Speaker 1: With technology with tex Stuff. Hey there, and welcome to
Speaker 1: tex Stuff. I am your host, Jonathan Strickland, and today
Speaker 1: I have a very special guest as a guest co
Speaker 1: host of this episode, then Johnson Marketplace dot Org. Welcome Ben. Hey,
Speaker 1: how's it going, Jonathan, It's great. Now I have a
Speaker 1: question for you before we get into the topic of
Speaker 1: the day. I got to chance to be a guest
Speaker 1: host on a show you host called code Breaker, And
Speaker 1: on code Breaker when we talked, I made a reference
Speaker 1: to Shakespeare. Yes. Now my question for you is, are
Speaker 1: you ever confused with the seventeenth century British playwright Ben Johnson.
Speaker 1: Oh man all the time, yapens all the time, embarrassing. Yeah,
Speaker 1: I've been you know, I have been to Stratford upon Avon.
Speaker 1: I have done that whole thing, and and and I
Speaker 1: used to you know, I never get confused for that
Speaker 1: Ben Johnson. But in the past, at least people made
Speaker 1: jokes about steroids because of the other Ben Johnson. Nadian
Speaker 1: Runner and I used to have a T shirt that
Speaker 1: said I'm not on steroids, but thanks for asking, which
Speaker 1: was a kind of inside joke among my friends. But
Speaker 1: it was, um, you know, I've never been. No, I've
Speaker 1: never been that poetic. I've never been that good at sonnets,
Speaker 1: so usually I'm not not confused. It's it's sad. It's
Speaker 1: really funny because, uh, I'm in in an upcoming episode
Speaker 1: of tech stuff. I'm going to have my friend i
Speaker 1: as actar from on and his his Twitter handle is
Speaker 1: at i as, so he gets a lot of hip
Speaker 1: hop and rap questions. So yeah, it's like, not so much.
Speaker 1: I I'm okay with that. But the reason I have
Speaker 1: been on today is because we're doing something kind of
Speaker 1: a complementary episode to the Codebreaker episode I was on,
Speaker 1: which was all about data tracking and data mining, and
Speaker 1: to kind of talk about what that is, what it's
Speaker 1: used for, what is the best case scenario, like what
Speaker 1: why do we want it? And what are some really
Speaker 1: really good reasons why we may not want it, despite
Speaker 1: the fact that at this point it's here to stay.
Speaker 1: I think a spoiler alert, data tracking is not going away,
Speaker 1: right until the sort of posted post apocalyptic world sent
Speaker 1: until we're all in um fallout for world whatever it is,
Speaker 1: or or we reached singularity and we're sharing the same
Speaker 1: consciousness anyway, right Yeah, and then it doesn't matter there's
Speaker 1: no such thing as an individual at that point, so
Speaker 1: until then we're stuck with it. So data tracking and
Speaker 1: data mining, these are terms that kind of relate to
Speaker 1: another buzz term that you've probably heard over and over again,
Speaker 1: which is big data. Um. And the reason why we're
Speaker 1: even talking about this at all is because information has value,
Speaker 1: but it's hard to pin down exactly what the value
Speaker 1: is because it may be very different from one person
Speaker 1: to another, or between the individual whose data it is
Speaker 1: and some other organization or a chain of organizations, and
Speaker 1: the whole thing gets really messy. And uh yeah, we'll
Speaker 1: be talking about information and information about information like metadata.
Speaker 1: I mean this, it becomes a rabbit hole that you
Speaker 1: can easily get lost in as you start to try
Speaker 1: and unravel everything and figure out, okay, well, what's the
Speaker 1: heart of this story. And to be fair, data trekking
Speaker 1: is something that's been going on for ages. It's not new.
Speaker 1: It's just that the Internet and the rules we use
Speaker 1: today allow us to generate, gather, and sift through more
Speaker 1: information than we've ever been able to do before. It's
Speaker 1: kind of a catch all phrase, right, Jonathan, It's it's
Speaker 1: this kind of catch all phrase for for computers in
Speaker 1: software to build a really comprehensive um data profile about
Speaker 1: you in some ways. But it's something that credit card
Speaker 1: companies and banks and advertisers have endeavored to do for
Speaker 1: decades exactly, I mean. And this can be done for
Speaker 1: very simple, quote unquote innocent reasons. Like one of the
Speaker 1: examples I like to say is you might have a
Speaker 1: shop and you in your shop, you sell ice cream,
Speaker 1: and you pay attention to see which flavors of ice
Speaker 1: cream sell the best, and that informs you of which
Speaker 1: flavors of ice cream you need to stock up on.
Speaker 1: You are tracking the data of sales. In this case,
Speaker 1: you're not necessarily looking at individuals. You don't care who
Speaker 1: it is that buys the vanilla, but if everyone's buying vanilla,
Speaker 1: you want to make sure you have more vanilla than
Speaker 1: the other flavors that aren't selling as well. So that's
Speaker 1: one that's like the very simplest version of data tracking
Speaker 1: there where you know you've scrubbed the identity of the individuals,
Speaker 1: that's not even that doesn't even come up, it's not important.
Speaker 1: But it can go all the way to the other extreme,
Speaker 1: where you want to create targeted advertising. That is going
Speaker 1: to appeal to each specific person that visits a particular
Speaker 1: site or use as a particular service, and the whole
Speaker 1: point of that is to attempt to get them to
Speaker 1: buy something or to enroll in something. It's it's the
Speaker 1: goal of all advertising. It's to get you to act
Speaker 1: in a certain way. And the idea is that, well,
Speaker 1: if we can narrow the target down to the most
Speaker 1: likely candidates, will get a better return on our efforts.
Speaker 1: And this is theoretically good for the user, right Jonathan.
Speaker 1: Theoretically it's it's sort of like, we want to give
Speaker 1: you the thing that you really want, and we want
Speaker 1: to give it to you in the most efficient way
Speaker 1: and in the most sort of sympatico way with with
Speaker 1: what you are interested in doing already. Um, but it
Speaker 1: can sort of like data tracting to me means so
Speaker 1: many things, Jonathan, Right. It means your online browsing behavior,
Speaker 1: or the things you choose to buy, the platforms, the
Speaker 1: software you use, the things you download, your name, age, race,
Speaker 1: sexual orientation, when you're sick, what what you're sick with,
Speaker 1: your eating habits, your television watching habits, your fears, hopes, dreams.
Speaker 1: Right when it really means so many things when you're awake,
Speaker 1: when you're asleep. I mean, like we we're talking about
Speaker 1: a world now where where we we want these wearables.
Speaker 1: They could give us real time data feedback on how
Speaker 1: we are doing throughout the day. Maybe you are an
Speaker 1: extreme type a personality and you are trying to schedule
Speaker 1: your day so that when you're hitting those peak moments
Speaker 1: of productivity, that's when you're tackling the most important tasks
Speaker 1: to you, and you're wearing wearables, you're getting that feedback
Speaker 1: and you're understanding all this stuff. That data may also
Speaker 1: be going to other places and there may not be
Speaker 1: any use for it right now. Like right now, as
Speaker 1: you're generating that data, that's fine, no one's looking at it.
Speaker 1: But down the road that might not be the case
Speaker 1: for various reasons, some of which are terrifying. But but
Speaker 1: you know, you were talking about the different types of data.
Speaker 1: There are actually three kind of broad categories we can
Speaker 1: classify the data that we are creating that that various
Speaker 1: entities are really interested in. There's there's volunteered data, and
Speaker 1: that's the stuff we share, right that's when we go
Speaker 1: on facebooks exactly if that's my tweets, your tweets, your
Speaker 1: your YouTube's, your facebooks, your Pinterests, it's it's the stuff
Speaker 1: where we are participating in the conversation and generating that information.
Speaker 1: We're not just doing that for the people that we
Speaker 1: are trying to impress or our friends or sometimes are
Speaker 1: enemies if we want to rub their noses and stuff
Speaker 1: because they think they're so big. But that's that's the
Speaker 1: stuff that we are actively sharing. Then there's the observed data.
Speaker 1: That's the stuff that companies and other entities can can
Speaker 1: figure out about us just by watching our behavior. So
Speaker 1: this would be kind of the stuff we buy, the
Speaker 1: browsing habits we have where we log in from. Like
Speaker 1: if you are doing most of your browsing on a
Speaker 1: mobile device, you better bet there their companies out there
Speaker 1: that want to know that. They want to know that
Speaker 1: you access stuff on your phone more than you do
Speaker 1: on a laptop or desktop. That's important information. And then
Speaker 1: there's the inferred data, and that's the stuff that companies
Speaker 1: guess is relevant to you based upon the information they
Speaker 1: gather from the other two types of sources. And it's
Speaker 1: it's this collection that's important. And you know I mentioned
Speaker 1: about value and how value is different for different people.
Speaker 1: So the Telegraph did a survey UH, in which they
Speaker 1: asked people what they felt their personal information was worth
Speaker 1: to them personally, Like, what is your identity worth to you? Uh?
Speaker 1: Based upon the kind of information to get shared around
Speaker 1: these entities? Can you make a ballpark? Guess I'll tell
Speaker 1: you this. It's it's it's less than ten thousand dollars,
Speaker 1: but it's more than five dollars. Man, man, I'm gonna
Speaker 1: go with I'm gonna go with twenty bucks or no, no, no, eight, wait,
Speaker 1: hundred bucks. You know it's probably because we share so
Speaker 1: much ben that we value it so little. But the
Speaker 1: average person five thousand dollars. Oh well, that's good. That
Speaker 1: that makes and that makes the average person a lot
Speaker 1: smarter than me. But to be fair, it was three
Speaker 1: thousand two pounds sterling. But I did the conversion. Yeah,
Speaker 1: and and uh. And they also mentioned the fact that,
Speaker 1: um that while they were doing this, they they found
Speaker 1: that there was a disparity that that women in the
Speaker 1: survey tended to answer more frequently that their personal information
Speaker 1: was priceless, that there was no price you could put
Speaker 1: on it that they would feel comfortable selling it. And
Speaker 1: men were less likely to do that. They also said
Speaker 1: an interesting data point, if you will, Yeah, I also
Speaker 1: mentioned that older people were less likely to want to
Speaker 1: sell their information than younger people, which kind of young
Speaker 1: younger people are just giving it away. Yeah they are,
Speaker 1: I mean they you know, we're seeing that more and
Speaker 1: more with the the the very cavalier behavior of certain
Speaker 1: executives like Zuckerberg who have said that privacy is dead. No,
Speaker 1: privacy is no longer a thing. That seems to be
Speaker 1: kind of the message that younger generations have not only
Speaker 1: absorbed but adopted at least to some extent. Say you
Speaker 1: and I asked old kajure, so we claim, yeah, no,
Speaker 1: if your generation X or older, you're like, get your dirty,
Speaker 1: grubby hands off my personal information, you damn millennial, right,
Speaker 1: so now here, that's so, that's how much the person
Speaker 1: values their personal information in the thousands of dollars range. Okay,
Speaker 1: I know what you're gonna do next, Right, You're gonna
Speaker 1: give me the average company's value on our data. Ye's
Speaker 1: gonnas exactly what's gonna happen? So here's the deal. Your
Speaker 1: personal information to a company at best, like at its
Speaker 1: peak for average amount of information. I'm not talking about
Speaker 1: very specialized stuff like if you were talking about that
Speaker 1: wearable example. I was saying earlier that would that would
Speaker 1: be worth more because it's more comprehensive data. But you're
Speaker 1: average stuff, like you're the stuff you might find on
Speaker 1: a Facebook profile page. It is worth point zero zero
Speaker 1: five dollars. So half a cent is how much your
Speaker 1: data is. We're at at the peak average is closer
Speaker 1: to point zero zero zero five dollars. To know that.
Speaker 1: I'm you know, a boring, middle aged white guy who's
Speaker 1: probably going to see the Star Wars movie probably, I
Speaker 1: mean I would have paid you for definitely, Okay, I
Speaker 1: mean it's seven days away, dude, it is seven days
Speaker 1: away as we record that. So so at any rate, yeah, exactly,
Speaker 1: Like the company is like, that's how much your information
Speaker 1: is worth to them, So to you, it's in the
Speaker 1: thousands of dollars. To them, it doesn't even equal a
Speaker 1: penny for the but an aggregate, right, it's gonna be
Speaker 1: worth so much more. Well, exactly because because when a
Speaker 1: company is buying information, they're not buying one person's information.
Speaker 1: They're buying bundles of thousands upon thousands of people's information.
Speaker 1: Then it's that company's job to either sell that information
Speaker 1: off to someone else. These are data brokers. This is
Speaker 1: all they do. That's that's their job. That's their business.
Speaker 1: They buy and sell info. They don't make stuff, they
Speaker 1: don't provide any services. They aggregate data and sell it
Speaker 1: off to other people. This is a fascinating part of
Speaker 1: this whole thing to me, Jonathan, because it's like they're
Speaker 1: these companies are really I mean, they're they're kind of
Speaker 1: like in the shadows, you know, they're they're they're like
Speaker 1: companies with like weird names that that that have you know,
Speaker 1: data centers in in in the middle of nowhere, and
Speaker 1: and they're really hard to find. And you you know,
Speaker 1: I mean you and I probably both remember the days
Speaker 1: of like do not call me at home, you know,
Speaker 1: getting getting that phone call from marketers, the pre do
Speaker 1: not call list days. Right, And we don't really have
Speaker 1: a do not call list for data yet, right. We
Speaker 1: have some tools, but we don't really have that. And
Speaker 1: I feel like we're in this kind of um, I
Speaker 1: don't know, gold rush on data exactly. We don't have
Speaker 1: we don't have the power to say here is what
Speaker 1: you can use my data to do, and here is
Speaker 1: what you cannot use my data to do. Uh. You know,
Speaker 1: if you're lucky, you will be or if you're very
Speaker 1: um you know, careful, and you're and you're paying really
Speaker 1: close attention, then you might use services where you're looking
Speaker 1: at the user agreement and looking to see do they
Speaker 1: have a policy? Do they do they specifically state they
Speaker 1: will not share your information with third parties. Um, you
Speaker 1: know you can find some of those, but honestly, that's
Speaker 1: not the way business works for most of these companies.
Speaker 1: You are the product, right, like like Google is free. Yep,
Speaker 1: you're the one the product. That's that's what Google, your
Speaker 1: Google's product, your Facebook product. You are what they are
Speaker 1: selling to other other interested parties. And uh, even if
Speaker 1: you look at ones that say that, oh, don't worry,
Speaker 1: we scrub all the identifiable stuff from your day, animize
Speaker 1: your data if you will. Yeah, that, Um, that don't
Speaker 1: work so good, Ben, it doesn't work very good. There
Speaker 1: have been studies about this in the last few years, right.
Speaker 1: I think there was a study that came out of
Speaker 1: the UK that suggested, um that really in order to
Speaker 1: identify a person, um, you really only needed like one
Speaker 1: or two pieces of information about them, right, Do you
Speaker 1: remember that study that came out in the last I
Speaker 1: don't know year or so. It was like, I remember
Speaker 1: that one. And there's one that Latania Sweeney Harvard she
Speaker 1: did one where she proved that with three points of data,
Speaker 1: which was gender, birth date, and ZIP code, she could
Speaker 1: identify of US residents. So so nearly of all people
Speaker 1: living in the United States she could identify just with
Speaker 1: those three pieces of information. That One of the things
Speaker 1: I like to stress to people is your name, which
Speaker 1: you try to protect when you are when you think
Speaker 1: you're being anonymous on the Internet and you use a handle,
Speaker 1: so you're not using your name like I use my
Speaker 1: name everywhere as my handle. That's just but most of
Speaker 1: the time, but a lot of people like, no, I'm
Speaker 1: going to use an anonymous handle. Your name is the
Speaker 1: least important thing about you. I hate to break it
Speaker 1: to you that because honestly, most of these companies don't care.
Speaker 1: Just like I was saying, the shop owner with the
Speaker 1: ice cream doesn't care who you are. Most of these
Speaker 1: companies don't really care who you are. They care about
Speaker 1: what you do. Yeah, and it's really interesting too, Like Google,
Speaker 1: for instance, leverages large amounts of money and even some
Speaker 1: of their best algorithmic computing power to make sure that
Speaker 1: advertisers and marketers, for instance, don't engage in dishonest practices
Speaker 1: or break Google's own rules about tracking data. And even
Speaker 1: for Google, this is becoming like a herculean task. We
Speaker 1: I talked to in for Cobreaker, I talked to some
Speaker 1: people at Columbia's and Columbia grad students who are studying this,
Speaker 1: and it's really interesting to see what gets pulled from
Speaker 1: even like emails that you might send in Gmail, for instance. Yeah, yeah,
Speaker 1: this is this is kind of um, kind of terrifying.
Speaker 1: Also if you if you think about because you think
Speaker 1: of email as being this this uh, private means of
Speaker 1: communication that you know, just as you wouldn't expect someone
Speaker 1: else to read your snail mail, that that would be
Speaker 1: a violation of trust to find out that stuff is
Speaker 1: popping up based on things that you've typed. Like if
Speaker 1: you've written an email recently and then something that is
Speaker 1: related to that email starts seemingly to pop up in
Speaker 1: places you weren't expecting, you might think at first that
Speaker 1: that's a weird coincidence. But if this happens a few
Speaker 1: times like, Okay, this is not a coincidence. What is
Speaker 1: going on? Yeah, it's really interesting. I think that's something Jonathan,
Speaker 1: that so many people can relate to, right, this idea
Speaker 1: of like, oh I looked at this use once or
Speaker 1: I looked at you know, whatever, the whatever item that
Speaker 1: you might buy online once and that advertisement follows you
Speaker 1: around forever. Yeah, you had that great moment and codebreaker
Speaker 1: in fact, about the sandals. Yes, the sandals that followed
Speaker 1: Molly around. Every time I logged into Facebook for a
Speaker 1: straight week, they would pop up, and I was so
Speaker 1: scared because I was like, oh my god, I'm gonna
Speaker 1: be sitting next to my boyfriend and I'm gonna log
Speaker 1: onto Facebook and these sandals are going to come up,
Speaker 1: and it's gonna reopen the wound, and he's going to
Speaker 1: know that I was sending them to people asking for
Speaker 1: an opinion. Did you change your behavior when you were
Speaker 1: hanging out with him? Yeah, Like if we were loafing around,
Speaker 1: I would open so many tabs and none of them
Speaker 1: would be Facebook. This is a really funny story. What
Speaker 1: does it make you think about? It made me wish
Speaker 1: that I could go back to the days where Facebook
Speaker 1: was just showing me things that I wanted to buy
Speaker 1: and not things that I wanted to run away from.
Speaker 1: That's uh, that's such a I mean, I've seen the
Speaker 1: same sort of stuff and I've seen advertisements that it's odd,
Speaker 1: like things that do not interest me at all. They're
Speaker 1: just completely off base and I can't It's one of
Speaker 1: those things where I think, well, maybe now, it's just
Speaker 1: that the system hasn't gathered enough information about me to
Speaker 1: get a good feel right, So I'm seeing stuff. It's
Speaker 1: like this is the shotgun approach. I'm throwing everything I
Speaker 1: can and hopefully stuff will work. Uh. Systems like Facebook,
Speaker 1: for example, where you can actually go in to an
Speaker 1: ad and say I am not interested in this, you know,
Speaker 1: then maybe you'll end up getting a different selection of stuff,
Speaker 1: but sometimes you'll just get different versions of the same
Speaker 1: thing you already said. Uh, that's not it's not me man.
Speaker 1: I appreciate the muscles on this dude, but I am
Speaker 1: not going to be that guy. So I'm into I'm
Speaker 1: into my little pony, but I I think I've outgrown it.
Speaker 1: I look, my collection, my collection can only be so complete.
Speaker 1: That's you know, there's you gotta draw the line after
Speaker 1: like series five, I gotta say I'm done. I have
Speaker 1: no more shelf space. Um, yeah, this is this is
Speaker 1: one of those those amazing stories, like the story about
Speaker 1: the sandals. Just just one of those things that has
Speaker 1: happened to pretty much anyone who has been active online
Speaker 1: over the last couple of years. And it's probably one
Speaker 1: of those stories I would imagine that people start to
Speaker 1: share not realizing that this is something that other people
Speaker 1: have experienced. Yeah, right, it is. It feels like a
Speaker 1: very personal experience, but it's something that seems to happen
Speaker 1: to everyone. And it was interesting to see relatively recently Facebook,
Speaker 1: I believe UM made it easier for people to control
Speaker 1: how much of their ex girlfriend or a friend or
Speaker 1: significant others information surfaced on their own timeline. Did you
Speaker 1: see that story that came out relatively recently. He was
Speaker 1: interesting this idea that like you know, you know, even
Speaker 1: the tech companies they're really really good at data tracking
Speaker 1: at this point, are still trying to figure out when
Speaker 1: it goes wrong and how to help the user. In
Speaker 1: some cases, UM make it a more kind of custom experience, right,
Speaker 1: And and we should also stress that that the overwhelming
Speaker 1: majority of companies out there do not want it to
Speaker 1: go wrong, because that's that's counterproductive to what the end
Speaker 1: end goal is, at least for the person that actually
Speaker 1: bought the advertisement and the person who owns the place
Speaker 1: where that ad is being shown. In the case of
Speaker 1: targeted advertising, at least like they don't want that to
Speaker 1: go badly. But in other cases, you know, they don't
Speaker 1: want to go badly either that that's counterproductive. But it
Speaker 1: is possible for this to go wrong even if everyone
Speaker 1: is trying to use it to to people's benefit. And
Speaker 1: on Codebreaker, you had a heartbreaking story about about a
Speaker 1: father who shared some information, not not knowing, not even
Speaker 1: realizing that that information was somehow going to make its
Speaker 1: way into a database and then came back to haunt him. Yeah,
Speaker 1: this guy, Mike say, and and this was happened a
Speaker 1: few years back. He you know, unfortunately, um his his
Speaker 1: one of his children, his daughter, died in a car accident.
Speaker 1: And he went to buy some uh you know, he
Speaker 1: went to buy some some actually some some um some
Speaker 1: stuff for photos, Like he was putting together some photo albums,
Speaker 1: and he went to buy some frames for those photos.
Speaker 1: And the place that he called to do that Um,
Speaker 1: he just in passing men sinned that his daughter had
Speaker 1: died in this car accident. And the person that he
Speaker 1: was talking to actually took down that information. And it's
Speaker 1: still not clear how it how all of this happened,
Speaker 1: but apparently they took down the information and that information
Speaker 1: ended up getting sold to a data broker that sold
Speaker 1: the information from the company he originally called to Office Max,
Speaker 1: and then Office Max sent a letter to him. Um,
Speaker 1: there was addressed to owner of the house or Mike,
Speaker 1: say daughter died in a car accident. Um, And that's
Speaker 1: when it goes really really wrong, right. The picture frames
Speaker 1: I was was buying with through my children in the
Speaker 1: bottom it had Ashley's name and her birth and death
Speaker 1: date and that she loved them, and you know what
Speaker 1: I mean, that's how she the conversation came about that
Speaker 1: what is this for? And she took you know, the
Speaker 1: information that would go on the bottom and put that,
Speaker 1: you know, on whatever to get it stamp. But they
Speaker 1: had nothing that it didn't say killed in car crashing
Speaker 1: out of that that was all on her own. And
Speaker 1: this is a totally separate company from Office Max. And
Speaker 1: somehow Office Max got the information they bought the information
Speaker 1: from a data group. So that information was sold from
Speaker 1: things remembered to a data group that collects data and
Speaker 1: then taken and sold to Office Max. And an Office
Speaker 1: Max used without looking at it. What an an emotional
Speaker 1: uh punch to the gut to see something like that.
Speaker 1: And and honestly, you know, I mean, you know, Office
Speaker 1: Max did not intend that. There's no sane reason anyone
Speaker 1: would do at So it's clearly an epic mistake that
Speaker 1: that could result in true emotional trauma, even if it's
Speaker 1: just a momentary thing. You don't want any person to
Speaker 1: experience that kind of thing. And uh, it was a
Speaker 1: pure accident, I imagine based upon way back in the day,
Speaker 1: I used to work for consultants. And when when I
Speaker 1: worked for consultants, one of the things I had to
Speaker 1: do was arranged giant mailing lists and print out tons
Speaker 1: and tons and tons of mailing labels. And so you're
Speaker 1: you're working with these big spreadsheets that are possibly thousands
Speaker 1: of names long. So I'm guessing what happened was you
Speaker 1: had the data entered into some sort of spreadsheet, and
Speaker 1: probably the spreadsheet was converted at least once, and in
Speaker 1: that conversion, a tab that had been or you know,
Speaker 1: a cell that had just been labeled miscellaneous got put
Speaker 1: into address instead of miscellany or title or something along
Speaker 1: those lines. And that's when it went from one point
Speaker 1: where it was a piece of data, which, as as
Speaker 1: Mike said, like, why do people even need to know this?
Speaker 1: No one needs to know this. This is this is
Speaker 1: not a relevant piece of information. It should never have
Speaker 1: gone into a database. But for that to then accidentally
Speaker 1: get transposed into a cell that is going to show
Speaker 1: up on a mailing label is incredibly it feels incredibly callous, yeah,
Speaker 1: and invasive. And it also, you know, it reminds you that,
Speaker 1: I mean, it reminds us all that the way we
Speaker 1: view ourselves isn't always the way we're viewed by the world,
Speaker 1: right and and and and that's an interesting aspect of
Speaker 1: this too, this idea that basically a data profile of
Speaker 1: most users on the Internet is being slowly put together
Speaker 1: over time, and and you might be we all might
Speaker 1: be really curious to know, um or disappointed to know, um,
Speaker 1: what that data profile looks like from the other side. Now,
Speaker 1: then let me ask you this. I'm gonna paint a
Speaker 1: picture for you of a dystopia. Okay, that's that's sadly
Speaker 1: not difficult to imagine based on our current events. All Right,
Speaker 1: so I'm sure you heard, I mean, I'm absolutely certain
Speaker 1: you heard about the app people p E E P
Speaker 1: l E. Right, Yes, this was the one that was
Speaker 1: supposed to be the yelp for people where you would
Speaker 1: be able to rate other human beings, and so then
Speaker 1: other human beings could see your rating and could see
Speaker 1: ratings of other people, and it just seemed like it
Speaker 1: was going to lead to fallout four. That's what literally
Speaker 1: an idea, an idea whose time should have never come exactly. Now,
Speaker 1: let's take that same basic concept, but instead of you
Speaker 1: rating people, imagine that you get to look up what
Speaker 1: that person's data profile is worth compared to your own.
Speaker 1: Because guess what you could do that you could build
Speaker 1: something where you start to look at the the robustness
Speaker 1: of a data profile, at least for a certain given
Speaker 1: set of transactions. Because keep in mind, there are a
Speaker 1: lot of data brokers out there, and your profile in
Speaker 1: one data broker's database may be very different from another
Speaker 1: because it may be less or more complete or from
Speaker 1: totally different sources than the other one. But that would
Speaker 1: be a weird world where you say, wow, my life
Speaker 1: is worth this much to a potential marketer, and my
Speaker 1: friends life is worth twice as much. What am I
Speaker 1: doing wrong in my life where I'm worth less? I mean,
Speaker 1: it's this is what this is how companies are looking
Speaker 1: at us. Yeah, and it is scary. Mean, I think
Speaker 1: it's also fair to say that this is as scary
Speaker 1: as it is to imagine that it is. It does
Speaker 1: also have some connection to reality. I mean, when you
Speaker 1: go into a bank and apply for a loan, um,
Speaker 1: you're also being sort of sized up, right, um, And
Speaker 1: over time, um, you know, we've we've we've seen some
Speaker 1: actually some really big problems arise from how banks look
Speaker 1: at people and look at people's ability to pay things back,
Speaker 1: and how credit card companies do this, how your net
Speaker 1: worth is calculated, how you're um, you know, your your
Speaker 1: likelihood of paying a loan back is calculated. And but
Speaker 1: at the same time, over time, there have been some
Speaker 1: safeguards put in against discrimination, right, and a lot of
Speaker 1: people have actually talked about this idea of data discrimination
Speaker 1: over time and how um, we really need to think
Speaker 1: about as we go forward and as more and more
Speaker 1: data gets collected on on on users over time. I
Speaker 1: mean people that were born, you know, in the last
Speaker 1: ten years are going to have a very different life
Speaker 1: on the Internet than even you or I have had, right,
Speaker 1: I mean I wasn't on the Internet until I was
Speaker 1: a teenager. So it's interesting to think about it. And
Speaker 1: it's also you know, you would you would hope some
Speaker 1: people are talking about this idea of like, you know,
Speaker 1: if you are perceived to be a a person of
Speaker 1: a certain race, a certain education level, tax bracketing like
Speaker 1: that the tax bracket, then you might actually see a
Speaker 1: web that is different from the web that someone else
Speaker 1: with a different perceived data profile would see. Yeah, this
Speaker 1: is this is like a dark twisted version of what
Speaker 1: the semantic web is supposed to be. The semantic web
Speaker 1: is supposed to learn about you and then respond to
Speaker 1: what you want, so that you don't have to play
Speaker 1: the game of the web search or the web browser.
Speaker 1: You just say what you want and it returns exactly
Speaker 1: what you want, possibly from multiple sources, so you're not
Speaker 1: even going to a web page in the semantic web necessarily.
Speaker 1: It may be that it's pulling things from various sources
Speaker 1: and presenting you kind of a united view. That's one
Speaker 1: realization of what those semantic web would look like. This
Speaker 1: this is similar to that, but a very dark version
Speaker 1: where it's not responding to what you want, it's responding
Speaker 1: to what the other side wants based upon what they
Speaker 1: think they can get out of you. Yeah, and that's
Speaker 1: a really scary thing. I mean there's also some potential
Speaker 1: good from that can come from this, right. Um And
Speaker 1: and you know, other people are talking about this idea
Speaker 1: of you know, a time at which maybe people actually
Speaker 1: start to you know, the data is more valuable big data.
Speaker 1: You know, it's all perceived to be more valuable to
Speaker 1: companies and marketers and and and and certain organizations in
Speaker 1: the aggregate. Right, So what if we actually reached a
Speaker 1: time where um not I won't say like data gets
Speaker 1: unionized necessarily, but like, what if you reach the time
Speaker 1: where users actually banded together and did collective bargaining on
Speaker 1: behalf of their data in order to say, hey, okay,
Speaker 1: my data maybe only half of a penny value, but
Speaker 1: when I get together with this large group of other
Speaker 1: users that I that I'm aligned with, it's actually worth
Speaker 1: a lot more. And I want to align myself with
Speaker 1: this large group and then sell that um and actually
Speaker 1: gained some agency in that way and gain maybe some
Speaker 1: some money. You're you're like creating a brand new career
Speaker 1: a data agent, Like they're like Dave Walkers. When if
Speaker 1: you want to go the vampire route, Yeah, that's that,
Speaker 1: that's I I honestly had never considered that, but that
Speaker 1: is a really compelling idea, especially when you think even
Speaker 1: if you go on a route where you're like, all right,
Speaker 1: we're not doing this to make money, We're doing this
Speaker 1: so that we're only opting into the things that we
Speaker 1: agree with that we find acceptable. One of the other
Speaker 1: things about data tracking that I think is problematic is
Speaker 1: that sure, uh, you might be getting stuff that's reacting
Speaker 1: to your behavior, but a lot of that is going
Speaker 1: to be based on guesswork. So for example, if I
Speaker 1: have posted a lot about hiking, it maybe that I
Speaker 1: get a lot of advertisements for outdoor supply stores like
Speaker 1: camping gear, that kind of stuff. And it may be
Speaker 1: that that doesn't fully overlap with my interests. That's not
Speaker 1: terrible or whatever, but it also could mean that I
Speaker 1: start to have a narrower selection of things presented to me,
Speaker 1: which in one way is good, but it also means
Speaker 1: it takes away the possibility of discovery. Yeah, this is
Speaker 1: a huge I mean, this is a really interesting idea
Speaker 1: and a really interesting topic, right. I mean, Amazon is
Speaker 1: is kind of thinking about, I think trying to do this.
Speaker 1: I mean, there there are companies that would would love
Speaker 1: to be able to basically when you go to their
Speaker 1: homepage they basically say or not that we would ever
Speaker 1: necessarily do that in the future, but you you, whatever,
Speaker 1: you you engage with their platform, they would say, here's
Speaker 1: the three products you can choose from today. Yeah, right, right,
Speaker 1: And that does, as you say, really reduce this idea
Speaker 1: of discovery. And this is you know, this happens in
Speaker 1: the media world to write this idea of like UM
Speaker 1: and in the social media world, this idea of UM
Speaker 1: being able to curate so in such a you know,
Speaker 1: a focused way to the individual user also makes it
Speaker 1: so that user doesn't i don't know, read the New
Speaker 1: York Times and and learn anything about what goes on
Speaker 1: outside of their own interest. Here it becomes it becomes
Speaker 1: like the ultimate echo chamber, where where you know you're
Speaker 1: you're you're stuck there seeing the same stuff and maybe
Speaker 1: you don't even realize that we're we're heading to like
Speaker 1: this is almost a Terry Gilliam film what we're describing
Speaker 1: at this right, Like we're heading to Brazil right now,
Speaker 1: let's head. Let's say I'm ready for that movie. I mean,
Speaker 1: I don't want to be in the reality that it depicts.
Speaker 1: I don't want to be I don't want it to
Speaker 1: be reality. But I would totally watch that movie. Yeah,
Speaker 1: me too. When we're talking big data, when we're talking
Speaker 1: about all the information that's going out there, and we
Speaker 1: mentioned the fact that companies are buying profiles in the
Speaker 1: tens or hundreds of thousands or more. Here's why. Let
Speaker 1: me give you a few statistics on how much information
Speaker 1: is hitting the Internet on a per minute basis. This
Speaker 1: is based off of research that was conducted by a
Speaker 1: data company called Domo, and this was published in August
Speaker 1: of two thousand fifteen, So keep in mind these numbers
Speaker 1: are already old. But every single minute, Amazon receives four
Speaker 1: thousand three unique visitors. So Amazon is one of those
Speaker 1: companies that's really good at doing this focused data tracking
Speaker 1: that you know, this is where you buy a product
Speaker 1: and it says, hey, people who bought this product also
Speaker 1: bought these other things, and then you think, oh, right, right,
Speaker 1: I should get that. It's like it's like the perfect
Speaker 1: impulse by shelf, and it's customized for every single purchase. Right,
Speaker 1: I do need batteries and dog biscuits. How did you know, Well,
Speaker 1: there's there's there's there's this other level two uh that
Speaker 1: that people are starting to imagine when you think about
Speaker 1: companies like nest Um, and you think about smart televisions
Speaker 1: that actually interact with your refrigerator. And one of the
Speaker 1: most convincing sort of imagined scenarios that was ever presented
Speaker 1: to me in recent years was this idea that essentially,
Speaker 1: over time, your refrigerator learns that you go for ice cream.
Speaker 1: You go in for that vanilla ice cream, um, right
Speaker 1: around eight pm, and um, it tells your television that.
Speaker 1: And then in the world of streaming that you know,
Speaker 1: more and more people are doing on their actual television
Speaker 1: instead of having it connected to a you know, a
Speaker 1: cable channel. Um, you actually get at eight forty five,
Speaker 1: you get an advertisement for ice cream, which reminds you
Speaker 1: to go and eat some ice cream, and then you
Speaker 1: end up eating more ice cream, and then you end
Speaker 1: up buying This is the world where the appliances are
Speaker 1: working against you. Yes, they're not telling you to eat
Speaker 1: a kale sale, or maybe they are. Yeah, that's also possible.
Speaker 1: Like if you have if you have this thing where
Speaker 1: you're going out and buying kale salads and then you're
Speaker 1: watching a lot of you know, food type stuff, and
Speaker 1: you're you might end up getting Hey, here's a quick recipe,
Speaker 1: and here are the fourteen ingredients you don't have that
Speaker 1: you need to go to Whole Foods and buy. Um. Yeah,
Speaker 1: it's a world. I host a show. I host another
Speaker 1: show called Forward Thinking, and in that show it's it's
Speaker 1: an optimistic view of the future. And I have often
Speaker 1: looked at this side from the optimist side, and it
Speaker 1: is similar to what you were saying about. Every single
Speaker 1: person's web experience could be unique to them based upon
Speaker 1: these factors. Uh. And and there's a way of doing
Speaker 1: that where it is not kind of the scary, oppressive
Speaker 1: type of way. But I I talk about how with
Speaker 1: the Internet of things and with this connectivity of different appliances,
Speaker 1: it's not that far of a stretch to a man,
Speaker 1: not just the web, your experience of reality could get
Speaker 1: to a point where a lot of it is catered
Speaker 1: specifically to you. Yeah, and there could be a lot
Speaker 1: of good things that come out of that, right, um
Speaker 1: and and and and I totally believe in that possibility.
Speaker 1: I mean, like like we like you said, I think
Speaker 1: so um so thoughtfully when you came in and and
Speaker 1: and uh talked about this stuff with me on Codebreaker,
Speaker 1: was was basically this idea that look, it's um I'm
Speaker 1: not going to pull out the quotes so eloquently as
Speaker 1: you did, but look, this technology can it's us that
Speaker 1: makes it good or bad? Right we We we have
Speaker 1: the power theoretically too to make all of this stuff
Speaker 1: and make data tracking work for us as users and
Speaker 1: work for us as organizations and companies. I mean, I
Speaker 1: think a lot about the possibility of data tracking being
Speaker 1: really good in a few sure where our climate is changing.
Speaker 1: Um and and the fact that we really understand on
Speaker 1: a global level a lot more about our climate than
Speaker 1: we did even thirty years ago thanks to an increased
Speaker 1: level of data tracking, right right, And and that is
Speaker 1: an important point to make, Ben, is that data tracking
Speaker 1: does not necessarily mean it's about just people. Although people
Speaker 1: are an important factor when you're looking even at climate change.
Speaker 1: Because you want to see, well, how is this affecting
Speaker 1: actual human beings as well as the environment in general.
Speaker 1: You need to know the whole picture. But data tracking
Speaker 1: is really it's it's agnostic as far as what the
Speaker 1: data actually represents. Like we're talking about data about everything,
Speaker 1: you know, anything that you can imagine that can be
Speaker 1: quantified in the form of data that is being tracked
Speaker 1: by someone for some reason. Some of those reasons are good,
Speaker 1: some of those reasons are way not good. But but
Speaker 1: it's not all just about people. And it's interesting too
Speaker 1: to think about I wonder what you think about this,
Speaker 1: But it's interesting too to think about just how I
Speaker 1: think a lot of it feels out of control at
Speaker 1: this point for the user. It feels like we don't
Speaker 1: fully understand it. We don't fully understand how it's being used.
Speaker 1: Um and what data about us is being tracked as
Speaker 1: users and um and you know, government, I mean the
Speaker 1: FTC in the past few years has become much more
Speaker 1: involved in this idea of helping consumers protect themselves against
Speaker 1: you know, abusive forms of data traction um if you will,
Speaker 1: um and and there are a lot of qui and
Speaker 1: and and also it's fair to say that. I think
Speaker 1: a lot of tech companies have endeavored to put not
Speaker 1: only in terms of service agreements, but in the actual
Speaker 1: tools that they give to the user when they manage
Speaker 1: their own accounts, as you know, to put the power
Speaker 1: in the user's hand ends to control this. But you know,
Speaker 1: let's be honest, right, I mean, you and I don't
Speaker 1: necessarily read all of the terms of services. You're telling
Speaker 1: me you don't read every end user license agreement. You don't.
Speaker 1: You can't recite the you know, it's it's tough. It's tough.
Speaker 1: I can't remember the last time, Honestly, I cannot remember
Speaker 1: the last time I actually read ULA because who who
Speaker 1: has time for that? I want to use your shiny
Speaker 1: new toy. I don't care that I've given up all
Speaker 1: my privacy and if I as long as I get
Speaker 1: to play with the shiny new toy. If my b
Speaker 1: B eight remote control droid is secretly telling Disney exactly
Speaker 1: how to make me buy more BB eight remote control droids,
Speaker 1: so be it. Um. It all comes back to Star Wars. Well,
Speaker 1: I mean, you know, the force is strong in my family.
Speaker 1: My father has it, I have it, my sister has it.
Speaker 1: So uh, you know. This is. This has been a
Speaker 1: great conversation, great talk about this topic. And uh, you
Speaker 1: know again, if you guys haven't checked out Code Breaker,
Speaker 1: you definitely need to remedy that immediately. It is a
Speaker 1: phenomenal series that covers various topics. And this is your
Speaker 1: first season, right, this is our first season, Jonathan, and
Speaker 1: we're looking forward to having you back on for our
Speaker 1: second season, which is coming soon. Um. But but yeah,
Speaker 1: it's it's been a lot of fun and and it
Speaker 1: was it was great fun to talk to you on
Speaker 1: Codebreaker and um, and have you be a part of
Speaker 1: the show, and and we hope you'll you'll keep listening absolutely,
Speaker 1: And guys, you also have to know this thing about
Speaker 1: Code Breaker because it's a gimmick that's so good. I
Speaker 1: was mad at myself for never thinking about it. This
Speaker 1: is it was brilliant you you produced, you had all
Speaker 1: of the shows produced and ready to go, and you
Speaker 1: hid codes in the episodes. And if you're able to
Speaker 1: break the code, you can listen to the next episode
Speaker 1: before it publishes. Yes, well we should start, you know,
Speaker 1: I mean, you know I'm ready to have we embedded
Speaker 1: a secret code in this episode, John, you know I
Speaker 1: like to say yes just to drive people crazy, but
Speaker 1: I'm not gonna do that. I was just so proud
Speaker 1: of myself for for breaking the the Morse code. In
Speaker 1: the you did an episode about internet porn, which is
Speaker 1: an incredible episode. Probably not safe for you to listen
Speaker 1: to on speakers if you have, not safe for work
Speaker 1: or for little ones, obviously for for obvious reasons. But
Speaker 1: it's a real honest look at what is not only
Speaker 1: a big industry online but legitimately it can make or
Speaker 1: break certain types of of on the edge technologies. Like
Speaker 1: if you wonder why Blu ray one over h D
Speaker 1: d v D, it's one of the big reasons. I mean,
Speaker 1: you know, it's it's crazy to think about that. If
Speaker 1: you wonder why the A v N conference overlapped with
Speaker 1: CS for so many years, this is why. It's an
Speaker 1: incredible episode and I managed to actually break the code,
Speaker 1: and I was probably a little more proud of myself
Speaker 1: than I should have been. Well, now you should, I
Speaker 1: mean you should be proud. It's Morris code. It's it's
Speaker 1: it's not used, um, you know, very very widely anymore
Speaker 1: by by at least most people. But it's uh, it's
Speaker 1: always a good thing to know. Yeah, it turns out
Speaker 1: you know, once you once you commit that scout stuff
Speaker 1: to memory and have and have a web page opened
Speaker 1: up that tells you what the dots and dashes me
Speaker 1: because I didn't remember, but but it's still it's it's
Speaker 1: a it's a great way to engage the listeners on
Speaker 1: top of this interesting take you have on the different topics.
Speaker 1: So guys, make sure you go and check that out. Ben,
Speaker 1: Thank you so much for agreeing to be part of this.
Speaker 1: This was I think a really fun kind of complimentary
Speaker 1: piece to your Codebreaker episode, which again, uh, I really
Speaker 1: enjoyed you guys. Let me listen to it before I
Speaker 1: was even I recorded the final segment on that episode
Speaker 1: and I got to listen to everything up to that point,
Speaker 1: and I was just I was like, are you sure
Speaker 1: you want me? You're bat and clean up man, you
Speaker 1: are are close up. We love im. Glad, glad I
Speaker 1: could could be of service. It was an honor. So well,
Speaker 1: it was great to have you. It was great to
Speaker 1: have you on and when we're big fans of tech stuff,
Speaker 1: so we're like, um, it was thank you so much
Speaker 1: for for having me and for for talking to me
Speaker 1: about this stuff, and I think, I don't know, man,
Speaker 1: I you know, I end up feeling you know, I
Speaker 1: think so many of us end up healing lost right
Speaker 1: about when it when it comes to data tracking. And
Speaker 1: my only hope is that is that I mean not
Speaker 1: to say that there should be an app for that,
Speaker 1: but you know, I'm my only hope is that we
Speaker 1: will at some point reach a reach a stage where
Speaker 1: it's easier for the user to to really have control
Speaker 1: over what they're sharing and and and also users are
Speaker 1: more more educated about what they're sharing. Um, and I
Speaker 1: think I think we're all, you know, we're all a
Speaker 1: little bit on the hook for for not being more
Speaker 1: educated about what we're sharing. But we're also you know,
Speaker 1: we also need better tools to understand how we are
Speaker 1: sharing our data and how it's being trapped and we
Speaker 1: and we definitely need to hold government accountable for making
Speaker 1: sure they create and foster departments that are specialized in
Speaker 1: informing other bodies of government about these things, keeping up
Speaker 1: to date, because as we know, I mean, we're we're
Speaker 1: still seeing an era where people who are in positions
Speaker 1: of power often they have limited to no experience with
Speaker 1: a lot of the things that are affecting us on
Speaker 1: a daily basis. Oh man, those those those uh, those
Speaker 1: Supreme Court transcripts. Man, it's a sad world. So if
Speaker 1: we're gonna like, we can't wait until the millennials are
Speaker 1: in the Supreme Court for one thing, I'm gonna be
Speaker 1: dead by then, So I would like to see things
Speaker 1: improve before that. But that's that's one of those real
Speaker 1: challenges is that we have a different population and positions
Speaker 1: of power who they may have every desire to help
Speaker 1: us make sure that these things are being used in
Speaker 1: a responsible way, a non predatory, and an ethical way,
Speaker 1: but they have, you know, to them, it's it's like
Speaker 1: if you were to present me with a page written
Speaker 1: in Greek, I would have no idea what to do
Speaker 1: with that. So what technology moves faster than policy? And
Speaker 1: and and and I think it's it's as true with
Speaker 1: data tracking as anything else. And it's just, um, it's
Speaker 1: a reality that we all have to deal with. But
Speaker 1: it's it's it's something that hopefully will will change over time,
Speaker 1: and it's not not not before, not before. A lot
Speaker 1: of stuff happens that many people may regret. So it's
Speaker 1: it's always good to be thinking about it. Yeah, absolutely, Uh,
Speaker 1: you know, I'm gonna have to have you back on
Speaker 1: the show. Maybe we can talk a little bit more
Speaker 1: about some of these other elements where technology has severely
Speaker 1: outpaced policy. I mean, autonomous cars is easily, that's easily
Speaker 1: one of the top ones to talk about, right Like
Speaker 1: they're and they're not even really out yet, and they're
Speaker 1: already outpacing policy. I mean, it's and and I mean
Speaker 1: I I am very much of the selfish opinion that
Speaker 1: I want autonomous cars here now. I want to be
Speaker 1: able to do right now. But but even then I recognize,
Speaker 1: like it's it's not it's not as easy an answer
Speaker 1: as saying, let's wait till the technology is ready. The
Speaker 1: technology is ready, we've got other things we have to fix. Yeah, yeah, yeah, absolutely, Well, happy,
Speaker 1: happy to talk to you any time, and thanks so
Speaker 1: much for for having me up. Yeah now, Ben, tell
Speaker 1: tell my listeners where they can find you so that
Speaker 1: they can actually seek out your brilliance apart from this, well,
Speaker 1: I don't know about that, but I'm I'm on Twitter
Speaker 1: at the brock Johnson are our show Codebreakers at Codebreaker
Speaker 1: dot Codes or you can find it on iTunes or
Speaker 1: wherever you get your podcasts. Uh and you know, right
Speaker 1: along tech Stuff and How Stuff Works. We're we're in
Speaker 1: there with you guys, so so you can find us,
Speaker 1: we're there, Thanks so much, and guys listen. If you
Speaker 1: have any suggestions for future episodes of tech Stuff, whether
Speaker 1: it's another guest host, someone I should interview, whether it's
Speaker 1: a company or technology, whatever it may be, write me
Speaker 1: let me know what you think. Text stuff at how
Speaker 1: stuff works dot com. Is that email address. You can
Speaker 1: also drop me a line on Facebook, on Twitter, or
Speaker 1: on Tumblr. We are tech stuff h s W at
Speaker 1: all three of those and I'll talk to you guys
Speaker 1: again really soon for more on this and thousands of
Speaker 1: other topics because a stock works dot com