Speaker 1: Oh man, this is a doozy. This is one of
Speaker 1: the ones that we we predicted nihilistically would become incredibly normalized.
Speaker 1: Remember all these years ago facial recognition, Oh absolutely, when
Speaker 1: it was just sort of like a glint in the
Speaker 1: eye of of the Zuckster, you know, I mean like
Speaker 1: it was about like, oh no, we're scared that they're
Speaker 1: using facial recognition on our Facebook pictures. But now we're
Speaker 1: actively feeding these AI apps like Lensa, you know, our
Speaker 1: data so that they can improve facial recognition and recreation algorithms. Yeah,
Speaker 1: and this is really an exploration of the tech that's
Speaker 1: making it work, right. That's what we brought on Jonathan Strickland,
Speaker 1: our buddy from Tech Stuff, to really go through all
Speaker 1: of that minutia. And we're gonna listen back along with
Speaker 1: you to see, uh, just how our predictions evened out.
Speaker 1: So I hope you enjoy it and think about who
Speaker 1: gets to look at your face on UFOs too, Psychic
Speaker 1: powers and government conspiracies. History is riddled with unexplained events.
Speaker 1: You can turn back now or learn the stuff they
Speaker 1: don't want you to know. Welcome back to the show.
Speaker 1: My name is Matt. They called me Ben, you are you,
Speaker 1: and that makes this stuff they don't want you to know. Listeners,
Speaker 1: you're probably wondering whatever happened to the other guy on
Speaker 1: the show. Don't worry. Our third amigo, Nol will be
Speaker 1: back in due time. Today, however, we've pulled a switcheroo
Speaker 1: on you. You see, this is not the ordinary episode
Speaker 1: of stuff they don't want you to know. Today we
Speaker 1: are joined by a very special and returning guest, Ladies
Speaker 1: and gentlemen, our resident tech expert, Jonathan Strickland. I'd like
Speaker 1: to think this is an extraordinary episode of stuff they
Speaker 1: don't want you to know. And uh, I am here
Speaker 1: to talk about stuff they legit do not want you
Speaker 1: to know. That's true. That's true, Jonathan, thank you for coming.
Speaker 1: You know one thing, one thing that we can say
Speaker 1: about Jonathan off the bat is that he has a
Speaker 1: face that is more recognizable than most. You host a
Speaker 1: show called tech stuff, you host a show called forward Thinking,
Speaker 1: You've worked with Everyday Science on brain stuff, and you
Speaker 1: regularly hit the streets on C, S and E three. Yep,
Speaker 1: these are all true. I I often will get maybe
Speaker 1: not often, but frequently enough, get recognized simply because my
Speaker 1: face has been in a lot of places and associated
Speaker 1: directly with my identity. It's not just that, hey, who's
Speaker 1: that guy in the background of every Avengers movie or whatever.
Speaker 1: I'm someone who my face is tied to who I
Speaker 1: am frequently enough that people recognized me for the person
Speaker 1: I am. Most people, I would argue, probably don't experience
Speaker 1: that to any great degree, apart from you know, they're
Speaker 1: they're close friends and acquaintances, that sort of thing whatever,
Speaker 1: and their exes. Yeah, the enemies that they have ranked
Speaker 1: up as people may or may not be aware. Ben
Speaker 1: and I are longtime enemies. I declared you as such
Speaker 1: within my first couple of months of working and how
Speaker 1: stuff works well, yeah, but also to be fair, I
Speaker 1: push people. Matt can attest to that. So most people
Speaker 1: would think there's not a real great chance they would
Speaker 1: be recognized whenever they go out in public. But here's
Speaker 1: an interesting fact that you may or may not know.
Speaker 1: In the United States, one out of every two adults
Speaker 1: is actually their faces actually tied to their identity in
Speaker 1: some law enforcements database. That law enforcement agency might be local,
Speaker 1: might be federal. Most of them are linked together loosely
Speaker 1: in the sense that if one law enforcement agency wants
Speaker 1: to run a recognition UH test on a suspect photo,
Speaker 1: they can get that done within other law enforcement agencies.
Speaker 1: So you can, in theory, have half of the adult
Speaker 1: population of the United States as your virtual lineup when
Speaker 1: you are looking for a match on a photo. So,
Speaker 1: if you are an adult in the United States, there's
Speaker 1: a coin flip chance that your faces in one of
Speaker 1: these databases. If you've committed a crime, or if you've
Speaker 1: been accused of committing a crime in the United States,
Speaker 1: there's essentially a chance that your face is in one
Speaker 1: of these databases. So that's what we're talking about today.
Speaker 1: Ladies and gentlemen. You will remember at the close of
Speaker 1: our previous episode, we asked you to consider how many
Speaker 1: times your face has appeared on a camera? Right, how
Speaker 1: many times a day does a camera perceive your visage.
Speaker 1: I would argue that a lot of people don't want
Speaker 1: to be recognized casually for one reason or another, perfectly legitimate,
Speaker 1: perfectly legal reasons. Yeah, if I'm if I'm walking around
Speaker 1: a place and I just want to duck into a store,
Speaker 1: I don't necessarily want the entire world to know that
Speaker 1: I went into that store. Let's say that I'm shopping
Speaker 1: for a surprise gift for my wife, totally innocent, nothing
Speaker 1: wrong about that. Then I don't necessarily want that information
Speaker 1: to get back to my wife and thus ruin the surprise.
Speaker 1: Or it could be it could be anything that you
Speaker 1: would argue is on the further end of the spectrum.
Speaker 1: At any rate, we have a certain expectation of privacy
Speaker 1: in here in the United States that I think is
Speaker 1: largely based off of wishful thinking at this point. So
Speaker 1: what is now philosophic the cool Yeah, waxing about privacy
Speaker 1: or the nature of it or how long it's existed.
Speaker 1: Is uh, it's sort of a hobby Matt and I
Speaker 1: on the show. I guess we should start, as the
Speaker 1: Mad Hatter said at the beginning. Sure, So, what what
Speaker 1: do we mean when we say facial recognition? What is this?
Speaker 1: So this was something that people were starting to work
Speaker 1: on very seriously in the nineties and early two thousand's
Speaker 1: because it required a great deal of processing power back
Speaker 1: in those days to achieve what now we can do
Speaker 1: much more simply with new types of computer architecture. But
Speaker 1: your basic approach has been the same, which is that
Speaker 1: you create a program and algorithm that identifies special anchor
Speaker 1: points on an image, such as a face, and identifies
Speaker 1: this looks like an eye. So I'm going to assume
Speaker 1: that this is an eye. So here's the other eye,
Speaker 1: here's the no is, here's the mouth. Based upon the
Speaker 1: relationship of these various components within a face, I would
Speaker 1: assign a numeric value more or less a numeric value
Speaker 1: to this. It's called a face print, but it's essentially
Speaker 1: the sum total of all the different features that that
Speaker 1: particular algorithm is looking for. And just to get this
Speaker 1: out of the way, there are a lot of different
Speaker 1: algorithms out there, made by a lot of different companies,
Speaker 1: so not everyone does it exactly the same way. Everyone
Speaker 1: claims that there's the best, but they all use similar
Speaker 1: but not identical methods. You then take this information, this
Speaker 1: face print, this numeric value you have created, and you
Speaker 1: run it against a database of previous face prints that
Speaker 1: are tied to known individuals. So the picture you take
Speaker 1: is called a probe photo. That is, the photo you
Speaker 1: are probing the database to see if you can find
Speaker 1: a match. You look and see if you find a
Speaker 1: close numeric match match between the two. The probe photo
Speaker 1: and all of your your collected images that are in
Speaker 1: your database that you have, you know who those belong to.
Speaker 1: So photo pro photo is of a person of interest.
Speaker 1: Might not be a suspect, but it's certainly someone you
Speaker 1: would like to talk to if you had a chance,
Speaker 1: because they're involved in a investigation in some way. You
Speaker 1: look against the database, you try and find a match numerically.
Speaker 1: At that point, at least with most systems, but not
Speaker 1: withal you would then have a human being, a real
Speaker 1: life person, comparing these images to each other to see
Speaker 1: if they actually represent a match of this photo looks
Speaker 1: like yes, it is in fact the person who's in
Speaker 1: the database. Because people can look very similar, people can
Speaker 1: look very similar, and but depending upon how the algorithm
Speaker 1: breaks down the face, it may mistakenly think that two
Speaker 1: people are the same, even if you were to look
Speaker 1: at them in person and say, well, that's clearly not
Speaker 1: the same person, and I mean I can tell that's
Speaker 1: not the same person. There are times where that happens. Uh.
Speaker 1: It also has a lot of other factors, such as
Speaker 1: the picture you take is the person facing face onto
Speaker 1: the camera If it's at a slight angle, that changes
Speaker 1: things as well. Right. Uh, there have been advances in
Speaker 1: algorithms where it's able to take better guesses as to
Speaker 1: the three dimensionality of a face. But previously it was
Speaker 1: very much a two dimensional image, which meant that you
Speaker 1: could fool it. You could fool it either on purpose
Speaker 1: or by accident. So this doesn't have to do with
Speaker 1: law enforcement. But in Japan they incorporated facial recognition software
Speaker 1: in vending machines that sold cigarettes because the idea was
Speaker 1: that if you appeared to be too young, the vending
Speaker 1: machine wouldn't sell cigarettes to you. But if you just
Speaker 1: held up a photo of someone who was old enough
Speaker 1: to purchase cigarettes, it could Because it couldn't detect depth,
Speaker 1: it was entirely just two dimensionals. Is this a face
Speaker 1: of a person over what is eighteen or whatever? Right?
Speaker 1: And now it's gotten to a point where because of
Speaker 1: camera sophistication, there are a lot of cameras that can
Speaker 1: incorporate two lenses, which gives it that that by optic
Speaker 1: a view, a parallax view that can simulate yeah, exactly,
Speaker 1: you can simulate that three dimensional vision. And there are
Speaker 1: algorithms that can take advantage of that. That has been
Speaker 1: decreased somewhat it is not as easy to fool, at
Speaker 1: least a sophisticated machine, but it can still happen. Now. Earlier,
Speaker 1: at the very beginning, you gave everybody a pretty harrowing statistic. Yeah,
Speaker 1: the what did you call a coin flip chance. Yes,
Speaker 1: there's a coin flip chance that if you are an
Speaker 1: adult in the US, your face is in one of
Speaker 1: these database. If you've never done anything wrong, yeah, you
Speaker 1: don't have to do anything. But really, all you have
Speaker 1: to have done is applied for some sort of license
Speaker 1: or accreditation that also includes your photo passport, driver's license,
Speaker 1: State I D. There are a lot of security clearances
Speaker 1: that do this. So let's let's back up a little
Speaker 1: bit and and let me explain the landscape, because it
Speaker 1: is complicated, right. It's not as simple as there's this
Speaker 1: one massive computer system at Langley and that's where your
Speaker 1: face is. It's much more distributed than that, much like
Speaker 1: the Internet itself. So the FBI has the Interstate Photo System.
Speaker 1: This was sort of a an evolution of their national
Speaker 1: fingerprint database. And I think you would say, all right, well,
Speaker 1: I understand the fingerprint database. They've collected fingerprints from various
Speaker 1: criminal investigations. This speeds things along when they are investigating
Speaker 1: a new crime. This, however, is more about faces less
Speaker 1: about fingerprints. So now what we're looking at is a
Speaker 1: system where if the FBI uses their normal method of operations,
Speaker 1: in theory, at least you probably would feel a little
Speaker 1: less creepy about all this. And that's because if they're
Speaker 1: using their basic method of running a search, what the
Speaker 1: FBI would do is take a probe photo from an investigation.
Speaker 1: They would then run it against their database, which includes
Speaker 1: both criminal and civil photos. The civil photos some of
Speaker 1: those are tied to some of the criminal photos. If
Speaker 1: they have the same picture of a person from a
Speaker 1: criminal investigation and from say a license that they had
Speaker 1: applied for that then both of those will be tied
Speaker 1: together in the database when they run a search. The
Speaker 1: search only looks for matches in the criminal side. It
Speaker 1: cannot look for matches on the civil side. So if
Speaker 1: your ideas in the FBI's uh interstate photosystem, but it's
Speaker 1: only in there as an example of a civil photo
Speaker 1: and you have no criminal record to tie it to,
Speaker 1: it should not pull you as a result. So this
Speaker 1: leads to an immediate question. We're talking about the division
Speaker 1: between civil and criminal images here facial images, at least
Speaker 1: as it pertains to the FBI. Do they not automatically
Speaker 1: correlate because of a matter of ability or because of
Speaker 1: a matter of legislation. They do it as a policy
Speaker 1: saying we we don't want to we don't want to
Speaker 1: bring in the civil photos. Trust us. But this was
Speaker 1: the basic search. I haven't gotten to their other search,
Speaker 1: and we'll dive into that after a word from our sponsor,
Speaker 1: and we're back. So for everybody who is just now
Speaker 1: becoming acquainted with us, we've we've heard all of this
Speaker 1: stuff people have seen like crime shows, c s I
Speaker 1: or whatever, and uh, facial recognition and action films and
Speaker 1: facial recognition and science fiction. And what we've talked about
Speaker 1: so far is a basic search. We've explained the technology,
Speaker 1: and we've explained a little bit about the evolution. I
Speaker 1: think it would surprise people how quickly this has evolved.
Speaker 1: But what's after the basic search? So all right, the
Speaker 1: FBI S General m O is using this this criminal
Speaker 1: database and avoiding the civil database, uh, when they're doing
Speaker 1: their own searches. But they also have a division called
Speaker 1: the Facial Analysis, Comparison and Evaluation Services or FACES want
Speaker 1: to do. The acronymic FACES is kind of like there
Speaker 1: their specialty division where they want to run a more
Speaker 1: UH more extensive search. So the FACES operators can run
Speaker 1: a search about that that incorporates both criminal and civil
Speaker 1: UH sources, and they can work with local state law
Speaker 1: enforcement agencies, will not just state, but tribal law enforcement
Speaker 1: agencies as well. In the United States, they have a
Speaker 1: they have this ability to work with all of them,
Speaker 1: to partner with them. Um, it's kind of a tip
Speaker 1: for tat sort of approach. Whenever anyone needs to run
Speaker 1: one of these, they can run it up the chain
Speaker 1: and see if they can get a bigger net to
Speaker 1: to pull from. Right, So what will happen is you
Speaker 1: would send a request to the FACES department. They could
Speaker 1: run it against not only the FBI's database, which is
Speaker 1: it's extensive, but that is not the one in every
Speaker 1: two Americans databases. That that statistic comes from the collection
Speaker 1: of databases across the United States at all different levels
Speaker 1: of law enforcements. So again, state, tribal, city, UH, federal,
Speaker 1: all of those different levels combined, that's where you get
Speaker 1: the one in two. But the FBI essentially has access
Speaker 1: to most of that because of these relationships they've developed
Speaker 1: with various other entities. So you would send a request
Speaker 1: to FACES. FACES would then handle the request sent out
Speaker 1: to other agencies, right so within they would do the
Speaker 1: all the FBI search, but they would also say, Hey,
Speaker 1: State of Utah, can you do this search? City of
Speaker 1: Las Vegas can you do this search? You know what
Speaker 1: we're gonna expand this, California can you do this search?
Speaker 1: And you start again casting a wider net. Well, you
Speaker 1: start running into problems very very quickly with this approach. Well,
Speaker 1: multiple multiple avenues. Let's start with technology. You remember earlier
Speaker 1: I mentioned that no, no facial recognition algorithm does the
Speaker 1: approach the exact same way as a different one, right like,
Speaker 1: So it's not like there's just one way to achieve
Speaker 1: facial recognition technology and some may be better than others.
Speaker 1: And the real problem is that we don't really know
Speaker 1: because most of these companies haven't had a third party
Speaker 1: come in and evaluate the software for accuracy. So there's
Speaker 1: no UM, there's no quality assurance, there's no accountability at all.
Speaker 1: So one from a technology standpoint, you cannot be certain
Speaker 1: that everyone is on the same playing ground, like they're
Speaker 1: on the same level. They may all be using different technologies,
Speaker 1: some of which may be more effective than others. That's
Speaker 1: problem number one. Just to add on to that, one
Speaker 1: of the larger problematic trends in many uh many government
Speaker 1: agencies attempt to implement technology is the arrival and persistence
Speaker 1: of outdated legacy stuff because they're locked into some service
Speaker 1: agreement or contract for or they just don't have budget
Speaker 1: for it. Like if you're running if you're running old servers,
Speaker 1: old computers, and they aren't capable of running the most
Speaker 1: up to date versions of software that's out there, then yeah,
Speaker 1: that's yet another layer of technology issues. Yes, ladies and gentlemen,
Speaker 1: if this sounds kind of weird to you, um, one
Speaker 1: of the one of the moments in American pop culture
Speaker 1: when the average non military, non law enforcement officer person
Speaker 1: learned about this was actually watching the wire when they
Speaker 1: had to use typewriters. Do you remember that? And this
Speaker 1: is just another example of this. So not only are
Speaker 1: their various forms of facial recognition algorithms and technology, some
Speaker 1: of which may be proprietary, all of it is proprietary.
Speaker 1: And not only do these not all's play nice together.
Speaker 1: I guess or agree at times. But some are I'm
Speaker 1: overwhelmingly certain. Some are pretty outmoded, Yeah, effectively obsolete. Some
Speaker 1: of them are. And again you don't necessarily know unless
Speaker 1: you are getting regular updates from all the different agencies
Speaker 1: out there. There are thousands of these law enforcement agencies
Speaker 1: right Like, it's not we're not talking about others. There's
Speaker 1: fifty of them, because there's fifty states. No, there's any
Speaker 1: more than that. And you're talking about databases that are
Speaker 1: not necessarily directly connected to one another. So each one
Speaker 1: is its own little island with its own little like.
Speaker 1: Some of them may be using the same facial recognition software,
Speaker 1: some of them may be using different versions of the
Speaker 1: same software. Uh Now, if if they all worked, if
Speaker 1: all the facial recognition software worked flawlessly, this would not
Speaker 1: be an issue. It wouldn't matter if you went and
Speaker 1: bought from Company A versus Company B, or if you
Speaker 1: had Company a's one point over version versus company as
Speaker 1: one point five version. If they all worked perfectly, that's
Speaker 1: not an issue. But we don't know because we haven't
Speaker 1: had these third party audits for a lot of those technologies.
Speaker 1: You get a lot of claims like You'll see a
Speaker 1: company say we're accurate, and then you say, well, well,
Speaker 1: where's the proof of that? And they say trust us,
Speaker 1: And then you say, well, can we hold you to
Speaker 1: that in court? And like, oh no, our terms of
Speaker 1: service say you can't. We can't be legally held accountable
Speaker 1: for the claim that we make that it is accurate.
Speaker 1: That's an issue, right, So that's all technology. There's another
Speaker 1: technology issue that gets super uncomfortable. What's that most of
Speaker 1: the time, facial recognition software is not equally accurate across
Speaker 1: all ethnicities because the way people choose these algorithms and
Speaker 1: the way they designed the faces, you know, the facial
Speaker 1: recognition that the element that they're looking for. It may
Speaker 1: be that in one ethnicity you see a greater variety
Speaker 1: of the features that were chosen to be the elements
Speaker 1: that you're looking for versus others. So within one ethnicity,
Speaker 1: you might see a greater variation in something like cheekbones.
Speaker 1: Let's say the cheekbone size, the cheekbone like, the prominence. Yeah,
Speaker 1: that kind of stuff might be really important with or
Speaker 1: at least there might be a greater variety within one
Speaker 1: ethnicity and less in another. Well, if your technology is
Speaker 1: focusing on that, and it doesn't focus so much on
Speaker 1: the other qualities that have a greater variety within a
Speaker 1: different ethnicity. You're gonna get a lot of false positives
Speaker 1: because the technology you're looking at, if it's looking at
Speaker 1: a subject from that ethnicity that doesn't have great representation
Speaker 1: in your technology, you're gonna get a lot of false
Speaker 1: positive identifications because the tech can It's like, it's like
Speaker 1: that that statement about someone being racist. They cannot tell
Speaker 1: the different between two different individuals from the same race.
Speaker 1: They don't see the difference because they have become so
Speaker 1: ethnically minded of their own ethnicity. They only recognize the
Speaker 1: differences that are representative of their respect by ethnicity. And
Speaker 1: this is what's what's fascinating and terrifying about this. I
Speaker 1: think the most immediate fascinating and terrifying thing is that
Speaker 1: we see again that software is limited by the creator.
Speaker 1: Technology is limited by we all make these amazing tools
Speaker 1: based on our own parameters. We very quickly make an
Speaker 1: assumption that something that's that's working on what appears to
Speaker 1: be cold hard logic is infallible, right like that that
Speaker 1: emotion that things like uh a rationality don't play a
Speaker 1: factor except for the fact that they played a factor
Speaker 1: when we made this stuff, and it didn't necessarily from
Speaker 1: a conscious decision. It may have been something that just
Speaker 1: in the design elements people were trying to figure out,
Speaker 1: all right, well, what components are the ones we need
Speaker 1: to really focus on in order for our technology to work.
Speaker 1: And some guy leaned in, took off his clan hood
Speaker 1: and said, cheek bones. Yeah, that's a very I mean,
Speaker 1: probably not quite as a as extreme as that, but
Speaker 1: the point being that it there have been some some claims,
Speaker 1: at least I don't know about any deep studies, but
Speaker 1: claims that facial recognition technology disproportionately affects people of UH
Speaker 1: specific ethnicities, particularly black people. They have been uh, disproportionately
Speaker 1: affected by this false positives. Yeah, absolutely, yeah. And and
Speaker 1: also there it's that goes down a road that is
Speaker 1: far beyond the scope of this episode, because we'd have
Speaker 1: to start talking about the vast gap uh in the
Speaker 1: experience of being say, a white person in the United
Speaker 1: States and being the subject of a law enforcement investigation
Speaker 1: and being a black person. There's huge issues there that
Speaker 1: really go beyond what I'm talking about here. Uh, And
Speaker 1: I'm not gonna jump into that because I'm sure you've
Speaker 1: covered those sort of things in previous episodes. If you're
Speaker 1: interested in learning more about that, you can look at
Speaker 1: the Center on Privacy and Technology at Georgetown Law. They
Speaker 1: have a website called Perpetual Lineup dot org and it
Speaker 1: goes through a lot of what you've been talking about. Yeah,
Speaker 1: it's it is a real thing. I want to stress
Speaker 1: that it is a real thing. People sometimes will think
Speaker 1: that I've encountered people with resistance to it because they
Speaker 1: don't like the idea of an inherently unfair system. It
Speaker 1: goes against some of their um their own personal values,
Speaker 1: which I completely understand. But it is a real thing. Yeah. Yeah. Well, also,
Speaker 1: every this is gonna be Ben's blanket statement for the episode.
Speaker 1: Always end up having one, and please write to me
Speaker 1: if you disagree. All systems are inherently, on some level unfair. Yes, yeah,
Speaker 1: because systems parse and we don't have we don't have
Speaker 1: a perfect way of incorporating every possible life experience and
Speaker 1: perspective when developing a system. Systems are meant to sort
Speaker 1: things into different categories. Typically it's like a bureaucracy. If
Speaker 1: you know how the bureaucracy works. You can very quickly
Speaker 1: move through a bureaucracy, but if you don't, your experience
Speaker 1: is going to be painfully laborious. And also, I mean,
Speaker 1: what else can we expect if if every system is
Speaker 1: inherently imperfect. We can't even as a species get our
Speaker 1: collection of people's face pictures correct. And we're pouring millions
Speaker 1: into this. Let me let me throw another issue here. Yes,
Speaker 1: so we had technology, it's it's all propried terran doesn't
Speaker 1: necessarily work well together. It's disproportionately discriminatory, leading to not
Speaker 1: only false positives, but in some cases wrongful of rest
Speaker 1: and imprisoned. And uh and Bennet's about to get a
Speaker 1: whole lot worse. Yes, you're right, it's time for a
Speaker 1: word from our sponsor. And we returned. It wasn't that bad.
Speaker 1: I I actually chuckled a little bit, but it was
Speaker 1: a tease. We weren't talking, weren't talking about the ad break.
Speaker 1: We were talking about yet another disturbing aspect of the
Speaker 1: emergence of facial recognition. Yeah, I wish, I wish I
Speaker 1: could say there's only maybe one or two layers left,
Speaker 1: But honestly, this, this is this is a mire of issues.
Speaker 1: So remember I said, we have thousands different agencies, all
Speaker 1: with various databases, all using various types of facial recognition software.
Speaker 1: The FBI can tap many of them when doing one
Speaker 1: of these searches. Um on top of this, you don't
Speaker 1: have you don't have a lot of regulations in place
Speaker 1: for most of these agencies on how they use this technology.
Speaker 1: So there are no rules, which means there's no accountability
Speaker 1: there either. It means that your face could be pulled
Speaker 1: into a virtual lineup. Essentially, it could be pulled up
Speaker 1: as a hit on one of these facial recognition searches.
Speaker 1: And because many of these places have no rules or regulations,
Speaker 1: you are not necessarily going to be alerted to this fact.
Speaker 1: You may not be aware that your face is even
Speaker 1: there in the first place. The first you may hear
Speaker 1: of it is if it goes far enough for for
Speaker 1: an agent to come and come knocking and ask like, hey,
Speaker 1: your face popped up when we ran this search. We
Speaker 1: need to talk to you about the thing that you
Speaker 1: may or may not have any connection to an alibi better. So,
Speaker 1: this is incredibly disturbing that because there are no rules
Speaker 1: and no accountability, you can't as a citizen take any
Speaker 1: steps because they didn't break rules. The rules didn't exist
Speaker 1: before we started recording. I said, it's kind of like
Speaker 1: if there were no laws, I could walk into your
Speaker 1: house and take whatever I wanted because there's no law
Speaker 1: against stealing as long as I have. If might makes right,
Speaker 1: then this is your time because there's only policy. Yeah,
Speaker 1: there's there's policy internally for these agencies, and even that
Speaker 1: doesn't match up from agency the agency. Yeah, there are
Speaker 1: eighteen states that have memorandums of agreement on this with
Speaker 1: the FBI specifically, and and there are more states that
Speaker 1: they're looking at adding to this all the time. But again,
Speaker 1: within each individual agency, they may have very different policies.
Speaker 1: Some of them have been trying to be responsible, right,
Speaker 1: some of them have of uh, done third party audits
Speaker 1: of their systems that sort of thing. That's great, that's
Speaker 1: at least a step in the right direction, But until
Speaker 1: we actually make rules, either at the state or federal level,
Speaker 1: we don't have any again, any accountability of something were
Speaker 1: to go wrong, Like, how do you complain if there's
Speaker 1: no rule against it? Right? Well, one of the policies
Speaker 1: that some of these agencies have is that before you
Speaker 1: can return results to the FBI or to the investigative agent,
Speaker 1: it has to first pass through humans who look at
Speaker 1: the pictures and determine whether or not they make a
Speaker 1: good match. So each of these agencies also have different
Speaker 1: agreements as to how many results they will return to
Speaker 1: whatever agent right like, uh, In the FBI, it's I
Speaker 1: believe between two and fifty, with the default being twenty results.
Speaker 1: But some places they'll be like no, we we will
Speaker 1: send you one, or will send you too, or we'll
Speaker 1: send you up to eighty results. And then human beings
Speaker 1: are supposed to call through these and decide which ones
Speaker 1: are actually the more uh likely matches to the one
Speaker 1: that the computer found. You're ready to have your mind
Speaker 1: blown here, Ben, Let's say, okay, all right, Let's say
Speaker 1: that your picture has come up in one of these investigations.
Speaker 1: Let's say that the agency that did it does have
Speaker 1: this policy that it must be reviewed by a human
Speaker 1: being before it goes any further. Fifty of the time
Speaker 1: they're going to make the wrong call the human being
Speaker 1: because this is hard. It's not just hard for computers,
Speaker 1: it's hard for people to take. If you're not looking
Speaker 1: at the exact same photos side by side. If you're
Speaker 1: looking at a person on a different day, from a
Speaker 1: different angle, from a different distance, under different lighting conditions,
Speaker 1: wearing different clothing, wearing a different hairstyle, wearing whatever, it's
Speaker 1: hard like unless you are really familiar with that person.
Speaker 1: I mean, I've seen pictures of people who look like
Speaker 1: me where I looked and I thought, I don't remember
Speaker 1: ever being there, and then as I looked, like, oh wait,
Speaker 1: that's not me. That has happened just to me, don't
Speaker 1: beat yourself up. I went home with the wrong girl, Like, yeah,
Speaker 1: I mean, I've definitely walked into the wrong house. But
Speaker 1: that's a totally different issue. That has nothing to do
Speaker 1: with my recognition. That just has to do with me
Speaker 1: not paying attention. That was back before you realize that
Speaker 1: Steely was against the law. Yeah, you know, people need
Speaker 1: to make these things a little more clear to me.
Speaker 1: Challenging stuff. So it turns out that you people really
Speaker 1: need to go through a very thorough, challenging, difficult training
Speaker 1: process to become experts in matching photos. If you have
Speaker 1: not done that, and a lot of agencies don't require
Speaker 1: that level of training. There's a fifty error rate so
Speaker 1: not only could the machines misidentify somebody, the human beings
Speaker 1: who are meant to be the the checks and balances
Speaker 1: against such things could make the same mistake. So too
Speaker 1: bad coin flips. You're in a lineup, you might be
Speaker 1: matched as someone of interest. Now you might say, let's say,
Speaker 1: let's say that you're the kind of person who says,
Speaker 1: I didn't do anything wrong to worry. What do I
Speaker 1: have to worry about? Well, I mean, you're talking about
Speaker 1: a violation of a basic amendment here, potentially the protection
Speaker 1: against unreasonable search and seizure. You're being searched with no justification.
Speaker 1: There's no reason for them to search you, but they're
Speaker 1: going to be looking into you and your background. Let's
Speaker 1: say that you did do something that was of minor
Speaker 1: significance in the past that maybe you know you're sorry
Speaker 1: for you did something dumb. You you are sorry for it,
Speaker 1: you have reformed, you're not you know, you luckily weren't
Speaker 1: taken to task for it. I guarantee you if there's
Speaker 1: a federal investigation, that's gonna be one of the things
Speaker 1: that pops up. I mean, it's it's things like your
Speaker 1: Your life could be ruined or at least significantly impacted
Speaker 1: in a negative way for something of minor importance that
Speaker 1: may have happened in the past that has nothing at
Speaker 1: all to do with whatever the investigation is. And also,
Speaker 1: let's let's go a step further here. Sure, so the
Speaker 1: practice of expunging a criminal conviction, right, that happens a
Speaker 1: lot with especially with people who commit a crime when
Speaker 1: they're teenagers. Right. Sure that whether whether or not that's
Speaker 1: expunged doesn't really matter in today's investigatory climate. It's kind
Speaker 1: of like that game where people pretend the floor is
Speaker 1: lava and no one steps on the floor. I'm sorry
Speaker 1: you have to learn about at this the editorial board,
Speaker 1: the floor is lava over at the editorial department. We Um,
Speaker 1: we started that joke. Um, that's why we have the
Speaker 1: carpet tiles. Ben, about two weeks before you got here.
Speaker 1: Shut up, just kept going. You don't have they're not
Speaker 1: special shoes. Man. On the flip side, My hamstrings are amazing.
Speaker 1: That's true. That's true. Alright, So, um, we're adding some levity.
Speaker 1: But we're adding levity. Yeah, you have to because and
Speaker 1: I am not you know me, Ben, I am not
Speaker 1: a They're all out to get you kind of guy. No, no, you.
Speaker 1: You endeavor to be as objective as possible when you're
Speaker 1: discussing anything tech related. I just feel very strongly that
Speaker 1: this particular practice is dangerous and irresponsible on multiple levels.
Speaker 1: And I'm not the only one. Congress called the FBI
Speaker 1: I in front of the principal's office back in March
Speaker 1: seventeen and essentially was saying the same thing they were saying,
Speaker 1: there's no accountability here. Uh. This is horrifying. It is
Speaker 1: a gross uh over overstepping of your authority. It Uh,
Speaker 1: it is violating people's privacy. People should have an expectation
Speaker 1: to not be called into these virtual lineups, even if
Speaker 1: you never know about it. The fact that it's happening
Speaker 1: is disturbing, right. I wonder if it qualifies on your
Speaker 1: unreasonable search that and that's one of the arguments. That's
Speaker 1: Georgetown University has has a great report on this that
Speaker 1: is very easy to read. Uh. There's also the Government
Speaker 1: Accountability Office that has an amazing report specifically about the FBI.
Speaker 1: Georgetown looks at the broader picture. Government Accountability Office looks
Speaker 1: specifically at the FBI's use because it's a federal agency
Speaker 1: in the Government Accountability Offices is also a federal agency.
Speaker 1: The Georgetown one looks at the broader use of facial
Speaker 1: recognition and law enforcement in general, and they there are
Speaker 1: some very strong points saying this seems like it violates
Speaker 1: our protection against unreasonable search and seizure, and that you
Speaker 1: know that that's a huge deal. That's that that's a
Speaker 1: protection granted to us by the Constitution of the United States.
Speaker 1: So there's definitely a movement in government to push these
Speaker 1: agencies to develop regulations. And I suspect that if the
Speaker 1: agencies failed to do so in a timely manner, we
Speaker 1: will see actual legislative movement in various state and federal
Speaker 1: governing bodies. Two codify it at that level, if not
Speaker 1: at the actual agency level. Uh. And I would prefer
Speaker 1: it to be codified at the state and federal level
Speaker 1: because I always worry about any group that's allowed to
Speaker 1: make up its own rules. Yeah. And and I find
Speaker 1: it optimistic to to say that someone would limit this
Speaker 1: ability because we know, for instance, that historically when aggregation
Speaker 1: of powerful data bases occurs, it almost never just due
Speaker 1: to the different paces of technological evolution and legislative evolution,
Speaker 1: the It almost never is reigned back efficiently. I mean
Speaker 1: like the like the right now. If, for instance, I
Speaker 1: was that I decided to pull it Edwards Snowden or
Speaker 1: Julian Assang or like what's a good bond villain? Doctor okay,
Speaker 1: doctor no, I was like, I'm gonna do a doctor
Speaker 1: no thing or rs Goldfinger, Yes, yes, I'm gonna do
Speaker 1: a doctor no goldfinger thing. And I'm like, Matt Jonathan
Speaker 1: and I are going to do some some terrible attack
Speaker 1: on the US from our secret underwater base. Of course,
Speaker 1: of course that's where we would do that. The volcano
Speaker 1: base is nice. But if you're doing like a world
Speaker 1: level attack where you're you're legitimately causing vast destruction, you
Speaker 1: want to be in that underwater base. You genuinely have
Speaker 1: to be afraid of floor lava in the volcano base,
Speaker 1: just you really do. Those carpet tiles are a lifesaver.
Speaker 1: Your ham strings do look great. So what What What
Speaker 1: happened though, is uh, I would quickly learn that whatever
Speaker 1: legislation exists will not in practice stop uh stop the
Speaker 1: n essay from knowing everything I've done online and from
Speaker 1: extrapolating in the interest of national security, any other information.
Speaker 1: The FISA courts are for the birds. Yeah let me
Speaker 1: let me let me throw one more. I would make
Speaker 1: it worse. Okay, you wanted to be worse. So you
Speaker 1: know I mentioned earlier that that idea that if I
Speaker 1: haven't done anything wrong, I'm all right. Here in the
Speaker 1: United States, one of the things that we are allowed
Speaker 1: to do is protected by the Constitution, is to assemble
Speaker 1: peacefully in public and express free speech. So there has
Speaker 1: been a fear, justifiably in my mind, that an agency
Speaker 1: that perhaps has a specific political inclination could use such
Speaker 1: facial recognition software to identify participants in a peaceful protest
Speaker 1: for later let us say discussions. Right, you don't think
Speaker 1: that's already been done. What I'm here to say is
Speaker 1: that Georgetown, when they did their report, they asked various
Speaker 1: agencies about their policies as to the use of facial
Speaker 1: recognition software. They found out of the fifty two agencies
Speaker 1: that they were able to contact and get a response from,
Speaker 1: only one has a specific rule against using facial recognition
Speaker 1: to identify people participating in public demonstrations or free speech
Speaker 1: in general. Only one out of fifty two that they
Speaker 1: asked specifically has a rule against that. So, in other words,
Speaker 1: fifty one out of fifty two have no rule. It
Speaker 1: means that if the police were to use it, or
Speaker 1: whatever other law enforcement agency were to use facial recognition,
Speaker 1: perhaps even in a live feed, because you can do
Speaker 1: it in video just as well as you could do
Speaker 1: it in photographs, to start identifying to populate a crowd
Speaker 1: with names and identities who are protesting a particular thing.
Speaker 1: Let's say it's police brutality, which would be very relevant,
Speaker 1: especially in recent years. That is a legitimate concern that
Speaker 1: that people's free speech could be, uh could be could
Speaker 1: be violated. If you feel like you can't go out
Speaker 1: there and express your thoughts because you might be unfairly
Speaker 1: targeted as a result of that, that is a violation
Speaker 1: of your free speech, not to mention, you know, your
Speaker 1: personal safety, depending upon how legit those fears are. So
Speaker 1: that is incredibly troubling. I don't know if you noticed this,
Speaker 1: Ben Matt. Maybe you guys don't notice it. I've noticed
Speaker 1: that there have been a couple of protests recently, in
Speaker 1: the last twelve months or so. I've seen a few,
Speaker 1: like some of some of which have had a significant
Speaker 1: number of participants, both on both on different sides of
Speaker 1: political issues, right, not just I'm not saying that this
Speaker 1: is one group targeting one other group. I'm saying there
Speaker 1: are a lot of different parties involved here, and no
Speaker 1: matter how this technology is used against whichever group it
Speaker 1: is used, the fact that there are no rules about
Speaker 1: how to use it is deeply troubling, and I would
Speaker 1: argue fundament to Lee un American, I would agree, what
Speaker 1: do you think, Matt, Yeah, definitely. I've seen the photographs
Speaker 1: and videos of police filming using video cameras to film
Speaker 1: protests and protesters, and that makes me very nervous. And honestly,
Speaker 1: if you're able to get at those images, then you
Speaker 1: can run that search, even if you weren't the one
Speaker 1: filming it. Right. Like, one of the elements we didn't
Speaker 1: really touch on in this in this episode, but that
Speaker 1: is related, is the fact that we have lots of
Speaker 1: private companies out there that incorporate facial recognition technology as
Speaker 1: a feature for you, for consumers. Right. So Facebook is
Speaker 1: a great example. You take a picture of someone on
Speaker 1: Facebook that you're friends with. Facebook will often say, hey,
Speaker 1: do you want to tag this photo? And they'll even
Speaker 1: give you the name of the person because their facial
Speaker 1: recognition software is good enough to let you know, like, oh, well,
Speaker 1: we're pretty sure this is your buddy, you know, Bob,
Speaker 1: So you want us to go ahead and tag Bob
Speaker 1: for you, Well, you know, on its own, as a feature,
Speaker 1: there's nothing necessarily wrong with that. But you could imagine
Speaker 1: that same sort of of deep, deep wealth of data
Speaker 1: being put to nefarious use under the right circumstances, maybe
Speaker 1: not by Facebook itself, but perhaps by an entity that
Speaker 1: forces Facebook to hand that information over exactly. And I'm
Speaker 1: glad you said that, because more more frightening than law
Speaker 1: enforcement unfairly targeting people is the idea of the increasingly
Speaker 1: gray area and the increasingly black box interactions between large
Speaker 1: data aggregators like Facebook, Google, your local I s P
Speaker 1: and or Internet service provider, and the rest of the
Speaker 1: alphabet soup of the federal government, you know. And it
Speaker 1: could be anything from the FBI to the n s A,
Speaker 1: to local police to Hartman writes in and to Facebook
Speaker 1: and says, hey, we need to We think there are
Speaker 1: these three guys who went to this underwater base off
Speaker 1: the coast of redacted, and they've been posting photos in
Speaker 1: their timeline, and we just want to make sure that
Speaker 1: these three guys are the same three guys who did
Speaker 1: that amazing diamond heist a few years ago for their
Speaker 1: for their freeze ray. Yes, yes, yeah, Hey do you
Speaker 1: guys remember b O b Y Yeah yeah yeah yeah
Speaker 1: bob uh he he came at us a little while
Speaker 1: ago us being the world through social social media and
Speaker 1: talked about the Earth being flat, and we talked about that. Well,
Speaker 1: he thinks that Snapchat is really just collecting blackmail photos
Speaker 1: of our silly faces doing things. I will tell you this. Uh,
Speaker 1: if you think that the images and video that you
Speaker 1: take on Snapchat that disappear after hours has truly disappeared,
Speaker 1: you're absolutely wrong. Yeah. No, that exists on a server somewhere.
Speaker 1: It has to. And then furthermore, and I love that
Speaker 1: we're exploring this part too, because okay, Facebook, people pile
Speaker 1: onto Facebook often because it is, you know, diabolical. It's
Speaker 1: it's an enormous corporation that is completely dependent upon users
Speaker 1: surrendering information about themselves. So due to its very nature
Speaker 1: as to what it is and how it makes money,
Speaker 1: it makes money through the fact that people use it
Speaker 1: and reveal their likes, their dislikes there you know the
Speaker 1: things that they want. Thus you can serve ads against that.
Speaker 1: It's very easy for that business model to take a
Speaker 1: very dark turn without without a lot of discipline and
Speaker 1: policies and and the will to actually uh perform business
Speaker 1: in an ethical manner. And to be completely fair, I
Speaker 1: don't think Facebook is run by a bunch of vampire demons,
Speaker 1: but I do think that it can be very easy
Speaker 1: to make unethical decisions even without being aware of it,
Speaker 1: when you have that massive amount of data. Absolutely, and
Speaker 1: it also extends to as you said, snapchat. And I
Speaker 1: had an issue when um one of one of our
Speaker 1: co host here knowl was going through a Pokemon Go
Speaker 1: phase and it don't put put Pokemon Go camera on
Speaker 1: me or hanging out, so there's always you know, a
Speaker 1: different Pokemon that's on you. And of course being a
Speaker 1: little bit on the paranoid side, I'm kidding, I'm way
Speaker 1: deep into that. Well, Ben, Ben, I mean you're I
Speaker 1: hate to tell you this, but you are a pig post.
Speaker 1: You just got piggies all over you. What do you
Speaker 1: think they mean when they say catch them all talking
Speaker 1: about all the faces? Yeah, one of the first things,
Speaker 1: I turned off on Pokemon Go, although it was not
Speaker 1: for fear. Well, there's some concern for privacy because you know,
Speaker 1: you're using it in public and there are people all
Speaker 1: around you. And while you can take photos of people
Speaker 1: in public, there's nothing illegal against that. I just think
Speaker 1: it's you know, rude. So I turned off the camera
Speaker 1: slash augmented reality part, so it just has the animated
Speaker 1: background as opposed to an actual camera view. Um and uh.
Speaker 1: But again I did that not really out of concern
Speaker 1: of other people, but rather because it doesn't drain. My
Speaker 1: battery is fast, so it's a selfish reason. I wish
Speaker 1: I could say I was being a really decent human being,
Speaker 1: but really I just didn't want my phone to die.
Speaker 1: But is it really turning your camera off? Well, I
Speaker 1: mean it's not showing it on my screen, whether it's
Speaker 1: showing it on someone screen. Oh gosh, I guess. I guess.
Speaker 1: What I am doing is consistently giving Nintendo and the
Speaker 1: Pokemon Corporation up to date information about who is on
Speaker 1: the belt line. That's good, that's what they need, and
Speaker 1: it's a shame that they don't pay you. So there
Speaker 1: there are a couple of things here that we should
Speaker 1: we should explore. So sure. Another another fact that we
Speaker 1: must add is, yes, your face is out there. We
Speaker 1: were increasingly, you know, I think it was Andy Warhol
Speaker 1: who said, now everybody will have fifteen minutes of fame.
Speaker 1: We're increasingly arriving at a world where everyone in some
Speaker 1: way is going to be recognizable in one of these databases.
Speaker 1: But living in an anonymous life is almost impossible if
Speaker 1: you are living in modern society, right, And to me
Speaker 1: a little twisting the knife with this, this is being
Speaker 1: sold at a profit and none of the no one
Speaker 1: is being compensated. So, for instance, every time Matt's UH
Speaker 1: image shows up, it's a false positive. Every time that
Speaker 1: that one resource that is entirely created by him shows
Speaker 1: up in you know, a p D, FBI, etcetera, he
Speaker 1: doesn't get any royalty from that, and I don't I
Speaker 1: for the record, I completely think that will never actually happen. Now,
Speaker 1: if we ever see a time where the FBI opens
Speaker 1: up a fun and zany FBI T shirts store where
Speaker 1: you can get your photo stored that was stored on
Speaker 1: the law enforcement database onto a T shirt and maybe
Speaker 1: as a mug shot or something that's a great idea.
Speaker 1: Oh on a coffee mic mug shot. Coffee mug shot.
Speaker 1: So let's uh, well, let's as as they like to say,
Speaker 1: let's in big in this all right. I think that's
Speaker 1: a cromulent decision. Thank you. I agree. So we're we're
Speaker 1: talking right now about the United States, which it does
Speaker 1: have an extensive network, as you said, an extensive array
Speaker 1: of networks. But my question is when it runs into
Speaker 1: like multinational private industry, does it also run into international
Speaker 1: law enforce I mean, really, if you don't think agencies
Speaker 1: like the Central Intelligence Agency have their own versions of
Speaker 1: the database that incorporate people from all over the world,
Speaker 1: you're absolutely wrong. I mean it has to write like
Speaker 1: and at some point in some aspects you understand, right,
Speaker 1: you're talking about trying to protect national interests from very flexible,
Speaker 1: very mobile, very agile enemies essentially, or or agents if
Speaker 1: you want to call them that, whether they're state agents
Speaker 1: or otherwise. So you understand, you understand the necessity. The
Speaker 1: problem is, again, unless you have a clear set of rules,
Speaker 1: you know, you have some transparency there on how it operates,
Speaker 1: and you have the ability to actually put your technology
Speaker 1: to the test to show that it is in fact accurate. Ultimately,
Speaker 1: you're left with a question of do we did we
Speaker 1: get the right person? This leads to deaths if we
Speaker 1: If we get the wrong person, two things happen. The
Speaker 1: bad guy got away, right, because you didn't get the
Speaker 1: person you wanted to get, and you've given the bad
Speaker 1: guys more reason to hate you. Because you are targeting
Speaker 1: people who are innocent. You are creating more bad guys. Right.
Speaker 1: If you go out there and you are targeting people
Speaker 1: who are are innocent of the crimes that you think
Speaker 1: they committed because you're following a wrong lead due to
Speaker 1: a technological glitch, then you are justifying that anti sentiment
Speaker 1: because you are doing what you have been accused of doing,
Speaker 1: which is coming into another culture and disrupting it. So
Speaker 1: this is true, whether it's domestically and we're talking about
Speaker 1: people who are disproportionately targeted by law enforcement because of
Speaker 1: this sort of same sort of issue, or on a
Speaker 1: global scale. Moreover, there are other nations that have even
Speaker 1: more pervasive camera technologies built around them than the United
Speaker 1: States does. Hello England, I love you, but I can't
Speaker 1: go anywhere without being on a camera in England, Right,
Speaker 1: don't they have the highest density of CCTV. They certainly did,
Speaker 1: at least for a long time. I don't know if
Speaker 1: they currently hold that record, but they for for a
Speaker 1: long time, like especially in London, because they had so
Speaker 1: many different CCTV systems that were most of them were
Speaker 1: self contained, right, so when a shop owner has a
Speaker 1: security camera, yeah, essentially that's what it was. So it
Speaker 1: wasn't like it was just this vast network apart from
Speaker 1: what you know you would see like in in um
Speaker 1: Sherlock where microft is able to zoom in on Watson
Speaker 1: wherever he goes because he can tap into every camera
Speaker 1: that's on every street corner. Well, that's one thing because
Speaker 1: those were those were streets, so those would presumably be
Speaker 1: operated by by the government itself or at least some
Speaker 1: branch of the government. But you wouldn't be able to
Speaker 1: necessarily tap into like every every stores feed because it's
Speaker 1: a self contained system. It's not connected to a network
Speaker 1: for now, which brings us to, of course the future. Yes, okay,
Speaker 1: very last part uh speculation on speculation, educated guesses where
Speaker 1: is this going? So? On the one hand, I am
Speaker 1: somewhat encouraged in the United States that a lot of
Speaker 1: focus has been brought to this matter along with the
Speaker 1: Government Accountability Office and Georgetown University. You have Congress actually
Speaker 1: taking the FBI to task about this. Obviously, there would
Speaker 1: need to be a lot more of those sort of
Speaker 1: discussions at all levels of government for that to actually
Speaker 1: lead to regulations and transparency. I think it's a good start.
Speaker 1: I do not know if it's going to have enough
Speaker 1: momentum to carry it forward to prevent massive abuse of
Speaker 1: this kind of technology on multiple levels, because I mean
Speaker 1: that's happening right now. I don't know if it'll curtail
Speaker 1: it um. I hope it will. That's one possible future
Speaker 1: is that we'll see over time the development of these
Speaker 1: rules and policies that various agencies have to follow so
Speaker 1: that the use of this technology is done in an
Speaker 1: accountable and responsible way, and that uh, you know. The
Speaker 1: FBI they state that when they're running these searches, any
Speaker 1: search result they get back, that does not mean the
Speaker 1: person that they get has become a suspect. It means
Speaker 1: that they have a match that a potential lead to follow.
Speaker 1: They're very careful in their wording to say that they
Speaker 1: haven't identified to suspect that's why they use probe photo
Speaker 1: rather than suspect photo. However, words only go so far.
Speaker 1: How where do the actions go? And that's the big
Speaker 1: fear is that we don't really know yet. So optimist
Speaker 1: Jonathan would say, over time, we developed these rules, we
Speaker 1: hold these agencies accountable for them, We make certain that
Speaker 1: no one is relying too heavily on a technology that
Speaker 1: is not perfect, and it just becomes another another tool
Speaker 1: of agencies to use for specific situations in order to
Speaker 1: investigate leads. And that is as far as it goes.
Speaker 1: Cynical Jonathan, I'm more familiar with this one. Cynical Jonathan
Speaker 1: thinks this is going to continue largely unchecked at various
Speaker 1: levels of law enforcement agencies, including the federal level, largely
Speaker 1: used in in agencies that have no transparency because of
Speaker 1: the nature of what they do, things like the n
Speaker 1: s A, and that there will be a growing number
Speaker 1: of people very much interested in using it for purposes
Speaker 1: that it was not intended, in other words, not to
Speaker 1: investigate crimes alone. I mean they would still want to
Speaker 1: use it for that obviously, but also to perhaps push
Speaker 1: specific political agendas by suppressing opposing political agendas. And this
Speaker 1: sounds very Orwellian and very like I'm not again, I'm
Speaker 1: not a Norwellian type of dystopia person, but it makes
Speaker 1: it so easy to do that even if you weren't
Speaker 1: consciously setting out on that path, you could do it
Speaker 1: just by happenstance of your other methods of going about
Speaker 1: your business. And because we have no rules or regulations
Speaker 1: for it, there's no protection against it. So I find
Speaker 1: it very concerning. I think it needs to be addressed
Speaker 1: and and it needs to be consistently looked at as
Speaker 1: something that we have to fix, because if we don't,
Speaker 1: the abuse of it is going to be rampant. It's
Speaker 1: going to be disruptive. And I mean, if you are
Speaker 1: not a member of whichever privileged group is not consistently
Speaker 1: targeted by this, you might not see a problem with it.
Speaker 1: But for everybody else, it's it's a huge it's un fair,
Speaker 1: it's a threat, it's not cool. Uh So, yeah, I'm
Speaker 1: I'm afraid we're going to a real dark place. And
Speaker 1: it's literally stuff they don't want you to know. When
Speaker 1: we talk about where this data goes, where it's aggregated,
Speaker 1: who's talking to whom? Right? This is? This is not
Speaker 1: only I don't know. I think you make an important
Speaker 1: point when you say it's not illegal, because there's no
Speaker 1: measure of legality yet. But legal and ethical are not
Speaker 1: always the same thing. No, I mean, there are plenty
Speaker 1: of things that I think you could argue are unethical
Speaker 1: that are perfectly legal, and vice versa. There are some
Speaker 1: things that you might argue are illegal but are perfectly ethical, right,
Speaker 1: Like it's legal to uh seeing that Journey song and
Speaker 1: get karaoke, But is it ethical? I would think not that,
Speaker 1: I would think at this point, don't stop, believe even
Speaker 1: has been overdone. Go with a different Journey song. So
Speaker 1: at this point we've talked about the past, the present,
Speaker 1: and the possible future of facial recognition. And Jonathan, I
Speaker 1: want to thank you so much for coming on the
Speaker 1: show and illuminating us choosing that word very carefully, uh
Speaker 1: with with the facts behind something that people hear about.
Speaker 1: And this is a morphous boogeyman sort of way. But
Speaker 1: it turns out that even when you shed this light
Speaker 1: on it for us, it's still a boogeyman. Oh yeah, now,
Speaker 1: this is this is something that is happening. It's happening
Speaker 1: without any real oversight it. It is certainly incorporating uh
Speaker 1: data from people who have no criminal background whatsoever, and
Speaker 1: it is it's very disturbing, Like it's It's one thing
Speaker 1: if everyone were to go with the rules that the
Speaker 1: FBI originally set, which is that we're only using the
Speaker 1: data that we've collected from criminal cases. Even then you
Speaker 1: could argue, well, we don't necessarily know that everybody who's
Speaker 1: in that criminal database actually did something criminal. There might
Speaker 1: be some innocent people in there, but there probably are
Speaker 1: some innocent people in there, and you're also missing out
Speaker 1: on everybody who has yet to commit a crime. But
Speaker 1: that that part would be less concerning from the average citizen, right, Matt.
Speaker 1: I mean, if you're FBI, you hate it because it
Speaker 1: makes it harder for you to find a lead. But
Speaker 1: if you're an average citizen, you're relieved because you know
Speaker 1: you're not gonna get targeted. But the problem is that no,
Speaker 1: those rules, that's not what everyone's following. People are in fact,
Speaker 1: we don't know what everyone is doing. And it's terrifying
Speaker 1: because you are there's no way of knowing how frequently
Speaker 1: your face has already been in virtual lineups. It probably
Speaker 1: has been at some point. It may have been that
Speaker 1: it reached a very early stage before it was dismissed.
Speaker 1: It never went further than that. But right now, as
Speaker 1: we're talking, as your listeners are listening to this, there
Speaker 1: are cold algorithms looking for that numeric match and your
Speaker 1: face might be it. And that reminds me, Uh, do
Speaker 1: you guys want to take a picture for Instagram after
Speaker 1: we after we closed the show doesn't have to be
Speaker 1: of us? All right, we'll break out I patches or something.
Speaker 1: Maybe this is all true. This is not a conspiracy theory.
Speaker 1: This is there is an active collection of groups that
Speaker 1: are cooperating using your data in what could be and
Speaker 1: what have been very dangerous ways. And Jonathan, I think
Speaker 1: you've ended it for us on the perfect note because
Speaker 1: this is only weirdly enough the beginning. So if people
Speaker 1: want to learn more about not just this, but all
Speaker 1: things tech related, could you could you tell us where
Speaker 1: to check you out? Absolutely? I host the podcast tech
Speaker 1: Stuff uh we uh we published twice a week Wednesdays
Speaker 1: and Fridays. We cover all sorts of technology topics, from
Speaker 1: things that are scary, like I did a recent episode
Speaker 1: about near misses with nuclear war. The Times when we
Speaker 1: were the world was on the brink of nuclear devastation
Speaker 1: and cooler heads prevailed because clearly that did not happen.
Speaker 1: But I also do fun, silly stuff. I did one
Speaker 1: that was all about the top memes of the last
Speaker 1: few years, so talking about where those came from and
Speaker 1: and uh and and how they evolved over time. So
Speaker 1: it's the whole gamut of technology stories. So if you
Speaker 1: want to check that out, go to your favorite podcatching
Speaker 1: service such as iTunes podcasts and listen to tech Stuff.
Speaker 1: If you like it, subscribe if you really like it.
Speaker 1: You can watch me record it live because I'm doing
Speaker 1: something crazy on Wednesdays and Fridays over at twitch dot
Speaker 1: tv slash tech Stuff, I live stream the recording sessions
Speaker 1: of my show, so you can go and watch me
Speaker 1: record live. And here's the cool thing or lame thing,
Speaker 1: depending upon your point of view. You can witness all
Speaker 1: the times I mess up or have to take a break,
Speaker 1: or have to cough, or have to drink some water
Speaker 1: or whatever it may be. Because it's live, it's all
Speaker 1: the stuff that we cut out before we publish the show.
Speaker 1: You can witness it and you can say I heard
Speaker 1: that show a month before it published, because that's how
Speaker 1: far out I am. That's fantastic, And I think Matt
Speaker 1: sometimes helps you produce that is that he does. Matt
Speaker 1: has I have pulled Matt in on multiple occasions to
Speaker 1: help set up the both the recording and the Twitch
Speaker 1: streaming side of that. Matt has appeared on camera briefly
Speaker 1: as part of that. I don't make him stay for
Speaker 1: the whole thing because he's cut. It turns out he's
Speaker 1: got stuff to do sometimes. Yeah, so, uh, do check
Speaker 1: out Jonathan and that if you are interested in the
Speaker 1: future of technology and want to feel a little bit
Speaker 1: better about the state of humanity than check out Jonathan's
Speaker 1: other show, Forward Thinking, which is available on YouTube, and
Speaker 1: the Facebook's Yeah Yeah if you wanna. If you wanna
Speaker 1: see my face getting identified all over the place, check
Speaker 1: out Forward Thinking. That one's that was kind of like
Speaker 1: the the Shiny Happy Cousin two Stuff they don't want
Speaker 1: you to Know's dark, dark Emo Cousin. I think we
Speaker 1: prefer to think of it as conspiracy realism. I look,
Speaker 1: I'm not making any kind of judgments here. I'm merely
Speaker 1: making an observation. I love all the children of how
Speaker 1: stuff works. Equally all right, Well, now we're definitely putting
Speaker 1: a picture of you on Instagram, and you may be
Speaker 1: asking yourselves, ladies and gentlemen, wait, the guys have an Instagram.
Speaker 1: Oh my gosh o MG, where can I find it? Well,
Speaker 1: the answer is Conspiracy Stuff Show. You can also find
Speaker 1: us on Facebook and Twitter, where we are Conspiracy Stuff.
Speaker 1: And that's the end of this classic episode. If you
Speaker 1: have any thoughts or questions about this episode, you can
Speaker 1: get into contact with us in a number of different ways.
Speaker 1: One of the best is to give us a call.
Speaker 1: Our number is one eight three three std w y
Speaker 1: t K. If you don't want to do that, you
Speaker 1: can send us a good old fashioned email. We are
Speaker 1: conspiracy at i heart radio dot com. Stuff they Don't
Speaker 1: Want you to Know is a production of I Heart Radio.
Speaker 1: For more podcasts from my heart Radio, visit the i
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