow.

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Speaker 1: Yeah, I call it wow.

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Speaker 3: It could be wow or it could be.

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Speaker 2: Are there official names? Is there a company line on

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Speaker 2: what these reactions should be known as?

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Speaker 1: I hope they all have specific proper noun names, like

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Speaker 1: Derek or calliope.

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Speaker 3: I'm sure the actually is a title to each of

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Speaker 3: those images. Right.

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Speaker 2: Seems like a pretty everything in its right place kind

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Speaker 2: of company. Facebook. Yeah, they don't really let much fall

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Speaker 2: through the cracks.

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Speaker 3: Yeah, I'm speaking of Facebook. While you're listening to this,

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Speaker 3: feel free to peruse our Facebook page stuff they don't

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Speaker 3: want you to know at conspiracy stuff, or our new

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Speaker 3: Here's where it Gets Crazy group on Facebook. Just you know,

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Speaker 3: hang out on there the whole time you're listening.

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Speaker 2: It's fine.

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Speaker 1: Nothing bad will happen to you, nothing bad that hasn't

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Speaker 1: already happened. We'd like to introduce you to Facebook. If

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Speaker 1: for some reason you manage to listen to podcasts and

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Speaker 1: never heard of this before, and you're wondering what the

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Speaker 1: heck is Facebook? I mean the Facebook, right, not a Facebook,

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Speaker 1: And we'll get to that because there was an earlier

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Speaker 1: physical iteration of Facebook. Little known fact, if you were

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Speaker 1: like over two billion people across this fair planet of ours,

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Speaker 1: you use Facebook on a daily basis. Mobile traffic is

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Speaker 1: a huge part of Facebook's operation nowadays because originally was

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Speaker 1: desktop and it was only for members of certain universities

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Speaker 1: or colleges, but now anyone can get on it were

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Speaker 1: provided they have the right equipment and internet connection.

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Speaker 2: Isn't it crazy how mobile has advanced so exponentially. Remember

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Speaker 2: a time where you still had a phone that barely

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Speaker 2: had stuff you could do on it. It had a

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Speaker 2: snake and like a calculator.

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Speaker 3: Yeah, it's what has it been twelve, fifteen, sixteen years?

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Speaker 3: I guess so as it's been really really developing.

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Speaker 2: But it's really ramped up in the last more like

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Speaker 2: ten years. Yeah, you know, with like the I mean

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Speaker 2: the iPhone's only you know, a little more than a

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Speaker 2: decade old, right m h.

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Speaker 1: Yeah, it's true, and it's an exponential growth that has

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Speaker 1: prompted some questions in the industry and outside of the

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Speaker 1: industry and in the minds of fringe researchers as well.

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Speaker 1: Did this stuff grow organically? Was state supported on some level?

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Speaker 1: The answer to that both of those questions is actually yes,

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Speaker 1: but maybe not in a super nefarious way. Regardless of

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Speaker 1: the origin of this exponential growth, we know that mobile is,

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Speaker 1: as Noel said, huge, and many people would not have

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Speaker 1: predicted it. It's safe to say, but as of December

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Speaker 1: twenty sixteen, there were one point one five billion mobile

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Speaker 1: daily active users on Facebook's platforms, whether mobile through an OS,

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Speaker 1: which is the safer way, or mobile through a Facebook app.

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Speaker 2: Does that include Instagram as well? Since Facebook owns Instagram.

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Speaker 1: No, that does not include Instagram, And that's a great question.

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Speaker 2: So that number shoots even further through the roof if

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Speaker 2: you include Instagram, which is also hugely popular.

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Speaker 1: Right, Instagram has been one of the fastest growing, especially

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Speaker 1: since Facebook has been taking an increasing amount of flack

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Speaker 1: over the years. That one point one five billion number,

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Speaker 1: that was an increase of twenty three percent from December

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Speaker 1: to twenty fifteen. And this means, you know, if we

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Speaker 1: read between the lines, that there are a lot of

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Speaker 1: people who do not have a desktop computer or a laptop,

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Speaker 1: have have no intention of buying one and say I

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Speaker 1: can just do everything in my digital life on my phone.

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Speaker 2: It's becoming more increasingly more true. I mean, there's many

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Speaker 2: things that we would rely on a laptop or a

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Speaker 2: computer to be able to do that you can totally

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Speaker 2: do on your mobile device. And it's so much more

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Speaker 2: convenient and easy and you always have it right in

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Speaker 2: front of your eyeballs.

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Speaker 1: Yeah, and you can put your friends on Pokemon Go

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Speaker 1: so that their faces will be introduced even if they

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Speaker 1: introduce to the system, even if they don't have an

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Speaker 1: online profile or a desire to create one.

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Speaker 2: You can poke your friends. If you're not into Pokemon,

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Speaker 2: you can just poke them.

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Speaker 3: So okay, here's here's my big question. And most of

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Speaker 3: you already know this, but you think to yourself, well,

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Speaker 3: Facebook is this massive thing with all of these people

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Speaker 3: walking around on their phones looking at their profiles and stuff.

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Speaker 3: It's all free though, I don't have to pay any

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Speaker 3: money to download Facebook.

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Speaker 1: Yes, if you have a service for free that uses

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Speaker 1: ad revenue, you are not the customer. You are the what.

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Speaker 3: The product.

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Speaker 1: And yep, that's true, folks. You are not the customer

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Speaker 1: of social media. You are the product of social media.

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Speaker 1: Unless you are paying a monthly fee for it. And

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Speaker 1: even then, if you are paying a monthly fee for

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Speaker 1: something if it's not social media, if you're paying a

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Speaker 1: monthly fee for your cable company or your internet connection,

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Speaker 1: they're also taking your data. You are more than one

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Speaker 1: revenue stream for them and for Facebook, this is hugely important.

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Speaker 1: We looked into the advertising revenue right where Facebook publicly

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Speaker 1: makes a lot of its money, and mobile advertisement alone

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Speaker 1: represents eighty eight percent of all Facebook's ad money from

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Speaker 1: Q three, twenty seventeen. Quick explanation if in case, we're

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Speaker 1: getting a little to inside baseball. In businesses, as you

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Speaker 1: may or may not know, profits and losses and revenue

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Speaker 1: are divided up into four segments per year that reach

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Speaker 1: three months long. They're referred to quarters, and in business

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Speaker 1: speak in the lands where people say things like wheelhouse

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Speaker 1: and granular and cadence, these quarters are simply referred to

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Speaker 1: as Q one, Q two, Q three, Q four. So

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Speaker 1: this mobile advertising is huge for Facebook. Facebook needs this

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Speaker 1: stuff to happen, and for that to work, they need

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Speaker 1: to have someone looking it ass They need to have users.

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Speaker 1: That's where you come in. You might be saying, not me,

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Speaker 1: fellas not me, ya, Jabroni's because I don't have a

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Speaker 1: Facebook profile. Well just hold on, hold on, just for now,

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Speaker 1: pretend you do. Let's talk a little bit about you.

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Speaker 1: Let's talk about you, the Facebook user. What do we

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Speaker 1: know about you?

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Speaker 2: So twenty nine point seven percent of users range in

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Speaker 2: age between twenty five and thirty four years old, and

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Speaker 2: that is the most common age demographic of Facebook use.

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Speaker 2: And that's also a pretty sweet marketing spot too, isn't it.

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Speaker 3: Oh yeah, all those young professionals, all that new income

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Speaker 3: fresh for the harvesting, or you know, the folks who

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Speaker 3: are staying at home with their parents because they can't

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Speaker 3: afford to buy, you know, pay rent anywhere, because that's

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Speaker 3: a whole other issue we're having to deal with.

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Speaker 2: Well that's a lot less fun, Matt.

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Speaker 1: But anyway, yeah, we do know that we should do

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Speaker 1: an episode on the looming financial crisis of the millennial

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Speaker 1: generation and the post millennial generation. Stay tuned. As a

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Speaker 1: matter of fact, if you're listening, you're in that age

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Speaker 1: group right to us, and let you let us know

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Speaker 1: what you think. The biggest misconceptions are about your generation.

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Speaker 1: And everybody does want to have some sort of attention

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Speaker 1: or connection on social media, right, That's why it works.

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Speaker 1: You know, you say something that is a funny one

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Speaker 1: liner or whatever, and then you check back a few

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Speaker 1: hours later you're like, wow, twenty four people know I exist.

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Speaker 3: Yeah, you know what I mean. Sweet addiction, isn't it?

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Speaker 1: It is? And we do see dopamine spikes both when

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Speaker 1: you check a mobile phone just in general for text

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Speaker 1: messages and when you look at Instagram or Facebook or

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Speaker 1: Twitter or what have you. So we are going to

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Speaker 1: help you a little bit with this. It turns out

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Speaker 1: that Facebook users in general are most often on this

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Speaker 1: platform in the middle of the week between one to

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Speaker 1: three pm, and they're eighteen percent more likely to engage

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Speaker 1: with whatever is posted. That means throw a like or

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Speaker 1: a comment or reaction of some sort if it's Thursday

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Speaker 1: and Friday. So if you want a lot of people

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Speaker 1: to see your stuff and talk to.

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Speaker 3: You, do it middle of the day on Thursday or Friday.

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Speaker 2: So what part of the bell curve am I if

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Speaker 2: I'm on it Monday through Friday, twenty four SEV.

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Speaker 1: We're a little bit different in that regard because, in

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Speaker 1: interest of full transparency, we use Facebook for work.

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Speaker 2: That's true, That's what I tell myself anyway.

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Speaker 1: And there are a lot of people I think who

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Speaker 1: would say who would be in the same boat and say, well,

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Speaker 1: I may only check it every so often, but I

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Speaker 1: get notifications automatically pushed, or I am always logged in

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Speaker 1: because Facebook wants you to feel like it's more convenient

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Speaker 1: to always be logged in. Not all of these users, though,

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Speaker 1: are real.

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Speaker 3: Say what right?

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Speaker 1: CNN estimates their eighty three million fake profiles floating around,

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Speaker 1: and that's probably a lower number than it is in actuality.

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Speaker 3: Wow, I mean, that's a sizable chunk of one point

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Speaker 3: something billion, you know users, But that still leaves you

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Speaker 3: with over a billion users. True, even if it's one

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Speaker 3: hundred million, even if it's two hundred million, there's still

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Speaker 3: so many human beings using this thing.

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Speaker 1: And there are also people who have multiple accounts of

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Speaker 1: their own, so there's still a meat sack at the

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Speaker 1: other side of the keyboard or the phone. But does

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Speaker 1: that mean that only one of those profiles accounts is real?

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Speaker 2: Well, it's true. It's like, you know, some folks that

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Speaker 2: we work with will make themselves a work related Facebook

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Speaker 2: what they call a public facing Facebook profile, and then

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Speaker 2: they'll keep their personal one, so they just you know,

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Speaker 2: befriend their actual friends. Not that we're not all friends

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Speaker 2: with you know, our people that like the show and

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Speaker 2: that we interact with. We're all internet friends. But you know,

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Speaker 2: it's different. You have to separate it. I, on the

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Speaker 2: other hand, I only use my one because I don't

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Speaker 2: care and I'm an attention hoard.

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Speaker 3: Uh. Same.

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Speaker 1: Actually, on average, regardless of whether the user is real

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Speaker 1: or a bot, the user will tend to spend twenty

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Speaker 1: minutes on Facebook per visit. Advertisers love this number because

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Speaker 1: that is an amazing amount of time to spend on

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Speaker 1: one website, especially one there's continually refreshing ads for you.

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Speaker 2: Can I ask you guys a quick question if either

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Speaker 2: have you ever been taken in by an online ad

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Speaker 2: as an online ad ever done anything more to you

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Speaker 2: than just register annoyance? Have you have you have you

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Speaker 2: had a successful interaction with an online ad where it

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Speaker 2: knew something about you and served you just the right

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Speaker 2: product or service that you were immediately like, I must

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Speaker 2: have this.

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Speaker 1: You mean, like a civilian thing. Yeah, instead of going

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Speaker 1: to complain about the ad, right?

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Speaker 2: Oh then no, no, never, I always wonder it just

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Speaker 2: it just seems very ham fisted most of the time.

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Speaker 2: The way you get served ads online. I'm just wondering, like,

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Speaker 2: how successful is it really?

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Speaker 3: I don't know, I can't think of one.

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Speaker 1: It's interesting because you're right. It reminds me of the

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Speaker 1: early days in the Terminator universe when they say the

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Speaker 1: first androids were easy to spot, right, they didn't sweat

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Speaker 1: or bleed or whatever, and they got increasingly sophisticated. That's

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Speaker 1: what's happening now. So it may be ideal well in

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Speaker 1: the ideal world for some of these ad providers and

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Speaker 1: data aggregation centers. It may be that they want you

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Speaker 1: to not know that you are being taken in by

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Speaker 1: an ad. They want you to feel like you just

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Speaker 1: had an epiphany and realized you needed a whopper.

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Speaker 2: Yeah, you got to wonder, like, maybe even the ads

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Speaker 2: that I see as being unsuccessful, maybe they're somehow implanting

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Speaker 2: that whopper idea in my head through subliminal means.

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Speaker 3: I don't know. I don't know, or they're just doing

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Speaker 3: it outright. If it's a beauty product or something like

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Speaker 3: what my wife watches on social media, it's, you know,

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Speaker 3: someone that she likes in, is interested in, and follows.

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Speaker 3: But then all of a sudden, a beauty product will

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Speaker 3: like show up that the person is holding, and we'll

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Speaker 3: mention just quickly, and then somehow she wants to buy

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Speaker 3: it all of a sudden.

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Speaker 1: Well, and that's you know, that's a super effective thing

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Speaker 1: that's happened ever since we were children. Right, and then

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Speaker 1: there's right now for a lot of Facebook ads. If

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Speaker 1: you have a Facebook account, you have probably encountered the

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Speaker 1: following cartoonish situation. You have, let's say, made a big purchase.

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Speaker 1: You have purchased a new car, and like, this is

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Speaker 1: my new Toyota Corolla, and that information bounces around backstage

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Speaker 1: where you can't see, and then boom, you all of

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Speaker 1: a sudden are inundated with ads for Toyota Corollas. That's

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Speaker 1: silly and it makes no sense, that's exactly yeah, but

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Speaker 1: that's what happens.

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Speaker 3: You know.

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Speaker 1: They're like, you know what, this person who just bought

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Speaker 1: a boat for the first time probably wants five more boats.

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Speaker 2: Why not like a boat shammy though, or like a

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Speaker 2: boat trailer, you know, why not, like, you know, get

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Speaker 2: a little smarter and granular with it, because you're right

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Speaker 2: every time, like or even if like I've looked at

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Speaker 2: a product on a site and maybe I didn't buy it.

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Speaker 2: Maybe this is smart. It inundates me with ads for

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Speaker 2: that same product that I almost bought, that I could

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Speaker 2: see being like, well, I guess I could buy it.

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Speaker 2: I keep getting reminded maybe I do need it after all.

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Speaker 2: But the whole boat with a boat thing just seems

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Speaker 2: like a waste of algorithm.

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Speaker 1: But if twenty thousand people ignore it, and one person says,

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Speaker 1: you know what, yes, is that the ratio you think

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Speaker 1: two boats? No, that's way too high. It's way less.

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Speaker 1: But we know that regardless of the reaction, people are

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Speaker 1: seeing this, and you don't always have to be on

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Speaker 1: Facebook to see it. On average, those like and share

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Speaker 1: buttons that go across all these websites, they're viewed across

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Speaker 1: ten million websites daily, again estimated, probably a lower end

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Speaker 1: estimation too. Every sixty seconds on Facebook, five hundred and

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Speaker 1: ten thousand comments are posted, two hundred and ninety three

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Speaker 1: thousand statuses updated, and one hundred and thirty six photos

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Speaker 1: are uploaded. And one in five page view in the

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Speaker 1: US occurs on Facebook one in five twenty percent of

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Speaker 1: every single page view. That is crazy. But how did

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Speaker 1: Facebook get there. How did it start? What led us

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Speaker 1: to this strange thing?

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Speaker 3: Well, you have to go back to two thousand and

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Speaker 3: four year of our Lord, February fifth. In fact, it

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Speaker 3: was the brainchild of a man named.

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Speaker 2: Mark Zuckerberg, Old Marcus Zuckerberg.

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Speaker 3: He and his roommate. You've all seen the movie. We've

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Speaker 3: all seen the movie, Fantastic Movie.

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Speaker 2: It's pretty good. Remember when you first heard there was

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Speaker 2: gonna be a Facebook movie and you're like, how's that

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Speaker 2: gonna work? Yeah, that's gonna be dull.

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Speaker 3: I had no idea there was actually compelling stuff behind

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Speaker 3: the creation of this, But yes, Zuckerberg and his Harvard

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Speaker 3: roommate Eduardo Severin, they created this website and it was

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Speaker 3: built after or off of another program he'd already created

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Speaker 3: called facemash all one word yep, and that one. That

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Speaker 3: one was created in two thousand and three year prior

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Speaker 3: to it. So face smash was developed by Zuckerberg. He

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Speaker 3: wrote all the software for it. He made a website

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Speaker 3: when he was in his second year of college, and

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Speaker 3: the website was set up as this it's like a

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Speaker 3: hot or Not game. You've seen it before online. At

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Speaker 3: least around that time, there was even hotter not dot com.

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Speaker 3: That was a thing you just say, yes, this person

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Speaker 3: is attractive or no, this person is not attractive.

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Speaker 1: And it was entirely female students, two female students side

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Speaker 1: by side, and then you would vote on who was

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Speaker 1: hot and who was not.

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Speaker 2: So this sounds great, yeah, which is like tender.

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Speaker 1: Basically, you know, I don't I don't participate in online

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Speaker 1: dating apps.

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Speaker 2: I'm smart, that's smart.

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Speaker 3: No.

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Speaker 2: But was this information stored? Was it was there some

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Speaker 2: kind of leaderboard? I thought I remember hearing something about that, like,

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Speaker 2: was it literally just you do it and it goes away?

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Speaker 3: Hey, man, I don't know the ins and outs of

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Speaker 3: face Smash Smash.

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Speaker 1: They did, they did store, they did store the data,

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Speaker 1: and they also at Harvard at the time, they had

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Speaker 1: a physical thing called a Facebook. There was more than one,

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Speaker 1: and these just had a picture of someone's staff, alumni

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Speaker 1: or current student and a little blurb of information about them,

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Speaker 1: sort of a who's who kind of thing. And so Facebook,

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Speaker 1: the online platform, was named after this because Zuckerberg thought

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Speaker 1: they would try to digitize and he's like, look, I

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Speaker 1: could do it better, and I could do it in

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Speaker 1: a week. And it turns out he was right. Because

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Speaker 1: today Facebook is one of the largest repositories of this

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Speaker 1: sort of information on the planet. And you know, to

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Speaker 1: your question, I wonder, I wonder if somewhere deep in

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Speaker 1: his own secret files completely air gapped, if Mark Zuckerberg

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Speaker 1: is still going through his hot or not and thinking for.

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Speaker 3: The world, for the world, Oh jeez. Well, And you know,

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Speaker 3: it's one of those things where when you're creating a

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Speaker 3: Facebook like that, you're gather information and one person or

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Speaker 3: a team of people are creating that physical book with

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Speaker 3: that information. Right. What Zuckerberg realized is that people would

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Speaker 3: voluntarily insert their own blurbs and then add pictures and

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Speaker 3: then videos, and then check into places and say where

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Speaker 3: I've been. He was onto something pretty crazy. That's kind

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Speaker 3: of deep in the heart of all of us.

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Speaker 2: It's very true. It's very true. I mean, you know,

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Speaker 2: that's why we're going to get into this later. But

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Speaker 2: it's hard to accuse people of stealing your data when

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Speaker 2: you give it so willingly.

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Speaker 3: Yeah.

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Speaker 1: I think the common human trait we're talking about is narcissism. Yeah,

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Speaker 1: is look at me itis, right, which we suffer from

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Speaker 1: as a species, but also compels our species to do

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Speaker 1: some amazing things, and now what happens. What happens when

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Speaker 1: one company has all of this information at its fingertips.

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Speaker 1: Fast forward way past two thousand and four, but not

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Speaker 1: as far as you would think, and enter Cambridge Analytica.

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Speaker 2: That's right, this company, Cambridge Analytica, which I knew precious

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Speaker 2: little about until you know the kerfuffle of late, was

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Speaker 2: started in twenty thirteen. The company markets and Marketed and

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Speaker 2: markets continues to market itself as a source of consumer research,

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Speaker 2: targeted advertising, and other data related services to both political

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Speaker 2: and corporate clients. So you know, in short, there they're

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Speaker 2: kind of mining data and crunching the numbers, running it right.

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Speaker 2: According to The New York Times, it was launched with

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Speaker 2: fifteen million dollars of seed money, backing money by billionaire

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Speaker 2: Republican donor Robert Mercer and Steve Bannon.

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Speaker 3: Oh Steve Bannon, that's sounds familiar.

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Speaker 1: Stevieb Yes, the advisor for the Trump campaign and later

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Speaker 1: for a time an advisor to the Trump administration. Now

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Speaker 1: bitter enemy.

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Speaker 2: Also was the kind of power behind the throne of Breitbart,

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Speaker 2: this very divisive right wing website.

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Speaker 1: And everything was going along swimmingly until that is in

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Speaker 1: March of twenty eighteen, when the house of cards began

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Speaker 1: to tumble, and we will.

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Speaker 2: Sort through the debris when we return from a quick

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Speaker 2: sponsor break.

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Speaker 1: Here are the facts. On March seventeenth, twenty eighteen, reports

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Speaker 1: from the Guardian and The New York Times revealed that

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Speaker 1: Cambridge Analytica, again a data analysis firm that had worked

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Speaker 1: with President Trump's campaign, had harvested the personal information of quote,

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Speaker 1: around fifty million Facebook users without permission. Whether that's permission

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Speaker 1: from Facebook, whether that's permission from the users like they

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Speaker 1: would ask. This revelation came from a whistleblower named Christopher

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Speaker 1: Wiley Wylie, who states that he helped build the firm

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Speaker 1: and worked there until twenty fourteen. The way this is

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Speaker 1: that Cambridge Analytica retrieved the data from an outfit called

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Speaker 1: Global Science Research or GSR, a company owned by University

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Speaker 1: of Cambridge professor Alexander Kogan.

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Speaker 3: Yeah, and he collected all this data in twenty thirteen.

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Speaker 3: He used a psychographic personality test app and it was

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Speaker 3: called this is Your Digital Life all one word, just

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Speaker 3: this is your Digital life. There were there are around

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Speaker 3: three hundred thousand people who and who actually like got

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Speaker 3: this app and installed it, and then they gave it

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Speaker 3: permission to collect their personal information from Facebook, including the

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Speaker 3: city where they set up the profile, the content they liked,

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Speaker 3: and the information about their friends. Now, this is the

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Speaker 3: most important part. You gave it permission to collect information

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Speaker 3: about your friends, so that three hundred thousand people jumped

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Speaker 3: up exponentially.

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Speaker 2: Let's say, even when you hear about like recon data

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Speaker 2: recon that law enforcement agencies or the FBI do where

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Speaker 2: you can know something about who people interact with that

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Speaker 2: actually knowing who they are kind of like you can

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Speaker 2: see the web of communications and it starts to give

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Speaker 2: you a really clear picture even without knowing the specifics

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Speaker 2: about who's in the chain.

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Speaker 3: Dude, you can do that with the white pages, That's what.

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Speaker 2: I was saying, But with this, it's like they literally

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Speaker 2: have like all the information. So it's like no end

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Speaker 2: to the kind of stuff you could learn about people

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Speaker 2: with this kind of access, Right, Yeah, So this is

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Speaker 2: what happens.

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Speaker 3: You get three three hundred thousand people to bite on

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Speaker 3: the hook. Then all of a sudden, you've got the

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Speaker 3: information on over fifty million people.

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Speaker 1: At the time. That's why we estimated exactly. And then

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Speaker 1: the other sticky problem with this. Without going too far

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Speaker 1: into big data, we can recommend that you check out

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00:23:46,040 --> 00:23:48,359
Speaker 1: our earlier episode on it. If you would like to

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00:23:48,400 --> 00:23:51,639
Speaker 1: learn some frightening things about target, you can find that

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Speaker 1: on our website. One of the sticky insidious things about

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Speaker 1: this is what Facebook would refer to as frictionless sharing. Right.

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00:24:02,160 --> 00:24:08,399
Speaker 1: It's very fluffy euphemistic phrase for the process that Nol

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00:24:08,560 --> 00:24:12,080
Speaker 1: and Matt are describing here, and it does expand. It's

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Speaker 1: like that six degrees of Kevin Bacon game, although using

438
00:24:17,280 --> 00:24:21,520
Speaker 1: their using their information that they had aggregated, Facebook later

439
00:24:21,600 --> 00:24:25,360
Speaker 1: found people are more like three and a half degrees

440
00:24:25,640 --> 00:24:29,840
Speaker 1: separated from each other, including maybe Kevin Bacon. I don't know,

441
00:24:29,920 --> 00:24:30,680
Speaker 1: it's an average.

442
00:24:30,760 --> 00:24:32,199
Speaker 2: I mean, how often have you, like, haven't met a

443
00:24:32,240 --> 00:24:35,359
Speaker 2: new random person on Facebook and then you've ended up

444
00:24:35,359 --> 00:24:37,600
Speaker 2: befriend of them on Facebook and realized that you have

445
00:24:37,720 --> 00:24:41,160
Speaker 2: several mutual friends, even if there's somebody from like another state,

446
00:24:41,400 --> 00:24:43,320
Speaker 2: you know what I mean, Like, it's not that far fetched.

447
00:24:43,960 --> 00:24:46,040
Speaker 2: So the web starts to shrink, or at least the

448
00:24:46,080 --> 00:24:47,760
Speaker 2: access to it, you know, the more you think about

449
00:24:47,920 --> 00:24:49,360
Speaker 2: you know, these connections.

450
00:24:49,720 --> 00:24:54,080
Speaker 1: Yeah, absolutely, And on the so March seventeenth, This all

451
00:24:54,080 --> 00:24:58,920
Speaker 1: goes public right the next day, Facebook bans Cambridge Analytica,

452
00:24:59,160 --> 00:25:02,360
Speaker 1: and they ban its parent company, and then they ban

453
00:25:02,600 --> 00:25:06,520
Speaker 1: Kogan himself. They also on the same day banned Christopher Wiley,

454
00:25:06,960 --> 00:25:07,840
Speaker 1: the whistleblower.

455
00:25:08,040 --> 00:25:08,240
Speaker 3: Yeah.

456
00:25:08,359 --> 00:25:12,320
Speaker 1: On March nineteenth, Facebook stock plummets takes a nosedive. On

457
00:25:12,359 --> 00:25:16,080
Speaker 1: the same day, Congress starts to sniff around. Senator Edward

458
00:25:16,200 --> 00:25:19,600
Speaker 1: Markey from Massachusetts says, hey, we got to hold some

459
00:25:19,720 --> 00:25:24,280
Speaker 1: hearings against these Facebook and Cambridge Analytica jokers and paraphrasing,

460
00:25:24,600 --> 00:25:27,720
Speaker 1: and specifically he wrote to the ranking members of the

461
00:25:27,760 --> 00:25:31,520
Speaker 1: Senate Committee on Commerce that Senator John Thune and Senator

462
00:25:31,600 --> 00:25:36,240
Speaker 1: Bill Nelson of South Dakota and Florida, respectively, and Marky says,

463
00:25:36,520 --> 00:25:39,400
Speaker 1: we need to move quickly to hold hearings since Facebook

464
00:25:39,480 --> 00:25:44,640
Speaker 1: is required to quote obtain explicit permission before sharing data

465
00:25:44,680 --> 00:25:48,840
Speaker 1: about its users. Facebook holds an emergency meeting. They also

466
00:25:49,320 --> 00:25:53,000
Speaker 1: this is enjoyable. They also for everyone at the meeting

467
00:25:53,119 --> 00:25:55,560
Speaker 1: their version of an all hands They have a poll

468
00:25:55,880 --> 00:26:00,399
Speaker 1: for the employees, you know what you think. Zuckerberg doesn't

469
00:26:00,480 --> 00:26:02,960
Speaker 1: say anything about this for three days, and it's not

470
00:26:03,040 --> 00:26:06,399
Speaker 1: until March twenty first that He addresses it with a

471
00:26:06,520 --> 00:26:09,880
Speaker 1: post on his Facebook account. Got to respect the consistency,

472
00:26:10,320 --> 00:26:13,199
Speaker 1: and he says what happens is his responsibility, but there

473
00:26:13,200 --> 00:26:16,119
Speaker 1: are changes made to the platform that will prevent it

474
00:26:16,160 --> 00:26:19,879
Speaker 1: from happening again. He proposes a three step plan going forward,

475
00:26:19,920 --> 00:26:23,680
Speaker 1: investigate third party apps, restrict developers' access to user data

476
00:26:23,720 --> 00:26:26,440
Speaker 1: on the whole, and give users tools to see what

477
00:26:26,560 --> 00:26:28,679
Speaker 1: information of theirs is being shared.

478
00:26:28,800 --> 00:26:32,040
Speaker 2: So, Ben, if I didn't install my Digital Life, what

479
00:26:32,160 --> 00:26:33,760
Speaker 2: was my data breached?

480
00:26:34,040 --> 00:26:34,919
Speaker 1: Yeah? Quite possible.

481
00:26:34,960 --> 00:26:36,960
Speaker 2: Yeah, so can you explain why.

482
00:26:37,000 --> 00:26:40,480
Speaker 3: So there's this thing where in the rules of Facebook,

483
00:26:40,800 --> 00:26:43,280
Speaker 3: if you're an advertiser and you are collecting data in

484
00:26:43,320 --> 00:26:46,840
Speaker 3: this way, it says that you can, as the developer

485
00:26:46,840 --> 00:26:50,400
Speaker 3: of an app, take like glean the information of friends

486
00:26:50,440 --> 00:26:53,880
Speaker 3: of the people who have actually installed your app or whatever. However,

487
00:26:54,240 --> 00:26:57,120
Speaker 3: you have to use that data in a very limited

488
00:26:57,160 --> 00:27:01,440
Speaker 3: way and only to improve your app that you're using.

489
00:27:01,440 --> 00:27:04,240
Speaker 3: You can't actually sell that data to a third party

490
00:27:04,640 --> 00:27:06,520
Speaker 3: or use that data for any other reason. That that

491
00:27:06,640 --> 00:27:09,199
Speaker 3: was the original rules they were setting, right, And.

492
00:27:09,200 --> 00:27:11,679
Speaker 2: Then just to clarify, that's exactly why they made that

493
00:27:11,800 --> 00:27:16,159
Speaker 2: change where the third parties don't have quite carte blanche

494
00:27:16,200 --> 00:27:19,920
Speaker 2: access to your friend's data only to yours directly because

495
00:27:19,960 --> 00:27:22,200
Speaker 2: you have opted in to write that third party.

496
00:27:22,680 --> 00:27:26,240
Speaker 1: Again, it's restricted. Ye, that's a lot. Like, that's a

497
00:27:26,240 --> 00:27:30,640
Speaker 1: lot like flame retardant or water resistant. It's not proof,

498
00:27:31,040 --> 00:27:34,480
Speaker 1: not water or flameproof. It's it's still not banned.

499
00:27:34,560 --> 00:27:35,480
Speaker 3: Yeah, there's still a fire.

500
00:27:35,760 --> 00:27:38,480
Speaker 1: And this is where some dicey things start happening. March

501
00:27:38,520 --> 00:27:41,560
Speaker 1: twenty second, This all happens so quickly. March twenty second,

502
00:27:42,200 --> 00:27:45,959
Speaker 1: President Donald Trump goes on Twitter, which is his social

503
00:27:46,000 --> 00:27:49,639
Speaker 1: media of choice, and he appears to boast about a

504
00:27:49,720 --> 00:27:53,359
Speaker 1: partnership with Cambridge Analytica. He doesn't name them, but he

505
00:27:53,400 --> 00:27:56,320
Speaker 1: says people are no longer claiming his use of social

506
00:27:56,400 --> 00:27:59,439
Speaker 1: media was less robust than Clinton, that being Hillary Clinton

507
00:28:00,160 --> 00:28:03,040
Speaker 1: in the twenty sixteen campaign in election, And while he

508
00:28:03,119 --> 00:28:08,119
Speaker 1: doesn't outright name them, the timing suggests that he was

509
00:28:08,200 --> 00:28:09,680
Speaker 1: talking about Cambridge Analytico.

510
00:28:09,680 --> 00:28:11,280
Speaker 2: Well, it's sort of like that boast he makes where

511
00:28:11,280 --> 00:28:13,720
Speaker 2: he says I don't pay my taxes, so I'm smart.

512
00:28:14,160 --> 00:28:16,280
Speaker 2: It's kind of like him saying I figured out how

513
00:28:16,320 --> 00:28:19,040
Speaker 2: to exploit social media, so I'm smart.

514
00:28:20,200 --> 00:28:23,399
Speaker 1: Yeah, I guess you know. I don't follow his tweets

515
00:28:23,440 --> 00:28:23,959
Speaker 1: very closely.

516
00:28:24,880 --> 00:28:26,880
Speaker 2: Don't you think it's sort of an odd thing to say,

517
00:28:27,080 --> 00:28:29,960
Speaker 2: given how controversial this stuff has been for him to

518
00:28:29,960 --> 00:28:32,000
Speaker 2: come out and say that, Sure, if he wasn't kind

519
00:28:32,000 --> 00:28:35,520
Speaker 2: of trying to vindicate himself in some way or say that, like,

520
00:28:35,880 --> 00:28:37,439
Speaker 2: I don't know, it just seems a little boastful to me.

521
00:28:38,080 --> 00:28:38,640
Speaker 3: It's puzzling.

522
00:28:38,680 --> 00:28:42,040
Speaker 1: Indeed, Yeah, he did. I mean, it's clearly a boast.

523
00:28:42,640 --> 00:28:47,600
Speaker 1: On March twenty third, ellin musk Our modern Tony stark

524
00:28:47,880 --> 00:28:51,200
Speaker 1: Our modern Tesla. I guess he would want us to say,

525
00:28:51,840 --> 00:28:55,960
Speaker 1: gets spooked and deletes his SpaceX account. He deletes a

526
00:28:56,000 --> 00:28:59,640
Speaker 1: Tesla account. Zuckerberg is invited on the same day to

527
00:28:59,720 --> 00:29:01,640
Speaker 1: test five in front of the House of Reps House

528
00:29:01,640 --> 00:29:05,440
Speaker 1: of Representatives, many of whom will go on to thoroughly

529
00:29:05,520 --> 00:29:10,120
Speaker 1: embarrass themselves with their lack of knowledge regarding social media

530
00:29:10,160 --> 00:29:11,920
Speaker 1: platforms and technology in general.

531
00:29:11,960 --> 00:29:13,320
Speaker 2: I saw a really good meme where it was like

532
00:29:13,360 --> 00:29:15,760
Speaker 2: a screenshot of all the people that were quizzing him,

533
00:29:15,920 --> 00:29:19,640
Speaker 2: and it just had like fifty spyware search bars like

534
00:29:19,680 --> 00:29:21,800
Speaker 2: stacked up on top of themselves, like in their browser,

535
00:29:21,880 --> 00:29:23,920
Speaker 2: you know, like those ones that like automatically install and

536
00:29:24,000 --> 00:29:25,840
Speaker 2: you can't get rid of them. Well, they can't fill

537
00:29:25,880 --> 00:29:26,480
Speaker 2: their whole screen.

538
00:29:26,640 --> 00:29:31,040
Speaker 1: Do you think his photoshop? Oh yeah, jud it's worth

539
00:29:31,040 --> 00:29:32,400
Speaker 1: it him. Yeah, I think so too. Well.

540
00:29:32,440 --> 00:29:34,440
Speaker 3: And here's here's the whole other thing that we're not

541
00:29:34,480 --> 00:29:39,959
Speaker 3: even gonna get into too deeply in this episode, but

542
00:29:40,040 --> 00:29:43,040
Speaker 3: some of the some of the ways that Cambridge Analytica

543
00:29:43,120 --> 00:29:48,120
Speaker 3: was using this data included influencing, if not just you know,

544
00:29:48,240 --> 00:29:53,560
Speaker 3: the American president presidential election, the entire Brexit situation.

545
00:29:54,080 --> 00:29:57,080
Speaker 1: Yes, that is true. That is true. And this is

546
00:29:57,160 --> 00:30:00,280
Speaker 1: one of and their UK based company to Cambridge. So

547
00:30:01,040 --> 00:30:05,480
Speaker 1: this is the primary impetus for officers from Britain's Information

548
00:30:05,520 --> 00:30:09,920
Speaker 1: Commissioner's Office getting involved. They're a data watchdog group, so

549
00:30:09,960 --> 00:30:13,800
Speaker 1: they raid the offices at Cambridge Analytica in London. They

550
00:30:13,840 --> 00:30:15,960
Speaker 1: take their time and they're there for seven hours.

551
00:30:16,040 --> 00:30:19,120
Speaker 2: I heard an NPR piece where someone was able to

552
00:30:19,200 --> 00:30:22,560
Speaker 2: get their kind of dossier, their their digital you know,

553
00:30:23,440 --> 00:30:28,360
Speaker 2: dossier that Cambridge Analytica assembled about them particularly, and it's

554
00:30:28,400 --> 00:30:31,200
Speaker 2: not the most alarming kind of sounding stuff that you

555
00:30:31,280 --> 00:30:35,400
Speaker 2: might imagine. It's like you're who are you, what party

556
00:30:35,400 --> 00:30:37,080
Speaker 2: are you more likely to identify with?

557
00:30:37,160 --> 00:30:37,360
Speaker 3: You know?

558
00:30:37,360 --> 00:30:40,960
Speaker 2: It represented that kind of stuff, and Zuckerberg, even in

559
00:30:41,000 --> 00:30:43,560
Speaker 2: his testimony said this is all stuff people would share

560
00:30:43,600 --> 00:30:47,040
Speaker 2: openly themselves. So, you know him, in an attempt to

561
00:30:47,080 --> 00:30:50,000
Speaker 2: absolve himself, I was wondering what you guys think, like

562
00:30:50,760 --> 00:30:54,360
Speaker 2: how is this different? Like what makes this so different?

563
00:30:54,520 --> 00:30:56,600
Speaker 1: I want to walk through the rest of the timeline

564
00:30:56,720 --> 00:30:58,760
Speaker 1: and get to the testimony here, because that is a

565
00:30:58,760 --> 00:31:03,840
Speaker 1: good question. So the organization, the Information Commissioner's Office was

566
00:31:03,880 --> 00:31:06,560
Speaker 1: given the search warrant by the High Court judge to

567
00:31:06,760 --> 00:31:10,560
Speaker 1: determine whether the group tampered with Brexit, as Matt as

568
00:31:10,560 --> 00:31:14,240
Speaker 1: you just brought up, and they are currently still analyzing

569
00:31:14,280 --> 00:31:18,840
Speaker 1: and considering the evidence. Right on March twenty fifth, Zuckerberg,

570
00:31:19,200 --> 00:31:21,560
Speaker 1: who has become at this point known as the Zuck

571
00:31:22,000 --> 00:31:24,880
Speaker 1: in a lot of online forums, takes out a full

572
00:31:24,920 --> 00:31:30,040
Speaker 1: page ad in multiple UK US papers. It's a profound apology,

573
00:31:30,080 --> 00:31:33,360
Speaker 1: repeats his three step plan. The FTC the next day

574
00:31:33,440 --> 00:31:37,400
Speaker 1: confirms that they're investigating Facebook for these privacy practices. On

575
00:31:37,440 --> 00:31:41,480
Speaker 1: the twenty seventh, Zuckerberg refuses to meet with British lawmakers.

576
00:31:41,480 --> 00:31:44,120
Speaker 1: He sends one of his employees, but he does agree

577
00:31:44,120 --> 00:31:49,400
Speaker 1: to meet with US Congress and while Britain is investigating

578
00:31:49,440 --> 00:31:53,480
Speaker 1: this Wiley that whistleblower from earlier tells Parliament that Facebook

579
00:31:53,600 --> 00:31:56,800
Speaker 1: uses microphones to improve ad targeting, which we've talked about

580
00:31:56,840 --> 00:31:58,880
Speaker 1: before on this show. Yes, what do you guys think?

581
00:31:58,880 --> 00:32:00,719
Speaker 1: Do you think that's a real thing.

582
00:32:00,200 --> 00:32:00,960
Speaker 2: Like on your phone?

583
00:32:01,120 --> 00:32:01,280
Speaker 1: Yeah?

584
00:32:01,360 --> 00:32:03,720
Speaker 2: Yeah, yeah, I've certainly and we all have I think

585
00:32:03,760 --> 00:32:06,440
Speaker 2: had a situation where we were chatting about something and

586
00:32:06,480 --> 00:32:08,400
Speaker 2: then got served with an AD about it the next

587
00:32:08,480 --> 00:32:11,400
Speaker 2: day or later. I mean, I yeah, I think it's

588
00:32:11,480 --> 00:32:12,320
Speaker 2: highly plausible.

589
00:32:12,440 --> 00:32:16,680
Speaker 3: Yeah, I'm utterly convinced. Even though I have not seen absolute,

590
00:32:17,520 --> 00:32:19,880
Speaker 3: like damning evidence, I'm convinced.

591
00:32:20,160 --> 00:32:23,360
Speaker 1: I you know, we have to ask ourselves at what

592
00:32:23,560 --> 00:32:29,120
Speaker 1: threshold can the sheer number of anecdotes and firsthand experiences

593
00:32:29,680 --> 00:32:33,479
Speaker 1: be accepted as as you said, met damning evidence. I

594
00:32:33,520 --> 00:32:36,520
Speaker 1: think we're close to that threshold. There's there's something we

595
00:32:36,560 --> 00:32:39,440
Speaker 1: should fast forward to. On April fourth, Buried in a

596
00:32:39,480 --> 00:32:43,240
Speaker 1: blog post about changes Facebook is making to quote clarify

597
00:32:43,720 --> 00:32:46,760
Speaker 1: in terms of service. You like that Clarify not correct.

598
00:32:47,320 --> 00:32:50,400
Speaker 1: The company casually increases the number of people affected by

599
00:32:50,400 --> 00:32:53,080
Speaker 1: Cambridge Analyticas' efforts from fifty million to.

600
00:32:53,920 --> 00:32:55,640
Speaker 3: Eighty seven million.

601
00:32:55,960 --> 00:32:57,560
Speaker 2: Okay, just a couple more.

602
00:32:58,160 --> 00:32:59,840
Speaker 1: Yeah, just a few.

603
00:32:59,680 --> 00:33:04,640
Speaker 3: Dollars, yes, almost half again, These these are individuals that

604
00:33:04,680 --> 00:33:07,320
Speaker 3: were compromised, right yeah, yeah, yeah.

605
00:33:07,360 --> 00:33:09,600
Speaker 1: And they do go out of their way to say people,

606
00:33:09,760 --> 00:33:12,480
Speaker 1: not profiles because they think their PR team is working

607
00:33:12,520 --> 00:33:15,160
Speaker 1: around the clock to humanize Facebook.

608
00:33:15,160 --> 00:33:17,920
Speaker 2: And do you think they've called out like a lot

609
00:33:17,920 --> 00:33:20,480
Speaker 2: of it. We talked about fake profiles and bots and stuff.

610
00:33:20,800 --> 00:33:23,160
Speaker 2: I mean, how do we know that number? Like, how

611
00:33:23,160 --> 00:33:26,280
Speaker 2: many in that number do you think could be fake profiles?

612
00:33:26,320 --> 00:33:27,920
Speaker 2: And like, I don't know, do.

613
00:33:27,920 --> 00:33:30,760
Speaker 3: You remember the number of fake profiles from the top. No,

614
00:33:30,960 --> 00:33:34,400
Speaker 3: it was house high eighty three million. So what if

615
00:33:34,440 --> 00:33:36,680
Speaker 3: these are all fake? No, they're not all fake, It's

616
00:33:36,720 --> 00:33:39,239
Speaker 3: just it's a proportion. I'm sorry. I just I think

617
00:33:39,240 --> 00:33:42,160
Speaker 3: it'd be hilarious if they only got fake profiles.

618
00:33:41,640 --> 00:33:43,120
Speaker 1: That would be you know what, that would be a

619
00:33:43,160 --> 00:33:48,440
Speaker 1: relief for everyone involved, except for the bot masters. Oh yeah,

620
00:33:48,480 --> 00:33:52,600
Speaker 1: but they continue to their credit. Facebook suspends two more

621
00:33:52,640 --> 00:33:56,720
Speaker 1: companies doing similar stuff, one called CBEU and one called

622
00:33:56,840 --> 00:34:00,719
Speaker 1: aggregate IQ, and then they start in four users who

623
00:34:00,760 --> 00:34:03,440
Speaker 1: are affected by the breach. And folks, you're listening to

624
00:34:03,480 --> 00:34:06,040
Speaker 1: this today, the odds are you probably have some sort

625
00:34:06,080 --> 00:34:08,680
Speaker 1: of Facebook profile, and if you have some sort of

626
00:34:08,680 --> 00:34:16,799
Speaker 1: Facebook profile, the odds are not insanely low. It's it's

627
00:34:16,800 --> 00:34:20,520
Speaker 1: not super likely, but it's it's quite possible that you

628
00:34:20,600 --> 00:34:24,120
Speaker 1: opened your Facebook one day and you saw a notice

629
00:34:24,160 --> 00:34:26,759
Speaker 1: that says that you may have been affected by this

630
00:34:27,200 --> 00:34:31,799
Speaker 1: because either you or a friend used the app This

631
00:34:31,840 --> 00:34:32,880
Speaker 1: is your Digital Life.

632
00:34:33,680 --> 00:34:36,520
Speaker 3: Oh, this is your Digital life.

633
00:34:36,560 --> 00:34:38,759
Speaker 2: That sounds like a soap opera or something for.

634
00:34:38,760 --> 00:34:39,600
Speaker 3: Like the future.

635
00:34:40,640 --> 00:34:44,840
Speaker 1: Yeah, oh man, or a Black Mirror episode, right.

636
00:34:44,719 --> 00:34:46,600
Speaker 2: That'd be a good one. I think, yeah, we should

637
00:34:46,640 --> 00:34:48,200
Speaker 2: pocket that at that.

638
00:34:48,800 --> 00:34:52,839
Speaker 1: So they appear to have also harvested not just the

639
00:34:53,760 --> 00:34:56,600
Speaker 1: what we will call like the metadata, the connections, the

640
00:34:56,680 --> 00:34:59,239
Speaker 1: time of log in, the time spent somewhere, but they've

641
00:34:59,280 --> 00:35:02,600
Speaker 1: also are did content, private messages.

642
00:35:02,719 --> 00:35:06,120
Speaker 2: Were harvested again those things. Isn't that That's dark to me?

643
00:35:06,320 --> 00:35:07,320
Speaker 2: That's really creepy.

644
00:35:07,400 --> 00:35:08,800
Speaker 1: The other synonym is scraped.

645
00:35:09,400 --> 00:35:10,480
Speaker 3: Yeah, I've heard that a lot.

646
00:35:10,600 --> 00:35:14,080
Speaker 1: Yeah. So they said a small number of people who

647
00:35:14,120 --> 00:35:18,840
Speaker 1: logged into This is Your Digital Life also shared their newsfeed, timeline,

648
00:35:18,880 --> 00:35:22,400
Speaker 1: post and messages, which may have included posting messages from you.

649
00:35:22,880 --> 00:35:26,920
Speaker 1: That's according to Cambridge Analytic and Facebook. They also gained

650
00:35:26,920 --> 00:35:29,880
Speaker 1: access to information from the friends of the apps users

651
00:35:30,280 --> 00:35:35,680
Speaker 1: and it's still, honestly is not clear how many users

652
00:35:36,080 --> 00:35:37,400
Speaker 1: were affected.

653
00:35:37,480 --> 00:35:39,799
Speaker 2: What it's like a disease. It's like it's it can

654
00:35:39,840 --> 00:35:41,840
Speaker 2: contract it from others.

655
00:35:42,040 --> 00:35:47,560
Speaker 1: It's a vector from contact and astute listeners. You noticed

656
00:35:47,640 --> 00:35:53,239
Speaker 1: earlier that Mark Zuckerberg himself refused to go to British lawmakers,

657
00:35:53,480 --> 00:35:57,319
Speaker 1: but did consent to go to US lawmakers. And we

658
00:35:57,320 --> 00:35:59,840
Speaker 1: were kicking this around a little bit off air. Why

659
00:35:59,880 --> 00:36:00,719
Speaker 1: do you think that is?

660
00:36:01,520 --> 00:36:04,960
Speaker 3: Well, I mean, he spent two days giving testimony to

661
00:36:05,920 --> 00:36:10,840
Speaker 3: the United States lawmakers. It sounds so much better when

662
00:36:10,880 --> 00:36:14,200
Speaker 3: you call them lawmakers instead of just you know, the

663
00:36:14,280 --> 00:36:17,920
Speaker 3: House or Congress. But anyway, yeah, he spent two days

664
00:36:17,960 --> 00:36:20,759
Speaker 3: there and he had not much of a problem at all,

665
00:36:20,880 --> 00:36:24,479
Speaker 3: fielding questions, like you said, Ben, from people who had

666
00:36:24,680 --> 00:36:29,520
Speaker 3: barely a grasp on what an online advertising is, or

667
00:36:29,560 --> 00:36:34,280
Speaker 3: what an app actually is, or how data transfers occur.

668
00:36:34,560 --> 00:36:37,080
Speaker 2: And correct me if I'm wrong. But it's my understanding

669
00:36:37,160 --> 00:36:43,560
Speaker 2: that British lawmakers do more so than American lawmakers, or

670
00:36:43,600 --> 00:36:47,120
Speaker 2: at least they know how to dig deep and put

671
00:36:47,160 --> 00:36:49,359
Speaker 2: people's feet to the fire, maybe a little bit more

672
00:36:49,360 --> 00:36:50,600
Speaker 2: than we did. It felt like this was a bit

673
00:36:50,640 --> 00:36:52,800
Speaker 2: of a softball kind of situation.

674
00:36:52,920 --> 00:36:57,439
Speaker 1: Oh, agreed. Yeah. And this goes into maybe journalistic culture too,

675
00:36:57,520 --> 00:37:03,040
Speaker 1: because BBC reporters are generally thought to be harder hitting

676
00:37:03,120 --> 00:37:07,319
Speaker 1: as journalists. I've got a short, just a couple of

677
00:37:07,440 --> 00:37:12,160
Speaker 1: choice questions, Yes, lawmakers asked Mark Zuckerberg. Would you like

678
00:37:12,200 --> 00:37:12,640
Speaker 1: to hear something?

679
00:37:12,719 --> 00:37:13,240
Speaker 3: Yes? Please?

680
00:37:13,280 --> 00:37:13,600
Speaker 2: Please?

681
00:37:13,719 --> 00:37:20,120
Speaker 1: Okay. Senator Lindsey Graham from South Carolina asked, Mark Zuckerberg,

682
00:37:20,480 --> 00:37:22,080
Speaker 1: is Twitter the same as what you do?

683
00:37:23,760 --> 00:37:26,600
Speaker 3: Lindsey Graham asking more like Lindsey Grandpa?

684
00:37:26,719 --> 00:37:27,279
Speaker 2: Am I right?

685
00:37:27,440 --> 00:37:27,800
Speaker 3: Home?

686
00:37:27,840 --> 00:37:35,000
Speaker 1: Boy? Florida Senator Bill Nelson is a fond of a

687
00:37:35,080 --> 00:37:37,680
Speaker 1: particular type of chocolate and he had mentioned that fact

688
00:37:37,680 --> 00:37:39,920
Speaker 1: to some Facebook friends, and now he's seen ads for

689
00:37:40,000 --> 00:37:42,239
Speaker 1: that chocolate. So he said, what if I don't want

690
00:37:42,280 --> 00:37:44,280
Speaker 1: to receive ads for chocolate?

691
00:37:46,400 --> 00:37:48,320
Speaker 3: Yeah?

692
00:37:48,360 --> 00:37:51,960
Speaker 1: And then Senator Roy Blunt from Missouri says, my son

693
00:37:52,080 --> 00:37:54,600
Speaker 1: is dedicated to Instagram, so he'd want to be sure

694
00:37:54,640 --> 00:37:57,600
Speaker 1: I mentioned him while I was here with you. It's

695
00:37:57,640 --> 00:37:58,400
Speaker 1: not even a question.

696
00:37:58,640 --> 00:38:01,880
Speaker 2: Can I can we can? I? Can you sign this

697
00:38:02,440 --> 00:38:04,680
Speaker 2: for me, Mark Zuckerberg, for my grandson?

698
00:38:04,920 --> 00:38:05,120
Speaker 3: Yeah?

699
00:38:05,160 --> 00:38:08,600
Speaker 2: Man, good lord? How much more? This is comically softball

700
00:38:09,200 --> 00:38:12,080
Speaker 2: and again like I mean, you know, there's probably a

701
00:38:12,120 --> 00:38:15,319
Speaker 2: committee that like knows how the internet works or why

702
00:38:15,400 --> 00:38:17,080
Speaker 2: why why aren't the FCC involved.

703
00:38:17,120 --> 00:38:17,480
Speaker 1: I don't know.

704
00:38:17,480 --> 00:38:20,200
Speaker 2: I just it just seems very like what's the word,

705
00:38:20,320 --> 00:38:23,000
Speaker 2: just a lot of pageantry and no real like yeah,

706
00:38:23,080 --> 00:38:24,279
Speaker 2: like action.

707
00:38:24,400 --> 00:38:26,319
Speaker 1: Like a lot like a lot of theater. And you

708
00:38:26,400 --> 00:38:32,600
Speaker 1: can you can read in depth some of these some

709
00:38:32,680 --> 00:38:36,600
Speaker 1: of these questions. Matt, you really took the you really

710
00:38:36,600 --> 00:38:40,360
Speaker 1: took the took one for the team because you watched

711
00:38:40,400 --> 00:38:41,040
Speaker 1: a lot of this.

712
00:38:41,440 --> 00:38:44,400
Speaker 3: I did. I started watching the first day and I

713
00:38:44,440 --> 00:38:47,520
Speaker 3: got lost. After about forty minutes, I just couldn't. It

714
00:38:47,560 --> 00:38:50,480
Speaker 3: became noise and I just shut it down.

715
00:38:50,520 --> 00:38:52,120
Speaker 2: Can you do an impression of Zuckerberg?

716
00:38:52,880 --> 00:38:54,640
Speaker 3: I don't know that I can. I was listening to

717
00:38:54,719 --> 00:38:57,200
Speaker 3: MPR and they were kind of summing up some of

718
00:38:57,239 --> 00:39:00,320
Speaker 3: the major parts and I heard him say, this is

719
00:39:00,360 --> 00:39:02,680
Speaker 3: a quote, so I guess I'll try it here. We

720
00:39:02,719 --> 00:39:05,880
Speaker 3: didn't take a broad enough view of our responsibility and

721
00:39:06,000 --> 00:39:08,840
Speaker 3: that was a big mistake and it was my mistake,

722
00:39:09,160 --> 00:39:12,520
Speaker 3: and I'm sorry. I started Facebook, I run it, and

723
00:39:12,560 --> 00:39:14,439
Speaker 3: I'm responsible for what happens.

724
00:39:14,120 --> 00:39:20,280
Speaker 2: Here, and I swear I am human. Yes, I swear, hello, friend,

725
00:39:21,160 --> 00:39:21,640
Speaker 2: it's just.

726
00:39:21,800 --> 00:39:25,040
Speaker 3: The responses that were given to a lot of the

727
00:39:25,080 --> 00:39:27,799
Speaker 3: major things. As you can expect since he's under oath,

728
00:39:28,600 --> 00:39:33,480
Speaker 3: he is, he's on the surface. Everything is on the surface.

729
00:39:33,200 --> 00:39:35,280
Speaker 2: And he just seems pretty surfacy in general.

730
00:39:35,719 --> 00:39:38,279
Speaker 3: Oh and I don't know. I don't know much about

731
00:39:38,320 --> 00:39:40,520
Speaker 3: the zuck Man. I feel like when I talk about

732
00:39:41,040 --> 00:39:44,640
Speaker 3: the zuck I have this bit of envy just about

733
00:39:44,680 --> 00:39:47,200
Speaker 3: how young and successful and.

734
00:39:47,160 --> 00:39:48,960
Speaker 2: Really you think he's happy that I think he'd feel

735
00:39:49,040 --> 00:39:53,040
Speaker 2: really isolated and I don't know, you know, more money,

736
00:39:53,080 --> 00:39:55,480
Speaker 2: more problems than he's just trying to connect the world.

737
00:39:55,920 --> 00:39:58,600
Speaker 1: Well, Mark, if you would like to hop on our

738
00:39:58,680 --> 00:40:02,600
Speaker 1: show in the future for an interview, or if you

739
00:40:02,800 --> 00:40:06,160
Speaker 1: just need some actual friends to maybe go bowling with

740
00:40:06,200 --> 00:40:07,319
Speaker 1: you or something, let us know.

741
00:40:07,360 --> 00:40:09,480
Speaker 2: Hit us up on Facebook where we are conspiracies.

742
00:40:10,880 --> 00:40:14,120
Speaker 3: Yes, just so I will say one last thing, Yeah,

743
00:40:14,120 --> 00:40:17,520
Speaker 3: there was one one, even though it's kind of a

744
00:40:17,560 --> 00:40:21,319
Speaker 3: low blow shot. Senator Dick Durban at one point of

745
00:40:21,520 --> 00:40:24,360
Speaker 3: all of a sudden, asked Mark Zuckerberg what hotel he

746
00:40:24,440 --> 00:40:26,680
Speaker 3: was staying at. He's like, well, so, what hotel are

747
00:40:26,719 --> 00:40:28,200
Speaker 3: you staying at while you're here?

748
00:40:28,280 --> 00:40:30,440
Speaker 1: Would you be comfortable revealing that information.

749
00:40:30,520 --> 00:40:33,120
Speaker 3: Yeah, exactly, And you know, Mark of course is like, no,

750
00:40:33,239 --> 00:40:37,759
Speaker 3: absolutely not, because again, all of the information sharing here

751
00:40:37,880 --> 00:40:39,440
Speaker 3: that we're talking about is voluntary.

752
00:40:40,960 --> 00:40:41,360
Speaker 1: Yep.

753
00:40:42,160 --> 00:40:45,040
Speaker 2: And as a point, he has a point. It's an

754
00:40:45,040 --> 00:40:47,960
Speaker 2: interesting argument because a lot of this is based on

755
00:40:48,400 --> 00:40:51,800
Speaker 2: the inherent problems of social media. But it's a contract

756
00:40:51,800 --> 00:40:55,440
Speaker 2: that we all sign on for and we accept the

757
00:40:55,480 --> 00:40:56,960
Speaker 2: trade off, you know.

758
00:40:57,120 --> 00:40:59,960
Speaker 1: Because surely we all took the time to read the

759
00:41:00,239 --> 00:41:02,239
Speaker 1: terms of agreement in full.

760
00:41:02,360 --> 00:41:05,440
Speaker 2: Now, Ben, I would think that's possible, mind you possible

761
00:41:05,480 --> 00:41:06,560
Speaker 2: that you have.

762
00:41:08,200 --> 00:41:11,480
Speaker 1: It is quite possible. But one thing that is impossible

763
00:41:11,520 --> 00:41:13,920
Speaker 1: for us to do now is to pretend that this

764
00:41:14,040 --> 00:41:16,799
Speaker 1: is not an ongoing thing, or pretend that there's a

765
00:41:16,920 --> 00:41:20,200
Speaker 1: full number, a full solid number of people that we

766
00:41:20,280 --> 00:41:24,360
Speaker 1: know have been impacted. We can't predict the future, but

767
00:41:24,440 --> 00:41:30,720
Speaker 1: what we can do is explore the troubling implications. After

768
00:41:30,840 --> 00:41:39,600
Speaker 1: that is a word from our sponsor. Here's where it

769
00:41:39,640 --> 00:41:45,080
Speaker 1: gets crazy. This granted, even though the three of us

770
00:41:45,080 --> 00:41:49,879
Speaker 1: are hilarious, sounds kind of dry, you know what I mean.

771
00:41:50,520 --> 00:41:54,840
Speaker 1: But that's because we're at the beginning of a much

772
00:41:54,880 --> 00:41:57,800
Speaker 1: longer story and a much larger revelation It's a story

773
00:41:57,800 --> 00:42:02,560
Speaker 1: that's been going on untold silently for you know, since

774
00:42:02,600 --> 00:42:06,920
Speaker 1: shortly after Facebook was opened to anyone who wanted to

775
00:42:06,960 --> 00:42:10,200
Speaker 1: log in. And while those investigations are ongoing, it's safe

776
00:42:10,200 --> 00:42:13,680
Speaker 1: to say more and more unpleasant discoveries linger on the

777
00:42:13,680 --> 00:42:17,120
Speaker 1: horizon waiting to be discovered. One of the first questions

778
00:42:17,200 --> 00:42:22,520
Speaker 1: is what other operations are occurring at this point. We

779
00:42:22,600 --> 00:42:27,440
Speaker 1: don't know. Very few people actually know. Like just before

780
00:42:27,480 --> 00:42:30,239
Speaker 1: we hopped in the studio today, a few hours ago,

781
00:42:30,719 --> 00:42:33,800
Speaker 1: it was revealed that Cambridge Analytica was planning to launch

782
00:42:33,840 --> 00:42:35,400
Speaker 1: its own cryptocurrency.

783
00:42:35,800 --> 00:42:38,880
Speaker 3: Yep, they want to raise money, don't they want? They

784
00:42:38,880 --> 00:42:40,799
Speaker 3: want to raise money to pay for the creation of

785
00:42:40,840 --> 00:42:44,600
Speaker 3: a system to help people store and sell their online

786
00:42:44,600 --> 00:42:47,360
Speaker 3: personal data to advertisers.

787
00:42:47,640 --> 00:42:48,120
Speaker 2: Oh, come on.

788
00:42:48,680 --> 00:42:53,080
Speaker 1: True story called dragon Coin, after a famous gambler in Macau.

789
00:42:53,200 --> 00:42:56,120
Speaker 2: But I thought we agreed that, like while in aggregate,

790
00:42:56,280 --> 00:42:59,319
Speaker 2: on the whole, our data is worth a lot. My

791
00:42:59,520 --> 00:43:01,680
Speaker 2: data is not worth a lot. Your data is not

792
00:43:01,719 --> 00:43:05,040
Speaker 2: worth a lot. I couldn't sell my data and have

793
00:43:05,120 --> 00:43:07,200
Speaker 2: it made. I mean, maybe I'm correct me if I'm wrong.

794
00:43:07,239 --> 00:43:10,560
Speaker 1: Oh no, they're using The thing is they were hoping

795
00:43:10,640 --> 00:43:14,759
Speaker 1: to build a system that would combat the same kind

796
00:43:14,800 --> 00:43:18,480
Speaker 1: of activities they did, the idea of being that who

797
00:43:18,480 --> 00:43:20,400
Speaker 1: knows better how to build a hen house than the

798
00:43:20,440 --> 00:43:21,760
Speaker 1: fox who got fat?

799
00:43:22,120 --> 00:43:25,160
Speaker 2: Okay, that's interesting, I understand what you're saying. Almost like

800
00:43:25,200 --> 00:43:27,320
Speaker 2: a password wallet or something like that, or like a

801
00:43:27,440 --> 00:43:30,640
Speaker 2: vault that you can keep your stuff under wraps.

802
00:43:30,800 --> 00:43:34,560
Speaker 1: Which would also be supervised by Cambridge Analytica and their

803
00:43:34,560 --> 00:43:40,040
Speaker 1: parent company. There's this other question, which is is it

804
00:43:40,080 --> 00:43:44,319
Speaker 1: that bad? What gives you know? The effort for this

805
00:43:44,600 --> 00:43:50,759
Speaker 1: was overseen by their former CEO, the disgraced chief executive,

806
00:43:50,800 --> 00:43:53,719
Speaker 1: a guy named Alexander Nix. He was forcedyle of the

807
00:43:53,760 --> 00:43:56,240
Speaker 1: company in March of this year because he was caught

808
00:43:56,280 --> 00:43:59,439
Speaker 1: on tape bragging about his company's approach to political work

809
00:43:59,600 --> 00:44:03,680
Speaker 1: in their countries across the world, including Eugene shell companies

810
00:44:03,719 --> 00:44:10,200
Speaker 1: and strategies to entrap opponents. Yeah. They they famously state,

811
00:44:10,239 --> 00:44:13,600
Speaker 1: and this is Cambridge themselves, that their database contains over

812
00:44:13,640 --> 00:44:17,680
Speaker 1: five thousand data points on nearly every American consumer. And

813
00:44:17,960 --> 00:44:20,960
Speaker 1: if you do some due diligence like NOL did, then

814
00:44:21,000 --> 00:44:22,880
Speaker 1: you will see that a lot of it seems kind

815
00:44:22,920 --> 00:44:23,520
Speaker 1: of innocuous.

816
00:44:23,640 --> 00:44:24,880
Speaker 2: That's what I was talking about. Earlier, I think I

817
00:44:24,920 --> 00:44:26,920
Speaker 2: jumped the gun a little bit and talking about that

818
00:44:27,000 --> 00:44:29,759
Speaker 2: NPR piece I heard where a gentleman was able to

819
00:44:29,840 --> 00:44:33,160
Speaker 2: obtain his Cambridge Analytica profile, let's call it, I think,

820
00:44:33,160 --> 00:44:37,719
Speaker 2: I said, dossier. And he was pretty sure that it

821
00:44:37,840 --> 00:44:40,279
Speaker 2: wasn't all of it, because I certainly wasn't that five

822
00:44:40,360 --> 00:44:43,760
Speaker 2: thousand points. But it was, you know, a pretty basic

823
00:44:44,080 --> 00:44:47,239
Speaker 2: little collection of data points that, yes, you could absolutely

824
00:44:47,280 --> 00:44:50,839
Speaker 2: glean from just kind of trolling someone's feed, their their

825
00:44:50,840 --> 00:44:53,440
Speaker 2: their page, you know, their wall, all the kinds like

826
00:44:53,600 --> 00:44:55,640
Speaker 2: is this person a Republican or a Democrat? You know,

827
00:44:55,719 --> 00:44:59,359
Speaker 2: most likely are they? What kind of income bracket are they?

828
00:44:59,360 --> 00:45:00,920
Speaker 2: And this is also if you could kind of figure

829
00:45:00,920 --> 00:45:03,040
Speaker 2: out by looking at pictures and seeing the kinds of

830
00:45:03,040 --> 00:45:06,680
Speaker 2: stuff people actually post about themselves already, but then much

831
00:45:06,719 --> 00:45:09,560
Speaker 2: fewer than five thousand, and he was pretty sure that

832
00:45:09,640 --> 00:45:12,840
Speaker 2: he didn't they weren't going to give him all five thousand.

833
00:45:13,239 --> 00:45:15,880
Speaker 1: Yeah, I mean be stuff that I love that point

834
00:45:15,880 --> 00:45:19,400
Speaker 1: you make about a lot of this stuff being things

835
00:45:19,440 --> 00:45:23,239
Speaker 1: that you or I could find just by looking at

836
00:45:23,239 --> 00:45:25,560
Speaker 1: someone's page without even being their Facebook friends.

837
00:45:25,600 --> 00:45:28,080
Speaker 2: Well, it's not my point. At Zuckerberg's point, he said,

838
00:45:28,120 --> 00:45:32,080
Speaker 2: that to US lawmakers. That was sort of his defense, right,

839
00:45:32,320 --> 00:45:33,759
Speaker 2: I mean, and that's why I think this is a

840
00:45:33,800 --> 00:45:36,640
Speaker 2: really interesting question that you raised, Ben. Isn't that bad?

841
00:45:36,680 --> 00:45:39,160
Speaker 2: And I want to know what's the difference between the

842
00:45:39,160 --> 00:45:42,120
Speaker 2: stuff we share and what Cambridge Analytica is doing with

843
00:45:42,239 --> 00:45:44,520
Speaker 2: this stuff they're harvesting from us.

844
00:45:44,760 --> 00:45:50,480
Speaker 1: Yes, okay, it becomes murky and dangerous when it goes

845
00:45:50,520 --> 00:45:55,560
Speaker 1: into political influence. So Cambridge Analytica did use this information,

846
00:45:55,680 --> 00:46:01,200
Speaker 1: this data to influence elections. Per Matthew oskal Ski, at

847
00:46:01,200 --> 00:46:06,000
Speaker 1: Cambridge Analytica, we break up our data into three buckets, political, commercial,

848
00:46:06,000 --> 00:46:08,480
Speaker 1: and first party. We work with several of the main

849
00:46:08,640 --> 00:46:11,840
Speaker 1: voter file providers depending on the preference of our clients

850
00:46:12,120 --> 00:46:16,120
Speaker 1: to access vote history and voter profiles. And then they

851
00:46:16,480 --> 00:46:20,200
Speaker 1: couple that with top commercial data providers for on a

852
00:46:20,239 --> 00:46:26,240
Speaker 1: licensing basis that stuff like general demographics, geographics, purchase history,

853
00:46:26,440 --> 00:46:28,960
Speaker 1: interest and so on. Then they get R and D

854
00:46:29,160 --> 00:46:35,800
Speaker 1: project data, internal surveys, research what they call exclusive data relationships, which,

855
00:46:36,160 --> 00:46:40,120
Speaker 1: as you know, in this kind of conversation, the most

856
00:46:40,840 --> 00:46:45,439
Speaker 1: blase phrase is usually hiding the strangest things, and data

857
00:46:45,440 --> 00:46:48,800
Speaker 1: collection through direct response projects, which is actually asking people

858
00:46:48,800 --> 00:46:53,640
Speaker 1: what they think. Whistleblowers, not just wiy alleged that Cambridge

859
00:46:53,640 --> 00:46:57,000
Speaker 1: worked with Steve Bannon to sway the vote starting as

860
00:46:57,080 --> 00:47:01,480
Speaker 1: far back as twenty fourteen. And this is not really

861
00:47:02,440 --> 00:47:07,040
Speaker 1: a big deal, does it seem? It's not unique. Everybody

862
00:47:07,440 --> 00:47:11,279
Speaker 1: we ever meet is going to try to persuade us

863
00:47:11,320 --> 00:47:15,400
Speaker 1: to do something without falling into relativism, right, Like, the

864
00:47:15,560 --> 00:47:18,759
Speaker 1: three of us are pretty opinionated people, and we're trying

865
00:47:18,800 --> 00:47:22,280
Speaker 1: to persuade each other's stuff all the time. Usually we agree,

866
00:47:22,640 --> 00:47:27,279
Speaker 1: but if it's a larger company, that doesn't make that

867
00:47:27,440 --> 00:47:33,800
Speaker 1: impulse itself inherently nefarious. Until we get to what Nick's

868
00:47:34,160 --> 00:47:35,160
Speaker 1: actually said.

869
00:47:35,480 --> 00:47:39,560
Speaker 3: Yeah, he was secretly recorded being a bit of a braggadocia,

870
00:47:40,480 --> 00:47:45,200
Speaker 3: a bragadouche. I try to squeeze a line in there somehow,

871
00:47:45,480 --> 00:47:49,319
Speaker 3: but yeah, he was bragging about how his firm, Camerge Analytico,

872
00:47:49,440 --> 00:47:54,279
Speaker 3: was largely responsible for the election of Donald Trump as

873
00:47:54,360 --> 00:47:56,680
Speaker 3: President of the United States. He described all kinds of

874
00:47:56,760 --> 00:48:02,880
Speaker 3: questionable practices they used to influence foreign elections. Because remember

875
00:48:02,880 --> 00:48:06,280
Speaker 3: this is a British firm and they're influencing the American election.

876
00:48:06,760 --> 00:48:10,480
Speaker 3: Why I thought Russia was influencing the election. You might say, oh, well,

877
00:48:10,719 --> 00:48:14,040
Speaker 3: maybe it was also our friends over in Britain.

878
00:48:14,160 --> 00:48:16,240
Speaker 2: I mean, look, I just want to say I pulled

879
00:48:16,320 --> 00:48:19,960
Speaker 2: up this press release from Cambridge Analytica's website and they

880
00:48:20,080 --> 00:48:23,000
Speaker 2: lay out a few points here. One no laws were broken.

881
00:48:23,480 --> 00:48:26,680
Speaker 2: Cambridge Analytica did not hack Facebook. Two, Cambridge Analytica did

882
00:48:26,680 --> 00:48:29,399
Speaker 2: not use the data or any derivatives of this data

883
00:48:29,400 --> 00:48:33,080
Speaker 2: in the US presidential election. Three Cambridge Analytica did not

884
00:48:33,200 --> 00:48:36,840
Speaker 2: work at all on the Brexit referendum. For mister Wiley

885
00:48:36,920 --> 00:48:39,439
Speaker 2: is not a whistleblower. It goes on well.

886
00:48:40,800 --> 00:48:44,720
Speaker 3: In this secret recording of Alexander Nicks, the former chief

887
00:48:44,760 --> 00:48:48,279
Speaker 3: executive of Cambridge Analytica, he said that his firm did

888
00:48:48,320 --> 00:48:51,560
Speaker 3: all the research, analytics, and targeting of voters for Trump's

889
00:48:51,560 --> 00:48:54,520
Speaker 3: digital and TV campaigns. He was recorded saying.

890
00:48:54,320 --> 00:48:57,040
Speaker 1: That He also said that he had met personally met

891
00:48:57,080 --> 00:49:00,239
Speaker 1: Trump when he was the candidate many times, and that

892
00:49:00,280 --> 00:49:05,040
Speaker 1: they deployed dirty tricks, including old school espionage stuff like honeytraps,

893
00:49:05,040 --> 00:49:08,960
Speaker 1: setting someone up with a sex worker prostitute, fake news campaigns,

894
00:49:09,000 --> 00:49:13,600
Speaker 1: fake bribery scandals, operations with ex spies and espionage agents

895
00:49:13,640 --> 00:49:17,680
Speaker 1: to swing election campaigns, and this was recorded by an

896
00:49:17,760 --> 00:49:21,320
Speaker 1: underground reporter working for The Observer. What they did is

897
00:49:21,360 --> 00:49:23,799
Speaker 1: they posed as a member of a wealthy family from

898
00:49:23,840 --> 00:49:28,160
Speaker 1: Sri Lanka seeking political influence. And then when the reporter

899
00:49:29,239 --> 00:49:33,720
Speaker 1: as this wealthy Sri Lanka in person, when they asked

900
00:49:33,719 --> 00:49:38,200
Speaker 1: if Cambridge Analytica could offer investigations into damaging secrets of rivals,

901
00:49:38,239 --> 00:49:42,279
Speaker 1: digging up dirt. Essentially, Nick said that Cambridge worked with

902
00:49:42,440 --> 00:49:45,320
Speaker 1: former spies from Britain and Israel to look for dirt

903
00:49:45,360 --> 00:49:47,840
Speaker 1: and said they were doing it as they were speaking.

904
00:49:48,000 --> 00:49:50,600
Speaker 1: He also volunteered that his team was ready to go

905
00:49:51,000 --> 00:49:55,319
Speaker 1: further than just a keyboard investigation. He's like, I've got

906
00:49:55,320 --> 00:49:58,680
Speaker 1: one guy. He's a master disguise. We can send him.

907
00:49:58,760 --> 00:50:01,760
Speaker 1: He can be anybody. It's like, also, we can entrap

908
00:50:01,840 --> 00:50:05,520
Speaker 1: people with some sex workers, bring a few Ukrainians with

909
00:50:05,600 --> 00:50:07,040
Speaker 1: us on the trip, if you know what I mean,

910
00:50:07,200 --> 00:50:12,439
Speaker 1: have a pp party. That was the implication something something untoward, right,

911
00:50:13,040 --> 00:50:17,680
Speaker 1: And then he said that we do. He said, deep

912
00:50:17,680 --> 00:50:19,920
Speaker 1: digging is interesting, but you know it can be equally

913
00:50:19,920 --> 00:50:22,400
Speaker 1: effective to just go in and speak to the incumbents

914
00:50:22,440 --> 00:50:24,200
Speaker 1: and offer them a deal that's too good to be

915
00:50:24,280 --> 00:50:27,040
Speaker 1: true and make sure the video of it is recorded.

916
00:50:27,360 --> 00:50:31,400
Speaker 3: Wow, hence the dirty tricks and also okay, so according

917
00:50:31,400 --> 00:50:35,560
Speaker 3: to Wiley the whistleblower, he says, quote, we exploited facebooks

918
00:50:35,560 --> 00:50:38,600
Speaker 3: to harvest millions of people's profiles and built models to

919
00:50:38,719 --> 00:50:42,480
Speaker 3: exploit what we knew about them and target their inner demons.

920
00:50:42,600 --> 00:50:45,239
Speaker 3: That was the basis the entire company was built.

921
00:50:45,000 --> 00:50:48,799
Speaker 1: On, alleged whistleblower bro alleged Okay, that's what we have

922
00:50:48,880 --> 00:50:52,560
Speaker 1: to Oh, yeah, we're gonna get alleged heavy here. Allegedly heavy.

923
00:50:52,960 --> 00:50:54,879
Speaker 1: That's not a bad name for an album of some sort.

924
00:50:54,960 --> 00:50:55,920
Speaker 2: We should put that in the book.

925
00:50:56,040 --> 00:50:57,320
Speaker 1: Yeah, allegedly heavy.

926
00:50:57,400 --> 00:50:59,080
Speaker 3: What if it's heavy? Allegedly?

927
00:50:59,880 --> 00:51:04,040
Speaker 1: I feel like there's a good got a concept album here?

928
00:51:04,120 --> 00:51:04,960
Speaker 3: Now to discs.

929
00:51:06,400 --> 00:51:11,600
Speaker 1: But the question remains, like, what is what is wrong

930
00:51:11,719 --> 00:51:16,360
Speaker 1: with this in a legal sense? If we have signed

931
00:51:16,360 --> 00:51:19,279
Speaker 1: a contract, essentially, if we have assented to having this

932
00:51:20,000 --> 00:51:24,759
Speaker 1: data traded in exchange for using this free service, then

933
00:51:25,400 --> 00:51:30,000
Speaker 1: what's the big hubbub? The problem here is that it

934
00:51:30,080 --> 00:51:34,799
Speaker 1: was used without notifying Facebook users, and Facebook really does

935
00:51:34,840 --> 00:51:37,720
Speaker 1: not want this to be characterized as a data breach

936
00:51:38,239 --> 00:51:41,000
Speaker 1: for a number of reasons, one of which is a

937
00:51:41,080 --> 00:51:45,680
Speaker 1: majority of American states have laws requiring notification in different

938
00:51:45,680 --> 00:51:49,600
Speaker 1: cases of data breaches, including California, where Facebook is based.

939
00:51:49,680 --> 00:51:51,680
Speaker 2: Yeah, you've got one of the emails from your bank

940
00:51:51,760 --> 00:51:54,840
Speaker 2: or target or something saying, hey, you were part of

941
00:51:54,880 --> 00:51:59,319
Speaker 2: a potential bundle of a trillion users who may or

942
00:51:59,320 --> 00:52:02,400
Speaker 2: may not have had your information compromise. You should probably

943
00:52:02,440 --> 00:52:04,480
Speaker 2: change your password. They didn't just send that to you

944
00:52:04,480 --> 00:52:06,080
Speaker 2: out of the goodness of their heart, did they.

945
00:52:06,320 --> 00:52:11,000
Speaker 1: No. No, there's a lot of cya there, and as

946
00:52:11,000 --> 00:52:13,040
Speaker 1: we know, being a family show, that stands for cover

947
00:52:13,120 --> 00:52:16,440
Speaker 1: your actions or ardvark or cover your ardvark, which I

948
00:52:16,560 --> 00:52:21,239
Speaker 1: like better actually. And the ultimate goal of Cogan, who

949
00:52:21,560 --> 00:52:24,160
Speaker 1: worked at the University of Saint Petersburg, by the way

950
00:52:24,239 --> 00:52:28,280
Speaker 1: and receive Russian grant money, was not just to collect

951
00:52:28,280 --> 00:52:31,840
Speaker 1: this and build an an anonymous database. He was working

952
00:52:31,880 --> 00:52:36,400
Speaker 1: with Cambridge Analytica to build a database of identified profiles,

953
00:52:36,480 --> 00:52:39,319
Speaker 1: or what they would call matched profiles. They started with

954
00:52:39,360 --> 00:52:43,080
Speaker 1: the aim of making two million match profiles, meaning tied

955
00:52:43,120 --> 00:52:48,680
Speaker 1: to electoral registers in eleven states, with room to expand

956
00:52:48,760 --> 00:52:52,080
Speaker 1: much further, ultimately aiming to get all fifty states.

957
00:52:51,719 --> 00:52:56,000
Speaker 2: To what end to send them targeted messages about candidates.

958
00:52:55,640 --> 00:52:58,600
Speaker 3: Very specific, personalized targeted messages. Wow.

959
00:52:58,800 --> 00:53:03,200
Speaker 1: So like if you're working for if you're working for

960
00:53:03,360 --> 00:53:07,080
Speaker 1: the Republican Party or something that's your client, right, and

961
00:53:07,400 --> 00:53:12,880
Speaker 1: you want to split a vote, you could say, well,

962
00:53:13,040 --> 00:53:18,680
Speaker 1: here's some fake news about a Bernie Sanders or Hillary

963
00:53:18,719 --> 00:53:21,799
Speaker 1: Clinton or something. Or you could also put out some

964
00:53:22,080 --> 00:53:28,680
Speaker 1: fake or embarrassing news about a Republican rival and say, oh,

965
00:53:28,800 --> 00:53:33,399
Speaker 1: Senator Ted Cruz is I don't know, stealing peeps from

966
00:53:33,480 --> 00:53:34,880
Speaker 1: kids on Easter Sunday.

967
00:53:35,120 --> 00:53:37,320
Speaker 3: Yeah, And you can do it with a Facebook ad

968
00:53:38,000 --> 00:53:40,239
Speaker 3: Like you could buy a Facebook ad spot that only

969
00:53:40,320 --> 00:53:42,600
Speaker 3: targets one human person, right.

970
00:53:42,680 --> 00:53:44,319
Speaker 2: And I think another thing that came from this in

971
00:53:44,400 --> 00:53:47,520
Speaker 2: terms of Facebook changing their guidelines, they're going to be

972
00:53:47,600 --> 00:53:49,760
Speaker 2: much they say they're going to be much more strict

973
00:53:49,760 --> 00:53:53,640
Speaker 2: about selling political advertisements. That they're going to be much

974
00:53:53,680 --> 00:53:56,880
Speaker 2: more like knowing where the business is coming from or

975
00:53:56,920 --> 00:53:58,080
Speaker 2: something right this.

976
00:53:58,400 --> 00:54:02,080
Speaker 1: Yeah, And that's a tough question too, because you know,

977
00:54:02,120 --> 00:54:04,640
Speaker 1: it's very easy to vilify a Facebook No, but there's

978
00:54:05,280 --> 00:54:08,319
Speaker 1: it's similar to the problem with YouTube, like how can

979
00:54:08,400 --> 00:54:14,360
Speaker 1: you tube catch every falling sparrow of inappropriate stuff people posts?

980
00:54:14,440 --> 00:54:16,920
Speaker 2: The Wild West Man? You know, I bet you it's

981
00:54:16,920 --> 00:54:19,240
Speaker 2: going to come out in this that Facebook didn't really

982
00:54:19,280 --> 00:54:23,280
Speaker 2: break the law. That not not necessarily true of Cambridge Analytica,

983
00:54:23,320 --> 00:54:25,800
Speaker 2: but it doesn't seem like it's like the thing. It's like,

984
00:54:25,800 --> 00:54:28,239
Speaker 2: why would you do something if you're not if there's

985
00:54:28,280 --> 00:54:32,120
Speaker 2: no monetary h consequence in place for doing it, you

986
00:54:32,160 --> 00:54:34,640
Speaker 2: know what I mean? Like that's a regulations are all about, right, so,

987
00:54:34,880 --> 00:54:37,000
Speaker 2: but you also don't know how to make a regulation

988
00:54:37,120 --> 00:54:39,960
Speaker 2: until the case comes up where it's needed. And I

989
00:54:39,960 --> 00:54:42,799
Speaker 2: think that we're going to see that now. But I

990
00:54:42,880 --> 00:54:46,560
Speaker 2: do think that Facebook probably didn't break the law.

991
00:54:46,880 --> 00:54:50,920
Speaker 1: It's interesting because, you know, legislation lags so far behind

992
00:54:51,000 --> 00:54:54,480
Speaker 1: technological innovation, you know, which we've talked about in the

993
00:54:54,520 --> 00:54:57,880
Speaker 1: show before for long time listeners. But there's something else,

994
00:54:58,120 --> 00:55:02,160
Speaker 1: a big implication that a lot of people are just

995
00:55:02,280 --> 00:55:06,840
Speaker 1: now becoming aware of, and a real spooky one that

996
00:55:06,840 --> 00:55:10,600
Speaker 1: that might surprise some folks. And that is the question. Okay,

997
00:55:10,719 --> 00:55:13,319
Speaker 1: let's say I don't have a Facebook profile. Let's say

998
00:55:13,360 --> 00:55:17,279
Speaker 1: I've never logged on to Facebook or Instagram I know

999
00:55:17,320 --> 00:55:20,160
Speaker 1: their fingers on the same hand, and Twitter or whatever,

1000
00:55:20,280 --> 00:55:23,400
Speaker 1: and I didn't even I didn't even mess with MySpace

1001
00:55:23,480 --> 00:55:26,200
Speaker 1: or friends stirs how much could Facebook know about me?

1002
00:55:26,520 --> 00:55:29,480
Speaker 1: While they know a whole heck of a lot because

1003
00:55:29,880 --> 00:55:34,560
Speaker 1: of things that are called shadow profiles, called shadow profiles

1004
00:55:34,560 --> 00:55:36,480
Speaker 1: by literally everyone but Facebook.

1005
00:55:36,960 --> 00:55:41,600
Speaker 3: Yeah, so we're gonna call them shadow profiles because it's

1006
00:55:41,600 --> 00:55:45,239
Speaker 3: an awesome sounding, ominous thing. Here we go. You may

1007
00:55:45,280 --> 00:55:48,160
Speaker 3: have never started Facebook, right, you never even opened it up,

1008
00:55:48,200 --> 00:55:50,080
Speaker 3: not once, not a single time.

1009
00:55:50,239 --> 00:55:52,160
Speaker 1: But guess what, I hate books and faces.

1010
00:55:52,280 --> 00:55:55,279
Speaker 3: Yeah right, I'm with you, brother, but I'm off the grid. Matt.

1011
00:55:55,640 --> 00:55:57,560
Speaker 3: You are off the grid. But you've got this friend

1012
00:55:57,560 --> 00:55:59,880
Speaker 3: that works over at the Pigley Wiggily. Here's the problem.

1013
00:56:00,640 --> 00:56:03,560
Speaker 3: She loves Facebook. She's on it all the time, right,

1014
00:56:03,960 --> 00:56:06,760
Speaker 3: and you know, you guys, you're just Palso, you're texting

1015
00:56:06,800 --> 00:56:07,920
Speaker 3: every once in a while, and.

1016
00:56:07,840 --> 00:56:09,000
Speaker 2: I'm on my flip phone.

1017
00:56:09,000 --> 00:56:11,120
Speaker 3: Well yeah, you're on your flip phone. But she's got

1018
00:56:11,160 --> 00:56:15,319
Speaker 3: that sweet iPhone five and uh, you know she's on

1019
00:56:15,360 --> 00:56:18,680
Speaker 3: Facebook with her app. That could be a problem for you.

1020
00:56:18,800 --> 00:56:22,040
Speaker 1: And then she maybe saves your number and labels you

1021
00:56:22,080 --> 00:56:23,520
Speaker 1: as hot Maddie.

1022
00:56:23,760 --> 00:56:27,239
Speaker 3: Yeah, hot Maddie in her contacts. That's that's the off

1023
00:56:27,239 --> 00:56:35,040
Speaker 3: the grid guy that I am. And bigly Wiggly.

1024
00:56:32,920 --> 00:56:37,120
Speaker 1: Paramore yeah yeah, And so yeah, so then uh, the

1025
00:56:37,120 --> 00:56:39,400
Speaker 1: then Facebook has that piece of information.

1026
00:56:39,800 --> 00:56:42,919
Speaker 3: And then let's say, because you allow Facebook to see

1027
00:56:42,960 --> 00:56:44,320
Speaker 3: your context, yes.

1028
00:56:44,640 --> 00:56:48,640
Speaker 1: And depending upon your interaction or you're the amount of

1029
00:56:48,640 --> 00:56:51,440
Speaker 1: trouble you're willing to go to, it's very easy for

1030
00:56:51,480 --> 00:56:53,840
Speaker 1: Facebook to do that. In some cases you cannot avoid it.

1031
00:56:53,880 --> 00:56:55,640
Speaker 2: And you do have to opt in for that though,

1032
00:56:55,680 --> 00:56:57,600
Speaker 2: you know, justice any apps like that, as soon as

1033
00:56:57,600 --> 00:56:59,560
Speaker 2: you do an action on that app that requires it

1034
00:56:59,640 --> 00:57:02,080
Speaker 2: to have access to your camera, or have access to

1035
00:57:02,120 --> 00:57:04,799
Speaker 2: your microphone, or have access to your contacts, a little

1036
00:57:04,800 --> 00:57:06,640
Speaker 2: pop up comes up and you have to say yes,

1037
00:57:07,560 --> 00:57:10,200
Speaker 2: and you know it's a permission thing. But I guess

1038
00:57:10,200 --> 00:57:12,880
Speaker 2: once you've done that, that's just for you, it's not

1039
00:57:12,920 --> 00:57:14,239
Speaker 2: for those around you.

1040
00:57:14,360 --> 00:57:17,040
Speaker 1: Well, it's also if you use an iPhone, it's going

1041
00:57:17,080 --> 00:57:20,280
Speaker 1: to be a tad bit better about that. And the

1042
00:57:20,520 --> 00:57:23,920
Speaker 1: blootwear can be put in by a provider. I see that,

1043
00:57:24,200 --> 00:57:26,280
Speaker 1: and that blootwear may be such that you would have

1044
00:57:26,400 --> 00:57:29,840
Speaker 1: to perform a process called rooting your phone to actually

1045
00:57:29,880 --> 00:57:30,520
Speaker 1: get rid of it.

1046
00:57:30,600 --> 00:57:33,040
Speaker 2: You mean on an Android, like on an because if

1047
00:57:33,080 --> 00:57:35,880
Speaker 2: you've always heard, I've always heard that PCs are more

1048
00:57:35,920 --> 00:57:39,120
Speaker 2: susceptible to spyware and things. I guess that's true of

1049
00:57:39,120 --> 00:57:39,880
Speaker 2: the phones as well.

1050
00:57:40,160 --> 00:57:42,400
Speaker 1: Yeah I could, Yeah, I could easily see that.

1051
00:57:42,720 --> 00:57:45,680
Speaker 3: So let's let's just quickly jump into why Facebook hates

1052
00:57:45,720 --> 00:57:49,959
Speaker 3: that term shadow profiles. Oh why do they Well, because

1053
00:57:50,000 --> 00:57:53,000
Speaker 3: it sounds like they're straight up making these hidden profiles

1054
00:57:53,040 --> 00:57:56,280
Speaker 3: somewhere on your phone or your app, or maybe just

1055
00:57:56,360 --> 00:57:59,800
Speaker 3: in Facebook at large, profiles of people's people in your

1056
00:57:59,840 --> 00:58:03,120
Speaker 3: car contacts list that don't have a Facebook profile, And

1057
00:58:03,160 --> 00:58:05,400
Speaker 3: they say, that's not what they're doing. It doesn't do that.

1058
00:58:06,880 --> 00:58:14,520
Speaker 1: But in twenty thirteen, Facebook said it discovered and fixed

1059
00:58:15,320 --> 00:58:21,040
Speaker 1: a quote unquote bug. The bug was that when a

1060
00:58:21,200 --> 00:58:24,720
Speaker 1: user downloaded their Facebook file, which you can still do today,

1061
00:58:24,960 --> 00:58:27,600
Speaker 1: and it'll give you all the information they say they

1062
00:58:27,640 --> 00:58:30,200
Speaker 1: have on you. It'll give you all the or at

1063
00:58:30,280 --> 00:58:32,360
Speaker 1: least the information you're allowed to see, they have on you,

1064
00:58:32,560 --> 00:58:37,439
Speaker 1: all your likes, comments, et cetera, your messages. The thing

1065
00:58:37,720 --> 00:58:42,440
Speaker 1: was that in twenty thirteen, this quote unquote bug included

1066
00:58:42,560 --> 00:58:47,400
Speaker 1: not just people's visible contact information for their friends, but

1067
00:58:47,520 --> 00:58:52,240
Speaker 1: also their friends shadow contact information, So they were seeing

1068
00:58:52,280 --> 00:58:54,360
Speaker 1: stuff that they weren't supposed to see. And the problem

1069
00:58:54,400 --> 00:58:56,840
Speaker 1: with the bug for Facebook was not that all of

1070
00:58:56,880 --> 00:58:59,640
Speaker 1: this stuff was lumped together. It was that it had

1071
00:58:59,720 --> 00:59:04,240
Speaker 1: shown people that it existed. So the extent of the

1072
00:59:04,280 --> 00:59:07,640
Speaker 1: connections that it built around every user was supposed to

1073
00:59:07,680 --> 00:59:12,520
Speaker 1: only be visible to Facebook. Yeah, and they admit that

1074
00:59:13,520 --> 00:59:16,760
Speaker 1: this is in their phrase, it's getting information from a

1075
00:59:16,800 --> 00:59:19,840
Speaker 1: friend or someone you know or might know. But what

1076
00:59:19,880 --> 00:59:23,600
Speaker 1: does that mean. That means anyone at any point who

1077
00:59:23,680 --> 00:59:26,920
Speaker 1: might have somehow labeled your phone number, your email, or

1078
00:59:26,960 --> 00:59:30,120
Speaker 1: even your physical trying not to curse here, even your

1079
00:59:30,160 --> 00:59:34,280
Speaker 1: physical address will be added.

1080
00:59:33,920 --> 00:59:34,400
Speaker 3: To that.

1081
00:59:37,120 --> 00:59:40,520
Speaker 1: Agglomeration of information that is you. So, whether it's the

1082
00:59:40,560 --> 00:59:45,320
Speaker 1: Pigley Wiggly Hot Maddie, whether it's an old email address

1083
00:59:45,400 --> 00:59:48,080
Speaker 1: from college that a friend of yours has, you know,

1084
00:59:48,160 --> 00:59:53,560
Speaker 1: and it's like, it's like hot dot Maddie at uga

1085
00:59:53,680 --> 00:59:54,240
Speaker 1: dot edu.

1086
00:59:54,520 --> 00:59:57,040
Speaker 3: It was Obadiah fourteen at AOL.

1087
00:59:57,720 --> 00:59:58,840
Speaker 1: I knew it was him.

1088
00:59:59,320 --> 01:00:00,920
Speaker 3: Dang is that him?

1089
01:00:00,960 --> 01:00:08,320
Speaker 1: The whole time? Well, Obadiah just thought he looked like, yeah,

1090
01:00:08,320 --> 01:00:09,280
Speaker 1: I've always thought that to him.

1091
01:00:09,320 --> 01:00:11,120
Speaker 3: Yeah, me too, That's why That's why I chose it.

1092
01:00:11,200 --> 01:00:14,680
Speaker 1: So this means that the average user is much more

1093
01:00:14,720 --> 01:00:19,760
Speaker 1: exposed than they were led to believe. And Facebook's position

1094
01:00:20,040 --> 01:00:26,439
Speaker 1: is that mistakes happen and will be improved. And I think, Noel,

1095
01:00:26,520 --> 01:00:30,360
Speaker 1: you make a really interesting point in prediction here will

1096
01:00:30,360 --> 01:00:33,560
Speaker 1: it turn out to be something illegal? Do laws exist

1097
01:00:33,680 --> 01:00:36,000
Speaker 1: for this sort of situation? It seems like right now

1098
01:00:36,280 --> 01:00:36,880
Speaker 1: not many do.

1099
01:00:37,080 --> 01:00:39,680
Speaker 2: It seems like this is a case that will lead

1100
01:00:39,720 --> 01:00:43,880
Speaker 2: to change. We're already seeing it, but you know, big,

1101
01:00:44,000 --> 01:00:46,800
Speaker 2: big changes. The tech companies will have to abide by it.

1102
01:00:46,920 --> 01:00:49,160
Speaker 2: And it also makes you wonder is this going to

1103
01:00:49,200 --> 01:00:54,120
Speaker 2: affect user experience in a way that is unpleasant, because

1104
01:00:54,240 --> 01:00:56,320
Speaker 2: I think it might. I think some of the things

1105
01:00:56,320 --> 01:00:59,440
Speaker 2: that we take for granted on an open internet or

1106
01:00:59,440 --> 01:01:02,080
Speaker 2: on like an app like Facebook, are largely because of

1107
01:01:02,120 --> 01:01:05,160
Speaker 2: the the lack of barriers in some of these kinds

1108
01:01:05,160 --> 01:01:08,880
Speaker 2: of things, and we may find ourselves being inconvenience because

1109
01:01:08,920 --> 01:01:12,280
Speaker 2: of fallout from the story, right.

1110
01:01:12,600 --> 01:01:15,120
Speaker 1: I mean, that's a that's a good that's a good point,

1111
01:01:15,200 --> 01:01:18,120
Speaker 1: and it's a great perspective to raise because as we know,

1112
01:01:18,840 --> 01:01:24,320
Speaker 1: history proves that privacy as a concept is a relatively

1113
01:01:24,480 --> 01:01:30,800
Speaker 1: recent phenomenon. You know, the idea of privacies. We understand

1114
01:01:30,840 --> 01:01:34,360
Speaker 1: it did not exist in centuries past, and it may

1115
01:01:34,400 --> 01:01:38,280
Speaker 1: well not exist by the end of this century, by

1116
01:01:38,320 --> 01:01:42,320
Speaker 1: the end of by the end of this next decade,

1117
01:01:42,640 --> 01:01:46,320
Speaker 1: start like twenty twenty eight. But the other thing is,

1118
01:01:47,440 --> 01:01:50,280
Speaker 1: this is the current lay of the land, folks. The

1119
01:01:50,280 --> 01:01:53,040
Speaker 1: other thing is that, as far as Facebook is concerned,

1120
01:01:53,440 --> 01:01:55,960
Speaker 1: none of that information that other people have about you

1121
01:01:56,120 --> 01:01:57,480
Speaker 1: counts as your information.

1122
01:01:57,600 --> 01:01:58,680
Speaker 3: That's shadow information.

1123
01:01:58,840 --> 01:02:02,000
Speaker 1: Yeah, it belongs to the people who uploaded it. They're

1124
01:02:02,040 --> 01:02:05,080
Speaker 1: the only ones with any control over it. That's why.

1125
01:02:05,160 --> 01:02:07,640
Speaker 1: For instance, you can remove a tag of yourself on

1126
01:02:07,680 --> 01:02:10,560
Speaker 1: a photograph, right, and this is not shadow stuff. This

1127
01:02:10,640 --> 01:02:13,320
Speaker 1: is public profile stuff. You can remove a tag of

1128
01:02:13,360 --> 01:02:15,840
Speaker 1: yourself in a photograph, but if you want the photograph

1129
01:02:15,920 --> 01:02:18,280
Speaker 1: to be taken down, you have to get the person

1130
01:02:18,320 --> 01:02:21,680
Speaker 1: who put it up to take it down. This is

1131
01:02:21,840 --> 01:02:26,200
Speaker 1: dangerous stuff. And this is why, even though it's not illegal,

1132
01:02:26,760 --> 01:02:29,040
Speaker 1: this is why it could put people in real danger.

1133
01:02:29,200 --> 01:02:32,439
Speaker 3: Yeah, someone who cannot have their identity known.

1134
01:02:32,680 --> 01:02:36,440
Speaker 1: Yeah, like an undercover cop for instance, or let's say

1135
01:02:36,560 --> 01:02:39,800
Speaker 1: a prosecutor for criminal.

1136
01:02:39,360 --> 01:02:41,360
Speaker 3: Cases, anybody in law enforcement.

1137
01:02:41,480 --> 01:02:44,760
Speaker 1: Anybody in law enforcement. Let's say people who have lives

1138
01:02:45,000 --> 01:02:48,720
Speaker 1: working in well it's not legal in a lot of

1139
01:02:48,760 --> 01:02:50,920
Speaker 1: the US here, but somebody who's working in the sex

1140
01:02:50,960 --> 01:02:54,760
Speaker 1: trade or something. And you can find these various stories

1141
01:02:54,880 --> 01:02:58,680
Speaker 1: of people who deleted their Facebook because they were in

1142
01:02:58,720 --> 01:03:02,400
Speaker 1: a profession like this and they got the scariest thing

1143
01:03:02,440 --> 01:03:07,120
Speaker 1: it can happen, which is someone they knew, maybe even

1144
01:03:07,120 --> 01:03:09,480
Speaker 1: someone they like, put away in jail, or something pops

1145
01:03:09,560 --> 01:03:14,120
Speaker 1: up on the people you May know list, or for instance,

1146
01:03:14,120 --> 01:03:16,560
Speaker 1: there was a There are little things that are just

1147
01:03:16,560 --> 01:03:20,680
Speaker 1: twilight zone moments, like you'll hear stories about someone who

1148
01:03:20,760 --> 01:03:23,840
Speaker 1: works as a receptionist at an office meets somebody for

1149
01:03:23,880 --> 01:03:26,280
Speaker 1: the second time and they call them by their nickname

1150
01:03:27,480 --> 01:03:30,280
Speaker 1: in their second conversation because they popped up on their

1151
01:03:30,320 --> 01:03:34,120
Speaker 1: Facebook list. Or what if you are a therapist and

1152
01:03:34,160 --> 01:03:38,200
Speaker 1: your clients start popping up on each person's Facebook list.

1153
01:03:38,680 --> 01:03:41,320
Speaker 1: This is I mean, this is dangerous stuff, especially when

1154
01:03:41,320 --> 01:03:45,520
Speaker 1: we're what we're getting to is, regardless of the intent,

1155
01:03:45,640 --> 01:03:51,480
Speaker 1: what we're getting to in action is a violation of confidentiality.

1156
01:03:51,520 --> 01:03:53,320
Speaker 1: It's a dangerous thing. But then we have to ask,

1157
01:03:53,680 --> 01:03:55,560
Speaker 1: and I keep going back to the question you asked, No,

1158
01:03:56,040 --> 01:04:01,040
Speaker 1: how can government's police international entities like this? Should they

1159
01:04:01,080 --> 01:04:04,360
Speaker 1: even be trying? You know, it seems like the EU's

1160
01:04:04,440 --> 01:04:09,520
Speaker 1: taking this more seriously than the US, But there's pretty

1161
01:04:10,080 --> 01:04:14,080
Speaker 1: compelling evidence that this is not in any way new.

1162
01:04:14,120 --> 01:04:17,560
Speaker 1: We've talked about Alphabet and Google, talked about the CIA

1163
01:04:17,680 --> 01:04:22,280
Speaker 1: and Facebook, Cambridge Analytica. It's just it's a gimme. It's

1164
01:04:22,320 --> 01:04:25,720
Speaker 1: a home run. If if we were intelligence agencies, or

1165
01:04:25,760 --> 01:04:30,120
Speaker 1: if we were these businesses, we would feel like it

1166
01:04:30,160 --> 01:04:31,720
Speaker 1: was foolish not to be doing this.

1167
01:04:32,040 --> 01:04:35,520
Speaker 3: I agreed. It feels like there needs to be some

1168
01:04:35,640 --> 01:04:43,919
Speaker 3: kind of United Nations style thing just for digital matters,

1169
01:04:43,600 --> 01:04:45,760
Speaker 3: for international digital matters.

1170
01:04:46,320 --> 01:04:48,480
Speaker 2: Something really good points.

1171
01:04:47,880 --> 01:04:50,280
Speaker 3: Something where it's like an international body that can make

1172
01:04:50,360 --> 01:04:56,000
Speaker 3: decisions and say anything that occurs on networks that go

1173
01:04:56,160 --> 01:05:02,440
Speaker 3: across what lines of states and countries they you have,

1174
01:05:02,560 --> 01:05:04,120
Speaker 3: you are beholden to our group.

1175
01:05:04,840 --> 01:05:07,040
Speaker 2: I say, we set it up and then immediately neuter

1176
01:05:07,160 --> 01:05:07,840
Speaker 2: the heck out of it.

1177
01:05:08,600 --> 01:05:11,240
Speaker 1: Yeah yeah, yeah, let's give it the Leaga Nations treatment. Uh.

1178
01:05:11,360 --> 01:05:12,800
Speaker 3: And I think it needs to be run by a

1179
01:05:12,800 --> 01:05:22,680
Speaker 3: group of ais. Paul's nodding robots factor to Mark Zuckerberg

1180
01:05:22,720 --> 01:05:23,560
Speaker 3: will be the head.

1181
01:05:24,000 --> 01:05:26,520
Speaker 2: Oh boy, have you seen those memes where they like

1182
01:05:26,520 --> 01:05:28,479
Speaker 2: paint him up like a data from Star Trek.

1183
01:05:28,600 --> 01:05:34,680
Speaker 1: Yes, it's really good and it's eerily eerily works.

1184
01:05:34,760 --> 01:05:37,560
Speaker 3: Oh yeah, but I am kind of serious with people

1185
01:05:37,600 --> 01:05:40,520
Speaker 3: that people that actually know what they're talking about and

1186
01:05:40,640 --> 01:05:44,080
Speaker 3: understand a technocracy of I mean, I think there should

1187
01:05:44,080 --> 01:05:46,680
Speaker 3: be a body that at least can enforce regulation.

1188
01:05:47,120 --> 01:05:50,240
Speaker 1: Well, there are several there's a wealth of bodies in

1189
01:05:50,520 --> 01:05:53,520
Speaker 1: the US and abroad that were built with that purpose,

1190
01:05:54,120 --> 01:05:57,960
Speaker 1: but of course, as you mentioned before, are a little

1191
01:05:58,080 --> 01:06:02,000
Speaker 1: close to the entity and institutions and industries that they

1192
01:06:02,000 --> 01:06:05,040
Speaker 1: are supposed to oversee. Yeah, well, what's the question? What

1193
01:06:05,320 --> 01:06:08,480
Speaker 1: happens next? Is this going to be a flash in

1194
01:06:08,520 --> 01:06:11,560
Speaker 1: the pan news wise? Will the news cycle move on

1195
01:06:11,760 --> 01:06:14,360
Speaker 1: to a different thing as the US and Russia continue

1196
01:06:14,360 --> 01:06:21,200
Speaker 1: to wage war for the pipeline in Syria? I'm sorry the.

1197
01:06:19,920 --> 01:06:21,160
Speaker 3: Way what'd you say? Proxy war?

1198
01:06:21,320 --> 01:06:22,560
Speaker 1: Freedom? And whatever?

1199
01:06:22,600 --> 01:06:23,560
Speaker 3: What was it?

1200
01:06:23,640 --> 01:06:28,040
Speaker 1: But the but the you know, is this going to

1201
01:06:28,120 --> 01:06:31,080
Speaker 1: stay in the headlines? Will we ever know the full story,

1202
01:06:31,120 --> 01:06:33,680
Speaker 1: the full cast of players, the extent of their involvement.

1203
01:06:34,480 --> 01:06:39,560
Speaker 1: Will it? Will anybody at Facebook be charged with a crime?

1204
01:06:40,000 --> 01:06:43,880
Speaker 1: Will anybody at Cambridge Analytica? Did Facebook even commit a crime?

1205
01:06:44,280 --> 01:06:44,600
Speaker 1: You know.

1206
01:06:46,160 --> 01:06:46,280
Speaker 3: That?

1207
01:06:46,440 --> 01:06:49,280
Speaker 1: That's what I keep going back to. Cynics in the

1208
01:06:49,320 --> 01:06:52,320
Speaker 1: audience will probably say no, guys, if you token scapegoats

1209
01:06:52,320 --> 01:06:55,040
Speaker 1: will be profited up for some public pitchforks and political

1210
01:06:55,080 --> 01:06:58,640
Speaker 1: theater and then it'll be business as usual.

1211
01:06:59,240 --> 01:06:59,800
Speaker 3: Yeah.

1212
01:07:00,280 --> 01:07:06,640
Speaker 1: Man, but that's our show but for today. Yeah. By

1213
01:07:06,720 --> 01:07:07,960
Speaker 1: the way, we're on Facebook.

1214
01:07:08,040 --> 01:07:08,200
Speaker 3: Yeah.

1215
01:07:08,240 --> 01:07:08,400
Speaker 1: Yeah.

1216
01:07:08,400 --> 01:07:12,280
Speaker 3: If you have any thoughts on Cambridge Analytica and Facebook's

1217
01:07:12,360 --> 01:07:16,280
Speaker 3: use of data and data and however you say that,

1218
01:07:17,000 --> 01:07:18,800
Speaker 3: you know, just write to us. You can find us

1219
01:07:18,800 --> 01:07:22,480
Speaker 3: on Facebook. We are conspiracy stuff, Like, really, just find

1220
01:07:22,560 --> 01:07:25,880
Speaker 3: us there. Make sure you put any and all identifying

1221
01:07:25,920 --> 01:07:29,560
Speaker 3: information just directly into the comment box. Cool, they know, Okay,

1222
01:07:29,880 --> 01:07:31,960
Speaker 3: but just you know, put more just as much as

1223
01:07:31,960 --> 01:07:32,680
Speaker 3: you possibly can.

1224
01:07:32,720 --> 01:07:36,560
Speaker 1: Listen to your fears, your blood type things that you

1225
01:07:36,680 --> 01:07:39,720
Speaker 1: worry about when you walk into an elevator full of strangers.

1226
01:07:39,800 --> 01:07:39,960
Speaker 3: You know.

1227
01:07:40,000 --> 01:07:42,040
Speaker 2: We had a funny realization the other day. We were

1228
01:07:42,040 --> 01:07:44,800
Speaker 2: looking at some of our breakdowns of like listenership, and

1229
01:07:44,880 --> 01:07:48,720
Speaker 2: there's a really huge percentage of our listenership that comes

1230
01:07:48,800 --> 01:07:54,600
Speaker 2: up in the data that we get as unknown or Yeah,

1231
01:07:54,640 --> 01:07:57,320
Speaker 2: and Ben immediately was like, well, that's probably because our

1232
01:07:57,320 --> 01:08:00,240
Speaker 2: listeners might be more likely than some other show to

1233
01:08:00,320 --> 01:08:01,000
Speaker 2: use a VPN.

1234
01:08:01,240 --> 01:08:02,959
Speaker 3: Yeah, we know what you're doing right now with all.

1235
01:08:02,880 --> 01:08:04,960
Speaker 1: Your encryption, and we applaud it.

1236
01:08:05,120 --> 01:08:06,040
Speaker 3: We think it's awesome.

1237
01:08:06,960 --> 01:08:11,160
Speaker 1: A VP of Virtual Private Network. Right, yes, let us

1238
01:08:11,240 --> 01:08:13,480
Speaker 1: know what you think about this. What do you think

1239
01:08:13,520 --> 01:08:16,280
Speaker 1: about the scandal? Would you, for example, would you be

1240
01:08:16,360 --> 01:08:20,520
Speaker 1: okay with it if you received some sort of compensation

1241
01:08:20,720 --> 01:08:22,600
Speaker 1: other than the use of that platform, like if you

1242
01:08:22,720 --> 01:08:29,200
Speaker 1: got to check every three months or something where they said, hey, great,

1243
01:08:29,240 --> 01:08:32,920
Speaker 1: great job, here's for being you. Here's for yeah, perfect, Yes,

1244
01:08:32,960 --> 01:08:33,680
Speaker 1: here's for being you.

1245
01:08:33,840 --> 01:08:35,200
Speaker 3: Here's two dragon coins.

1246
01:08:35,280 --> 01:08:40,200
Speaker 1: Here's two dragon coins, and a couple embarrassing pictures of

1247
01:08:40,240 --> 01:08:44,000
Speaker 1: you from middle school. Don't thank us, We like to

1248
01:08:44,040 --> 01:08:46,280
Speaker 1: thank you. And if you just know which side your

1249
01:08:46,280 --> 01:08:48,920
Speaker 1: bet is made, yeah, yeah, I.

1250
01:08:49,040 --> 01:08:51,680
Speaker 3: Just remember if you know, you stop using our services.

1251
01:08:51,720 --> 01:08:53,920
Speaker 3: We do have these pictures and they may or may

1252
01:08:53,920 --> 01:08:55,880
Speaker 3: not end up in the hands of your enemies.

1253
01:08:55,880 --> 01:08:58,719
Speaker 2: It'd be a shame if the enemy's found out about

1254
01:08:58,720 --> 01:09:02,479
Speaker 2: your head gear, your brace face.

1255
01:09:03,000 --> 01:09:06,680
Speaker 1: So do you click like angry or wow? What was it?

1256
01:09:06,720 --> 01:09:07,160
Speaker 1: O face?

1257
01:09:07,320 --> 01:09:07,559
Speaker 3: Yeah?

1258
01:09:07,640 --> 01:09:08,120
Speaker 2: O face?

1259
01:09:08,160 --> 01:09:08,479
Speaker 1: I don't know.

1260
01:09:08,520 --> 01:09:09,519
Speaker 2: I do the heart?

1261
01:09:09,840 --> 01:09:10,799
Speaker 3: Yeah, that's my favorite.

1262
01:09:10,800 --> 01:09:11,439
Speaker 2: I like the heart.

1263
01:09:12,080 --> 01:09:15,280
Speaker 1: So let us know. Thank you so much as always

1264
01:09:15,320 --> 01:09:20,880
Speaker 1: for tuning in. Stay tuned as we return with more strange, unusual, fascinating,

1265
01:09:21,040 --> 01:09:25,439
Speaker 1: and at times terrifying stuff. They don't want you to

1266
01:09:25,479 --> 01:09:27,040
Speaker 1: know in the meantime, and.

1267
01:09:27,040 --> 01:09:30,000
Speaker 3: That's the end of this classic episode. If you have

1268
01:09:30,120 --> 01:09:34,200
Speaker 3: any thoughts or questions about this episode, you can get

1269
01:09:34,200 --> 01:09:36,639
Speaker 3: into contact with us in a number of different ways.

1270
01:09:36,840 --> 01:09:38,400
Speaker 3: One of the best is to give us a call.

1271
01:09:38,439 --> 01:09:43,280
Speaker 3: Our number is one eight three three STDWYTK. If you

1272
01:09:43,280 --> 01:09:45,120
Speaker 3: don't want to do that, you can send us a

1273
01:09:45,160 --> 01:09:46,320
Speaker 3: good old fashioned email.

1274
01:09:46,560 --> 01:09:50,720
Speaker 1: We are conspiracy at iHeartRadio dot com.

1275
01:09:50,880 --> 01:09:52,960
Speaker 3: Stuff they Don't Want You to Know is a production

1276
01:09:53,040 --> 01:09:57,599
Speaker 3: of iHeartRadio. For more podcasts from iHeartRadio, visit the iHeartRadio app,

1277
01:09:57,680 --> 01:10:00,679
Speaker 3: Apple Podcasts, or wherever you listen to your favorite shows.