Speaker 1: My REGI welcome Stuff to Blow Your Mind, a production
Speaker 1: of I Heart Radios How Stuff Works. Hey, welcome to
Speaker 1: Stuff to Blow Your Mind. My name is Robert Lamb
Speaker 1: and I'm Joe McCormick, and we're back with part three
Speaker 1: of our Journey through Facial Recognition. In the first episode,
Speaker 1: we talked a lot about the sort of the current
Speaker 1: tech landscape of a company focusing on facial recognition, some
Speaker 1: issues with that. In the last episode we focused primarily
Speaker 1: on the biological domain of facial recognition, and now we're
Speaker 1: bringing it back to technology to uh to finish up today.
Speaker 1: So one of the things that we were looking at
Speaker 1: coming into today's episode was an article that I thought
Speaker 1: was really good in Wired magazine again from this month
Speaker 1: from January of twenty by Seaun Revieve called the Secret
Speaker 1: History of Facial Recognition, And I will say I was
Speaker 1: surprised to find out how far back facial recognition projects go.
Speaker 1: This this goes into the nineteen sixties. Yeah, yeah, this,
Speaker 1: this this is a really good read. This article. I mean,
Speaker 1: it's it's extremely well written. It almost has I would
Speaker 1: say it has a very narrative flow to a beginning
Speaker 1: with this scene in which Uh, an elderly researcher who
Speaker 1: is you know at this point, I believe you can
Speaker 1: find to a wheelchair is instructing his son to to
Speaker 1: unlock some old, rotting files from the sixties and burn
Speaker 1: them in front of him and the garbage can in
Speaker 1: the garage or something. Yeah, with you know, with and
Speaker 1: you can you can tell that there are things about
Speaker 1: classified information, top secret or what have you on the documents.
Speaker 1: So it's a wonder wonderful, um you know, ominous start
Speaker 1: to this article, which of course deals predominantly with the
Speaker 1: the origins of facial recognition and touches on other at
Speaker 1: times inspiring and other times creepy and devastating scientific programs
Speaker 1: that were going on or in like in full swing
Speaker 1: during the sixties. Well. Yeah, one of the things that
Speaker 1: really comes home in this article is that the creepiness
Speaker 1: of facial recognition technology is not new. That's sort of
Speaker 1: been there since the very beginning. It's not one of
Speaker 1: these things where um, like even the author here mentions like,
Speaker 1: you know, social media, where it seems great at first,
Speaker 1: and it's not until it's quote in the wild that
Speaker 1: we begin to realize, oh yeah, this is um civilization
Speaker 1: wrecking awfulness. Uh. And not just a fun way to
Speaker 1: share photos. No. At the time, there was a realization
Speaker 1: that this was potentially problematic. Yeah. And so it might
Speaker 1: not come as a big surprise, especially given the story
Speaker 1: we mentioned about burning documents that some of the earliest
Speaker 1: funding for facial recognition technology research clearly came from the
Speaker 1: c i A and front companies set up to funnel
Speaker 1: c i A money. Yeah, this was this was super interesting,
Speaker 1: the CIA funding through these various phony companies, and and
Speaker 1: due to the CI funding, some of the you know,
Speaker 1: stuff was was secret. Some of the image material has
Speaker 1: only come out, you know, due to Freedom of Information
Speaker 1: Act filings, and and some of the work was just
Speaker 1: never published. A lot of the work was never published. Uh.
Speaker 1: To to drive home the creepiness though, one of the
Speaker 1: companies involved was this company, Panoramic, which which was also
Speaker 1: tied to other programs including UH the Project mk Ultra. Uh.
Speaker 1: It was one of eighty organizations that worked on Project
Speaker 1: mk Ultra in particular quote subprojects and ninety four on
Speaker 1: the study of bacterial and fungal toxins and the remote
Speaker 1: directional control of activities of selected species of animals Animal control. Yeah,
Speaker 1: I was like, if I can, if I want to
Speaker 1: sick tigers at you from the other side of the world. Yeah.
Speaker 1: MK Ultra, just to remind everybody, was a c I
Speaker 1: project that explored the potential for mind control using psychedelics
Speaker 1: UH and other tactics like basically looking at ways to
Speaker 1: take these mind expanding um UH agents and use them
Speaker 1: to break down the human psyche and then inevitably built
Speaker 1: something back up that it could be tightly control. Now,
Speaker 1: the evidence today is that the mind control experiments of
Speaker 1: m K culture didn't really work, but they were really
Speaker 1: good at destroying the human mind. I mean because basically
Speaker 1: the project was responsible for psychological torture. UH. Just a
Speaker 1: horrible program in a real blight on the scientific history
Speaker 1: of the United States. UM. You know, not the only blight,
Speaker 1: but but I think an appalling one. They were not good.
Speaker 1: They did not figure out a way how to read
Speaker 1: and you know, to rebuild say an ideal sleeper agent
Speaker 1: out of the psychological destruction that they wrought. Yeah. So
Speaker 1: a lot of this article focuses on this one main
Speaker 1: figure named Woody Bledso, who was a leader at this company.
Speaker 1: A founder and leader at this company, Panoramic Research, Incorporated,
Speaker 1: which you mentioned earlier, which got a lot of business
Speaker 1: from c I A and c I A front organization
Speaker 1: funding in the sixties to study things like facial recognition.
Speaker 1: But if you put yourself back in the context to
Speaker 1: the nineteen sixties, I think one thing that's kind of
Speaker 1: funny is people then might not yet have realized how
Speaker 1: difficult of a project recognizing a face would be for
Speaker 1: a machine, because we'd have tons of sci fi goes
Speaker 1: back decades before that, where of course robots, computers, whatever,
Speaker 1: just recognize people easily. Yeah, And I think that's mainly
Speaker 1: because we would just we generally would just have a
Speaker 1: rough idea that a robot can do everything a person
Speaker 1: can do. A robot is a mechanical person, and you
Speaker 1: didn't have to think too hard about all the complexities
Speaker 1: involved there. I mean, even like the best example of
Speaker 1: nineteen sixties science fiction and really one of the twenty
Speaker 1: and twenty one centuries best examples of science fiction two
Speaker 1: thou one of Space Odyssey, it does reference facial recognition
Speaker 1: capabilities for how but it doesn't I wouldn't say that
Speaker 1: it really it goes in depth about what that means.
Speaker 1: But but how does is able to recognize faces, and
Speaker 1: even like can recognize faces when it is a sketch
Speaker 1: as opposed to video feed or a photo. Yeah, and
Speaker 1: I think if you're not well versed in the computer
Speaker 1: technology world, it might not be immediately apparent what's so
Speaker 1: difficult about making a computer recognize a face? But you know,
Speaker 1: our facial recognition systems, the things going on in our
Speaker 1: brain have amazing capabilities and their analog right. You know,
Speaker 1: a face, face has all kinds of variables that move
Speaker 1: around all the time. It can be extremely difficult to
Speaker 1: reduce a face to a set of nu miracle values,
Speaker 1: which are what you need to do in order to
Speaker 1: have a computer recognize a face. Yeah, as as I
Speaker 1: think we've explored in the previous two episodes, it's um yeah,
Speaker 1: there's there's a lot going on in facial recognition. There
Speaker 1: are a number of challenges to it, and it is
Speaker 1: still not a perfected technology by any means. Yeah, that's true,
Speaker 1: And so I think it's reasonable to think of what
Speaker 1: he bled So and his colleague as legitimate AI pioneers,
Speaker 1: even with their work in the nineteen sixties here. Oh absolutely,
Speaker 1: but I'm Bledso was working with a number of you know,
Speaker 1: highly talented individuals, sometimes on projects like involving atomic weaponry,
Speaker 1: for instance, before he became more focused on AI. Another
Speaker 1: individual that he worked on concerning facial recognition was a
Speaker 1: Helen shan Wolf. Um Wolf was involved in the development
Speaker 1: of Shaky, which is a robot which DARPA describes as
Speaker 1: quote the first mobile robot with enough artificial intelligence to
Speaker 1: navigate on its own through a set of rooms. I
Speaker 1: think somebody at the company also worked on a robot
Speaker 1: called Mobot, which mode lawns and a random and unattended pattern.
Speaker 1: I'm not joking, by the way, where that is the
Speaker 1: thing Revieve mentioned. Yeah, it just always that That makes
Speaker 1: me think of the the old Gumby short where the
Speaker 1: Gumby family have the robots that are doing lawn care
Speaker 1: and home repair and they just go perserk and the
Speaker 1: Gumby family has to as to at them down. I
Speaker 1: don't think I know that one. Oh, it's good. There
Speaker 1: was an MST three k riff five eight years back.
Speaker 1: That sounds horrible. It is. It's horrifying. At the end,
Speaker 1: there's like a robot's head on the wall. It's it's
Speaker 1: terrifying material. But one of the things that ended up
Speaker 1: being the case if if you're trying to think, okay,
Speaker 1: in the sixties, how would you even begin to get
Speaker 1: a computer to recognize a face across different images? Uh.
Speaker 1: One of the problems is, of course, the lack of
Speaker 1: existing digitized imagery at the time, right. I mean, we
Speaker 1: live in a world where digital imagery is ubiquitous. It
Speaker 1: was not at all back then. There was almost none
Speaker 1: of it. Yeah. Yeah, Today when you read about official
Speaker 1: recognition research that are often working off of digital databases
Speaker 1: containing hundreds and not thousands of photographs. Uh. And at
Speaker 1: the time, yeah, how many digital photographs were there in
Speaker 1: the world, right, So they actually had to have some
Speaker 1: kind of digitization process, Like they had to be able
Speaker 1: to take a photo and turn that photo into some
Speaker 1: no numbers that could then be interpreted by the machine
Speaker 1: to try to recognize a person or you know, say that, yeah,
Speaker 1: that's the same person or not. And so a lot
Speaker 1: of these methods involved number one, recognition rules based on
Speaker 1: explicit measures, not machine learning. They didn't have the machine
Speaker 1: learning methods we have today back then. They would have
Speaker 1: to have explicit rules like the distance between randomly selected
Speaker 1: features of the face. So in this image, what's the
Speaker 1: difference between the left eyebrow and the right ear and
Speaker 1: the you know, left eye and the corner the right
Speaker 1: corner of the mouth or something. And furthermore, to get
Speaker 1: those measures, they often had to resort to what they
Speaker 1: called a man machine approach, which was it would pair
Speaker 1: initial measurements made by humans. Uh. It would take those measurements,
Speaker 1: turn them into numerical values, and then train the computer
Speaker 1: to match based on those values, which is quite laborious. UH.
Speaker 1: And the man machine approach was apparently necessary to input
Speaker 1: the measures based really until the seventies, until um Review
Speaker 1: writes quote. In nineteen seventy three, a Japanese computer scientists
Speaker 1: named Takeo Kannate made a major leap in facial recognition technology.
Speaker 1: Using what was then a very rare commodity, a database
Speaker 1: of eight hundred and fifty digitized photographs eight hundred and
Speaker 1: fifty taken from the nineteen seventy World's Fair in sweet To, Japan,
Speaker 1: Kannada developed a program that could extract facial features such
Speaker 1: as the nose, mouth, and eyes without human input, and
Speaker 1: so by doing that, Cannadi was able to finally eliminate
Speaker 1: the man part of the man machine approach the human
Speaker 1: input of the measuring and input of the values. But
Speaker 1: throughout all this period, I mean there were still huge
Speaker 1: problems with machine recognition of faces. Like they would sometimes
Speaker 1: get to the point where the system developed by by
Speaker 1: Panoramic would be it would be more efficient than humans
Speaker 1: at matching faces under ideal conditions. So if you could
Speaker 1: get all the faces like oriented the same way, looking
Speaker 1: right into the camera and all that, the machine would
Speaker 1: be better than humans trying to match photos. But if
Speaker 1: you just pollute the imagery a little bit and make
Speaker 1: people like turn their heads or change the lighting, etcetera. Yeah,
Speaker 1: any kind of problems like that, suddenly the machine loses
Speaker 1: all its advantages and the humans are better again. Right,
Speaker 1: And some of the other problems that are still issues
Speaker 1: today we're problems at the time, like like depending too
Speaker 1: much on say, uh an all lighte male database, you know,
Speaker 1: or something to that effect. You know, where you just
Speaker 1: do not have you don't have a broad enough sample
Speaker 1: of of human appearances to to really have a robust
Speaker 1: facial recognition system. Right. If it's not trained on humans generally,
Speaker 1: it's not gonna work on humans generally, right, Uh. And
Speaker 1: so yeah, like the racial bias problems that show up
Speaker 1: an existing facial recognition technologies today, we're basically they're right
Speaker 1: from the beginning. But towards the end of his article,
Speaker 1: Revieve writes, quote only in the past ten years or so,
Speaker 1: as face show recognition started to become capable of dealing
Speaker 1: with real world imperfection, says A. Neil K. Jane, a
Speaker 1: computer scientist at a Michigan State University and co editor
Speaker 1: of handbook on Face Recognition, nearly all of the obstacles
Speaker 1: that would he encountered, in fact, have fallen away. And
Speaker 1: one of the big ones they point to is not
Speaker 1: just the fact that you can get some digital images now,
Speaker 1: but you can get so many of them that you know,
Speaker 1: it provides these vast databases for for machine learning and
Speaker 1: data sets for training of neural networks and stuff. That's right,
Speaker 1: you can just basically crawl, um, you know, any given
Speaker 1: social media side. And well, we mentioned a few already.
Speaker 1: We'll mention a few different ones as we proceed here. Well,
Speaker 1: maybe we should pivot to talk about the ways that
Speaker 1: facial recognition technology is being used today. Uh And there
Speaker 1: was one thing that I was looking at, uh So
Speaker 1: it was a piece in the New York Times. It
Speaker 1: was an opinion piece a guy named Bruce Schneier, who
Speaker 1: is a fellow at the Harvard Kennedy School, that was
Speaker 1: making a point about facial recognition that I'm not sure
Speaker 1: I fully agree with the framing of. But within making this,
Speaker 1: within writing this article, he articulates something that I think
Speaker 1: is important and clarifying for us to remember as we
Speaker 1: go on. So to first acknowledge his main point, Uh,
Speaker 1: he writes, quote, facial recognition bands are the wrong way
Speaker 1: to fight against modern surveillance. Focusing on one particular identification
Speaker 1: method misconstrues the nature of the surveillance society we're in
Speaker 1: the process of building. So he's arguing facial recognition, you know,
Speaker 1: it's just one technology among many, and banning it doesn't
Speaker 1: necessarily stop all of these other surveillance technologies from doing
Speaker 1: effectively the same thing. Uh. And we can talk later
Speaker 1: about why. I'm probably not really convinced by this argument,
Speaker 1: But Uh, the point that I think is clarifying is
Speaker 1: that he, you know, he says what we call facial
Speaker 1: recognition is not just one act, but it's at least
Speaker 1: three different acts, each coming with their own challenges. Quote
Speaker 1: in all cases. Modern mass r valance has three broad components, identification, correlation,
Speaker 1: and discrimination. So of course identification is just the first step.
Speaker 1: It recognizes who you are. Correlation then associates that ide
Speaker 1: with other information about you, and then finally discrimination treats
Speaker 1: you differently because of that I D or because of
Speaker 1: the associated information. Now, this in and of itself, of course,
Speaker 1: is something that that humans are. Humans are perfectly capable
Speaker 1: of carrying out all three of these tasks without the
Speaker 1: aigive machines. But what we're talking about here is that
Speaker 1: it would be automated. It would be something that would
Speaker 1: be happening by default to everybody, as opposed to something
Speaker 1: that might take place uh and perhaps even laboriously uh
Speaker 1: in specific scenarios such as, you know, once you've been
Speaker 1: flagged at a security checkpoint or something like that, they
Speaker 1: they ask you to pull out your I D and
Speaker 1: then look you up, to correlate you with other information
Speaker 1: and then maybe treat you differently based on whatever they
Speaker 1: find out. That we're dealing with the scenario here where
Speaker 1: that this would all be happening in real time and
Speaker 1: would happen perhaps with very little delay. And I think
Speaker 1: most importantly would be the scale and pervasiveness. It would
Speaker 1: be happening everywhere all the time. We know from experience
Speaker 1: how quickly new digital technology pervades all spaces, so basically,
Speaker 1: you know the one of his arguments is that these
Speaker 1: are three broad components that it might be easier to
Speaker 1: regulate individually, as opposed to saying, let's not do facial recognition. Well, instead,
Speaker 1: let's maybe we go after each of these three things.
Speaker 1: I think that's not necessarily a bad idea to to
Speaker 1: think about a framework for overall regulation of surveillance and
Speaker 1: preservation of privacy. I mean, ultimately, I think I agree
Speaker 1: that it's important to better understand and regulate the entire
Speaker 1: process recognizing the three different components of identification, correlation, and
Speaker 1: discrimination individually. I just don't think it makes a lot
Speaker 1: of sense to frame this is an argument against banning
Speaker 1: a regulating facial recognition, because to me, that sounds kind
Speaker 1: of like saying, well, international treaties banning the use of
Speaker 1: smallpox virus as a weapon of war miss the point
Speaker 1: that we need to rethink our entire concept of war
Speaker 1: and defense, and we need to regulate international conflict in
Speaker 1: a more comprehensive way. Um. I mean, like, yes, that
Speaker 1: would be true. But if you don't know when and
Speaker 1: whether that full comprehensive thing is going to be accomplished,
Speaker 1: and you do currently have a consensus to ban the
Speaker 1: use of germ warfare, why wouldn't you do it? Absolutely? Um,
Speaker 1: here's another quote from that that article though that I
Speaker 1: that I was really taken by quote. The point is
Speaker 1: that it doesn't matter which technology is used to identify
Speaker 1: people that they're currently is. No comprehensive database of heartbeats
Speaker 1: or gates doesn't make the technologies that gather them any
Speaker 1: less effective, And most of the time it doesn't matter
Speaker 1: if identification isn't tied to a real name. What's important
Speaker 1: is that we can be consistently identified over time. And
Speaker 1: then on on top of that though, you know, once
Speaker 1: your real name is then attached to that information, there's
Speaker 1: no turning back, right. The system is building a picture
Speaker 1: of you, move by move, purchased by purchase, search by search,
Speaker 1: over the course of years, if not decades. Yeah. Uh.
Speaker 1: And of course, because it's the Internet, what kind of
Speaker 1: system are we talking about? I mean, I would argue
Speaker 1: that it is a dystopia A little bit less like
Speaker 1: nineteen eighty four and more like Terry Gilliam's Brazil, where
Speaker 1: you know, there's no one person in charge that you
Speaker 1: can rebel against. No one person seems to understand how
Speaker 1: it all works or call all the shots. It is
Speaker 1: a terrible will emanating from the void that is expressed
Speaker 1: through millions of antlike functionaries, each doing it's bidding without
Speaker 1: being bidden. I mean, isn't the oppression scarier when there's
Speaker 1: no boss in charge of it all that you can
Speaker 1: rebel against? Absolutely, And especially when it's uh when it's
Speaker 1: a case like facial recognition where a lot of the
Speaker 1: a lot of the the advancements that have been made,
Speaker 1: and then more specifically a lot of the cases where
Speaker 1: it has been or is being um rolled out. You know,
Speaker 1: it's often it's it's not as a situation where people
Speaker 1: are getting to vote on it or even necessarily having
Speaker 1: any kind of really broad discussion about it before it
Speaker 1: takes place. It just sort of happens and then here
Speaker 1: we are. You know, So not only is there no
Speaker 1: key individual in place that you can blame and rebell against,
Speaker 1: there's like they're not even necessarily a set point in
Speaker 1: time where you could say, we have to go back
Speaker 1: and change this, you know, getting a time machine and
Speaker 1: try and prevent facial recognition from being rolled out. And
Speaker 1: where do you go, Who do you try and stop?
Speaker 1: Who do you try and speak to except the whole world?
Speaker 1: You know? Another thing I would say is to go
Speaker 1: back to my germ warfare analogy. I think it's possible
Speaker 1: that that he's not quite right, that the different methods
Speaker 1: of ideaing you and tracking you are indistinguishable from each other.
Speaker 1: I think it's possible that facial recognition is an especially
Speaker 1: insidious and corrupting type of automated identification compared to some
Speaker 1: other methods, like you know, ideas of credit card numbers
Speaker 1: or math addresses on your phone. Because our lives are
Speaker 1: built around faces, our social existence and our cultures are
Speaker 1: built around interfacing with faces. I mean, it seems like
Speaker 1: somehow a more dangerous kind of well of information to
Speaker 1: poison in the culture than to say, like, well, you know,
Speaker 1: your phone knows where you are. I mean, you could
Speaker 1: potentially smash your phone with a hammer. Yeah, and again,
Speaker 1: I think I've mentioned this already. So many of the
Speaker 1: things involving facial recognition on the surface, it doesn't seem threatening.
Speaker 1: It seems like, well, we wanted machines to do what
Speaker 1: people could do, so we we've created a way for
Speaker 1: them to do it. That's what we always wanted. How
Speaker 1: is that that terrifying? And I think part of it
Speaker 1: is haven't you always wanted a person who is a
Speaker 1: boss who can watch you every minute of the day. Well,
Speaker 1: I mean, yes, when you get do specific examples of it.
Speaker 1: But but I think I think one of the keys
Speaker 1: is that when we're talking about automating this sort of thing,
Speaker 1: and when we're talking about the machine version of it,
Speaker 1: we're talking about a version of it that exists on
Speaker 1: scale and on a scope that is beyond human. And
Speaker 1: that's that's the thing. It's like, we're not talking about
Speaker 1: a human capability anymore. We're talking about an inhuman capability
Speaker 1: the same way that we've you know, we've talked about
Speaker 1: consciousness before and we sort of post the question, well,
Speaker 1: is part of consciousness? The limitation of what we can
Speaker 1: focus our attention on? Is there is part of the
Speaker 1: key to being human? Uh? Is part of it? The
Speaker 1: limitations on what our brains can do, on what our
Speaker 1: senses can do. And we're talking about models that are
Speaker 1: not limited by those senses or those uh, you know,
Speaker 1: those computational resources as beautifully put. And I think you're
Speaker 1: exactly right. I mean, if you could be conscious of everything,
Speaker 1: I'm not sure you would be conscious anymore. I'm not
Speaker 1: sure you'd be a person. Yeah, yeah, I don't think.
Speaker 1: You know, this is something that that far more specialized
Speaker 1: people than myself probably have better arguments about. But yeah,
Speaker 1: to to what extents extent would a super consciousness not
Speaker 1: be a consciousness? It would be beyond what we think
Speaker 1: of a consciousness. Okay, we need to take a quick break,
Speaker 1: but when we come back, we will discuss how facial
Speaker 1: recognition technology is already being used in several countries around
Speaker 1: the world. Alright, we're back, all right, So let's let's
Speaker 1: look at just this is just going to be kind
Speaker 1: of a snapshot at a few different a few different
Speaker 1: stories covering facial recognition as it is being rolled out
Speaker 1: right now as of this recording in at the tail
Speaker 1: end of January. So the first a couple of sources
Speaker 1: of looking at uh. There's a an opinion piece by
Speaker 1: Frederic califooner UH in The Guardian titled Facial Recognition cameras
Speaker 1: will put us all in an identity parade. And this
Speaker 1: piece also refers in places to reporting from the same
Speaker 1: month and publication by Vicram Dodd, a police and crime
Speaker 1: correspondent for The Guardian. So basically, London's Metro Police announced
Speaker 1: their intention to launch live facial recognition cameras in London,
Speaker 1: a city already known for a mass surveillance survey ence
Speaker 1: via their CCTV system. It's a move condemned by civil
Speaker 1: liberty groups as as a quote breathtaking assault on human rights. Uh.
Speaker 1: The police, however, claimed that eight of people surveyed back
Speaker 1: the move and they would only be used to catch
Speaker 1: violent criminals and find missing people. Uh. They also stressed
Speaker 1: that it will be properly posted and it will be
Speaker 1: rolled out with clarity and transparency. Uh and and only
Speaker 1: you know, following outreach in affected communities. Now, you know,
Speaker 1: we've kind of talked about about this already. If you
Speaker 1: if you stand by the notion that, Okay, I trust
Speaker 1: the government as as it exists now, and I trust
Speaker 1: whatever model it will take in the future. I trust
Speaker 1: the keepers of this information and my personal information and uh,
Speaker 1: and I don't have anything to hide anyway. Then okay,
Speaker 1: I guess you can easily get on board with something
Speaker 1: like that, but a lot of folks have have issues
Speaker 1: with this rollout in particular, as well as with facial
Speaker 1: recognition technology in general. So for starters, this comes as
Speaker 1: the European Union is consider during a temporary ban on
Speaker 1: facial recognition, and of course this is also occurring as
Speaker 1: the UK continues to extract itself from the EU. Also,
Speaker 1: the only independent review of the Metro facial recognition public trials,
Speaker 1: by one Pete to Fussy of Essex University, found that
Speaker 1: it was only verifiably accurate in just nineteen percent of
Speaker 1: the cases that opposed to the uh TOO. I think
Speaker 1: it was something like a seventy success rate that the
Speaker 1: Metro police were claiming. Yeah, I mean we saw in
Speaker 1: the first episode we talked about how the company clear
Speaker 1: View AI claimed that they found correct matches up to
Speaker 1: seventy percent of the time. But we've definitely read reviews
Speaker 1: that that placed correct to numbers way way lower. Right,
Speaker 1: And and the other thing is there's no there's no
Speaker 1: opt out here. This is not even though they're talking
Speaker 1: about targeted uses of it, the technology is not targeted.
Speaker 1: All faces are scanned that are at all scannable, and
Speaker 1: therefore everyone is in a virtual criminal line up whenever
Speaker 1: they're in the sights of the camera. Um. This is
Speaker 1: a quote from that opinion piece quote. Given that the
Speaker 1: system inevitably processes the biometric data of everyone live, facial
Speaker 1: recognition has the potential to fundamentally change the power relationship
Speaker 1: between people and the police, and even alter the very
Speaker 1: meaning of public space. So the you know, the standard
Speaker 1: criticisms come up as well, including misidentification, but not only misidentification,
Speaker 1: but also automated misidentification, which the author charges shifts the
Speaker 1: burden of proof onto the falsely recognized individual because there's
Speaker 1: no human accuser, right, it's just the machine said you
Speaker 1: did this, and it's it's up to you to prove
Speaker 1: that you are not that face, that that face is
Speaker 1: not yours. Again and and and this is and I
Speaker 1: want to stress that this is problematic even if that
Speaker 1: even if the success rate were not so low, like
Speaker 1: because obviously the technology is improving and that is not
Speaker 1: the ants or that is part of the problem. No,
Speaker 1: I'm more scared when the success rate gets verifiably higher,
Speaker 1: because then if the trust and it just keeps going
Speaker 1: up and up. The fewer cases where people still get misidentified,
Speaker 1: which always is going to continue to happen to some degree,
Speaker 1: are are going to get more and more like, you know, terrified.
Speaker 1: They're going to be more and more under the knife, right,
Speaker 1: I mean you end up with it again, it changes
Speaker 1: what is a public space. Well, a public space then
Speaker 1: I can't go to the park without being in a
Speaker 1: virtual uh like a lot of crime line up, Like
Speaker 1: I am always going to be profiled as to be
Speaker 1: as a potential criminal. So yeah, it's a it's a
Speaker 1: good piece, worth a worth reading. So Caul Throner, you know,
Speaker 1: sums this up by saying, you know, look, the stakes
Speaker 1: are very high with this kind of invasive technology. And
Speaker 1: while these rollouts are not without their critics. Obviously we've
Speaker 1: been talking about the various criticisms of it, but they
Speaker 1: argue that we still haven't really seen them subjected to
Speaker 1: true public debate. The technology is happening faster than the
Speaker 1: awareness of it is exactly. Yeah. Yeah, And I would
Speaker 1: point out another thing about it, which is just that, Okay,
Speaker 1: if you suddenly put a pause on this kind of
Speaker 1: technology and say we can't use it, and then you
Speaker 1: go under a review, when you review and review and review,
Speaker 1: and you're sure in the end, okay, it definitely the
Speaker 1: benefits outweigh the harms. Then you could still release it
Speaker 1: in the future. But I think you can't really go
Speaker 1: the other way, right, once you live in the facial
Speaker 1: recognition world, that animals not getting back in the cage, right, Yeah,
Speaker 1: once you've sort of eroded the norms of of privacy,
Speaker 1: like how do you go back? You know, you and
Speaker 1: quickly enter an age where people who don't want to
Speaker 1: be a part of the database are going to be
Speaker 1: seen as the outsiders in the fringe cases and the
Speaker 1: you know, the people who live out in the hut
Speaker 1: in the woods. And perhaps that would be also because
Speaker 1: they would be effectively ostracized from so many of these systems. Yeah. Uh,
Speaker 1: And again we're mainly talking about like state uses or
Speaker 1: lease uses or whatever, but there's a whole other world
Speaker 1: to consider of just like, uh, distributed public use of
Speaker 1: facial recognition systems, which could be hugely socially important, could
Speaker 1: lead to new types of social ostracism and stuff. Yeah,
Speaker 1: or situations where oh well, I'm I'm opting out of
Speaker 1: facial recognition technology. Oops. Looks like it's gonna make it
Speaker 1: really difficult for me to pay my bills because I
Speaker 1: need my face for identification. On that, we'll get to
Speaker 1: some examples of of that currently in use here in
Speaker 1: a bit. Or how about I mean this might sound petty,
Speaker 1: but imagine a world where, uh, anytime somebody invites you
Speaker 1: to do something and you say no, I can't, they
Speaker 1: can also very likely find out wherever you were at
Speaker 1: the time you couldn't do the thing they invited you to. Die. Yeah,
Speaker 1: I was just having a conversation with my wife about
Speaker 1: smartphone technology, about how I had recently been to a
Speaker 1: concert venue and uh, and I'm not going to name
Speaker 1: the organization that was handling a concert, but you had
Speaker 1: to have a mobile entry ticket, which, as far as
Speaker 1: I know, that meant it had to be I could
Speaker 1: be wrong on this, but my understanding was you had
Speaker 1: to have like a smartphone version of your ticket to
Speaker 1: get in, which I just kind of blew me agg
Speaker 1: And I'll what if you don't have a smartphone? Not
Speaker 1: everyone has one? Does this mean like only smartphone people
Speaker 1: are getting into this thing? And my wife had a
Speaker 1: similar situation with a parking deck where you had to
Speaker 1: have an app on your phone to get and and
Speaker 1: so you can easily imagine this kind of scenario extrapolated
Speaker 1: to to facial recognition, like, oh, you're not opting in
Speaker 1: on facial recognition, Well, I'm sorry. You can't come to
Speaker 1: this this this concert because you have to have a
Speaker 1: face scandy get in and your face will be scanned
Speaker 1: of course while you're in the venue by the security systems.
Speaker 1: But the UK, of course is by far not the
Speaker 1: only place that's already trying to roll out some kind
Speaker 1: of public form of facial recognition technology. I've read about
Speaker 1: uses in China. I've read about uses in Russia. Yeah,
Speaker 1: even in Russia, though, civil rights activists are criticizing facial
Speaker 1: recognition technology is a threat to privacy and human rights,
Speaker 1: specifically for the technology's ability to say, identify individuals at protests,
Speaker 1: store them a database, and then track them. Right, you
Speaker 1: could easily be put on the you know, the government's
Speaker 1: undesirables list. Here's a quote from Natalia's Virgina, Amnesty International
Speaker 1: Russia's director. She's quoted in the January BBC article Russia's
Speaker 1: use of facial recognition challenged in court. Quote, facial recognition
Speaker 1: technology is by nature deeply intrusive, as it enables the
Speaker 1: widespread and bulk monitoring, collection, storage, and analysis of sensitive
Speaker 1: personal data without individualized reasonable suspicion. Again, everybody's in the
Speaker 1: criminal lineup. Yeah, you don't have to do anything in
Speaker 1: particular to arouse suspicion of yourself just by being in public,
Speaker 1: that's enough. According to the article that PBC article, Moscow
Speaker 1: has about a hundred and sixty thousand CCTV cameras in
Speaker 1: operation in the city and this month month they expanded
Speaker 1: the number using facial recognition with no x a nation
Speaker 1: of how privacy and human rights would be insured or
Speaker 1: I mean it seems if they would being insured, because
Speaker 1: it seems like the basic system would would be an
Speaker 1: athema to that. The BBC article also pointed out that
Speaker 1: the Moscow depart Department of Information Technology has reportedly signed
Speaker 1: a deal with the Russian firm intech Lab to provide
Speaker 1: the need to pride the needed technology, and this firm
Speaker 1: had previously rolled out the the Fine Face app, which
Speaker 1: used data from a website that is often referred to
Speaker 1: as Russia's Facebook. It is not an actual Facebook website,
Speaker 1: but is often held up as the equivalent. Yeah, there
Speaker 1: are a lot of different sort of other countries facebooks,
Speaker 1: and then there's China to consider. So China has also
Speaker 1: rolled out facial recognition, and there are a number of
Speaker 1: different uses and implicatement implementations that you can find just
Speaker 1: all over the place. But you can find it in
Speaker 1: train stations, airports, stores, hotels, gated communities, and more. I
Speaker 1: think I had seen allegations that it had been used
Speaker 1: in um in attacks on the weaker communities. Basically, yes,
Speaker 1: that's a big one, the tracking of the alleged tracking
Speaker 1: of ethnic minorities in China using these systems. But you
Speaker 1: also you also find it another place, like it's used
Speaker 1: for things that are almost comical but also troubling by
Speaker 1: just how tedious and small steaks they seem, such as
Speaker 1: you know, using it to catch toilet tissue thiefs at
Speaker 1: bathroom um as. Some of what I was reading about
Speaker 1: it came from a New York Times article by any
Speaker 1: chin Uh that's about q I in it. In case
Speaker 1: you want to look around, I'm not gonna name I'm
Speaker 1: not gonna mention the title of the article because it
Speaker 1: will spoil the fun of what I'm about to share.
Speaker 1: But anyway, she in this she described China as a
Speaker 1: quote a country accustomed to surveillance, which I think is
Speaker 1: a is an interesting way of looking at because I
Speaker 1: wonder if it is if China's case in some way
Speaker 1: provides a model of where some of these other countries
Speaker 1: we've mentioned could be head it very soon, Like it
Speaker 1: is just given a a culture that is in very
Speaker 1: broadly um predisposed to to being okay with surveillance. Uh,
Speaker 1: it might provide a future look at where where we're
Speaker 1: going in these other countries. Uh. For for example, you
Speaker 1: see quite a bit of facial recognition used to do
Speaker 1: stuff like open your phone or make a payment, something
Speaker 1: that the South China Morning Post points out has been
Speaker 1: disrupted by the current coronavirus outbreak. So in the wake
Speaker 1: of this current global health threat which has impacted China
Speaker 1: the most thus far, lots of people are wearing surgical masks,
Speaker 1: and they were already in many cases wearing these masks
Speaker 1: and high numbers due to pollution concerns. But the software
Speaker 1: that is used in many of these instances is apparently
Speaker 1: struggling to deal with such little facial information, requiring other
Speaker 1: biometric data uh, in order to still I D and individual.
Speaker 1: So so Chen's article goes into this about like people
Speaker 1: basically have to make a choice between uh, being able
Speaker 1: to easily make payments at stores using facial recognition or
Speaker 1: feeling like they are adequately protected against a dangerous illness.
Speaker 1: This is a great example of something that I was
Speaker 1: searching for an example of later in the episode. So
Speaker 1: we'll have to remember this later. Now I mentioned something
Speaker 1: kind of fun but also still scary, and that it
Speaker 1: seems small steaks and uh. And that is the central
Speaker 1: point of Chen's recent article, and that's the use of
Speaker 1: facial recognition technology to root out quote uncivilized behavior in
Speaker 1: in an Hua Province in eastern China. So using facial
Speaker 1: recognition data, she writes, the urban management the Urban Management
Speaker 1: Department of SUSO published surveillance photos of individuals engaged in
Speaker 1: said uncivilized behavior with partial exposure of their names. So
Speaker 1: like a public shaming or public outing, kind of like saying,
Speaker 1: look at these people, her her name is, you know,
Speaker 1: Amy or whatever. Uh, here they are in gauged in
Speaker 1: uncivilized behavior. They should not do this. What was the
Speaker 1: uncivilized behavior? I know you you would think Okay, is
Speaker 1: it defacing you know, a public place, stealing the toilet
Speaker 1: paper even is low stakes? Is that same? No, it's
Speaker 1: even lower stakes. It's the wearing of full length pajamas
Speaker 1: in public. What yeah, So, I mean I've seen people
Speaker 1: wear pajamas in public. I am currently wearing clothes as
Speaker 1: I record this. I'm wearing clothes that I also sleep in.
Speaker 1: So I think the last time you came to my
Speaker 1: house to let me borrow a book, I answered the
Speaker 1: door in pajamas and didn't realize until you were there.
Speaker 1: Because I'm sorry, man, No, no, because you don't have
Speaker 1: to apologize, because this is the universal truth. Chen also
Speaker 1: describes it thus in her article, pajamas are comfortable. They're
Speaker 1: very comfortable, and public pajama wearing is common in China,
Speaker 1: especially among older women and especially in cold months and regions.
Speaker 1: And to add another level to the injustice is the
Speaker 1: sort of thing that is praised as fashionable when celebrities
Speaker 1: do it, but to this sort of backlash when it's
Speaker 1: common people. And it's been a point of contention um
Speaker 1: for for a little while here, with government officials trying
Speaker 1: to ban it in some places, but the people pushing
Speaker 1: back and say no, you know this, this is a
Speaker 1: bridge too far. I want to wear my comfy pajamas
Speaker 1: and if I want to wear them outside of my home,
Speaker 1: I would do so. It's not uncivilized to do this.
Speaker 1: And in this case, it's a rare it's a rare
Speaker 1: example of of people in China pushing back against facial
Speaker 1: recognition technology in a place again where the technology has
Speaker 1: already been highly established and is is recognized and appreciated
Speaker 1: for the things that it makes easier in life, such
Speaker 1: as making payments. So when you're purchasing something at the store, yeah,
Speaker 1: I mean the process there is so clear suddenly, how
Speaker 1: like you are lured in with convenience, you know, you're
Speaker 1: lured in with immediate concrete benefits, and then there are
Speaker 1: these consequences that just come later. Right, Yeah, And again
Speaker 1: it shows also it should shows you what happens when
Speaker 1: the government or the police, you know, change their mind
Speaker 1: about how specific they need to be with the laws
Speaker 1: that they readily enforce. You know. It's it's like, it's
Speaker 1: it's similar case to you know that we often encounter
Speaker 1: with say traffic cameras, like how are we supposed to
Speaker 1: feel about like, yes, we don't want people just you know,
Speaker 1: going through the traffic running every red light. There needs
Speaker 1: to be order, uh, And I guess you know, there
Speaker 1: needs to be there needs to be this idea that, yes,
Speaker 1: if you break these rules, you will you know, you
Speaker 1: could be pulled over, you get a ticket, there could
Speaker 1: be some sort of a punishment in place. But when
Speaker 1: you have this situation where any any transgression, uh, you know,
Speaker 1: for you know, for even the smallest thing can be
Speaker 1: met with an automated fine. Uh. You know, that gets
Speaker 1: into an area that feels more like dystopia than order. Yeah,
Speaker 1: it certainly can. I mean again, it's one of those
Speaker 1: things where it starts to get harder to argue with,
Speaker 1: Like how do you argue with the machine? The camera
Speaker 1: said you were speeding and you're like, I wasn't speeding.
Speaker 1: What do you do? Yeah? So anyway, these I think
Speaker 1: these these examples, I think they just they just helped
Speaker 1: provide a more a more nuanced idea of like what's
Speaker 1: going on in the world right now with facial recognition, uh,
Speaker 1: and what the many pain points are and and and
Speaker 1: what people are where people are fighting back against him. Yeah.
Speaker 1: So I think now that we better understand how the
Speaker 1: technology is being deployed. Of course, in the first episode
Speaker 1: we talked about how it's being deployed even in the
Speaker 1: United States. The question is how should we react, Like,
Speaker 1: what can we do? Uh? And I think maybe it
Speaker 1: would be best to first talk about individual countermeasures and
Speaker 1: then come back to broader action after that. Alright, so
Speaker 1: individual countermeasures so something that an individual person can do
Speaker 1: in the face of facial recognition technology. Yes. So. A
Speaker 1: bunch of the existing knowledge about how to confuse and
Speaker 1: confound facial recognition tech was summarized in a really good
Speaker 1: article I was reading and wired by at least Thomas
Speaker 1: from February of nineteen called how to Hack Your Face
Speaker 1: and Dodge The Rise of Facial Recognition Tech, And unfortunately,
Speaker 1: Thomas points out that the best way to foil a
Speaker 1: facial recognition system is to know what method of facial
Speaker 1: recognition is being used. Thomas quotes a privacy advocate named
Speaker 1: Lily Ryan, who says, quote, you really need to know
Speaker 1: what's under the hood to know what's most likely to work,
Speaker 1: And it can be very hard for the average person
Speaker 1: to know what kind of facial recognition is being used
Speaker 1: on them at any particular time. So it's worth pausing
Speaker 1: to look at different kinds of rebellion against the recognizing machine.
Speaker 1: I think the first one is the simpler one. That
Speaker 1: the first would be an anonymity defense, some method of
Speaker 1: making your actual identity unrecognizable and presenting as an unknown,
Speaker 1: unscannable person. This would essentially trying to become faceless, yeah, exactly.
Speaker 1: The second path would go beyond that into what are
Speaker 1: called presentation attacks in some of the literature. For example,
Speaker 1: there's an article that's linked by Thomas by Raga, Vendra
Speaker 1: Rama Chandra and Kristoff Bush called Presentation Attack Detection Methods
Speaker 1: for Face Recognition Systems. A comprehensive survey published in in
Speaker 1: a c M Computing Surveys and so this is a
Speaker 1: survey of known presentation attacks, also known as direct attacks
Speaker 1: or spoof attacks the author's right quote. The goal of
Speaker 1: a presentation attack is to subvert the face recognition system
Speaker 1: by presenting a facial biometric artifact. So if you go
Speaker 1: in front of a facial recognition scanner and you hold
Speaker 1: up a picture of Nicolas Cage, or you wear a
Speaker 1: Richard Nixon mask or something, you are conducting a presentation attack.
Speaker 1: You're not just trying not to be recognized as yourself,
Speaker 1: but actively trying to be recognized as someone else. Common
Speaker 1: methods here would include like presenting a photo of someone
Speaker 1: to a scanner that has actually worked in a lot
Speaker 1: of cases, uh, playing a video of the target face,
Speaker 1: or wearing a three D mask of somebody else's head.
Speaker 1: All of these methods have had some success us, but
Speaker 1: the researchers here describe presentation attack detection algorithms or p
Speaker 1: A D algorithms that are countermeasures against the countermeasures. Now,
Speaker 1: a question you might be wondering is like, well, why
Speaker 1: would you want to present as someone else instead of
Speaker 1: just being anonymous? Well, I mean there there are all
Speaker 1: sorts of reasons for that. I mean, there's certainly nefarious
Speaker 1: reasons for that. If you get into a situation where
Speaker 1: saying facial recognition is required to interrogated community, well, then
Speaker 1: if you wanted to break into sedigated community, it would, uh,
Speaker 1: it would behoove you to have another person's face to wear.
Speaker 1: Perhaps you know, print it out exactly right, That could
Speaker 1: be the direct reason. Maybe you want access to a
Speaker 1: location or a device, and access is granted to specific
Speaker 1: people based on facial recognition, So you use an authorized
Speaker 1: person's face in order to get in. But I can
Speaker 1: also see another idea, which is perhaps rampant presentation attacks
Speaker 1: could be an effective method for fighting the facial recognition
Speaker 1: reign of terror. Beca is it would not deny these
Speaker 1: data collecting systems the data they want, not just do that.
Speaker 1: It would go further and come up the databases with
Speaker 1: lots of confusing, incorrect information, which might in fact make
Speaker 1: them less useful overall. Yeah. I mean another application here
Speaker 1: that is awful to think about is the use of
Speaker 1: you We're talking about sort of the automated guilt machine
Speaker 1: that could exist with facial recognition technology. Say you had
Speaker 1: it in for somebody you know you're mad at, you know,
Speaker 1: a coworker or a schoolmate. Then you go and you
Speaker 1: do something illegal with with a mask of that person's face.
Speaker 1: You know, not enough to say send them away forever,
Speaker 1: but enough to say, uh, you know, to to cause
Speaker 1: them a lot of grief in the short term, in
Speaker 1: the very least. I mean, you don't know how well
Speaker 1: their their defense would be. I mean, maybe it would
Speaker 1: send them away forever. I mean, I don't know how
Speaker 1: much faith the criminal justice system is going to end
Speaker 1: up putting in the verdicts of these machines. I wouldn't
Speaker 1: be surprised if it's too much. We've seen that before.
Speaker 1: We've certainly seen models of that with some of the
Speaker 1: past episodes where where we've discussed a forensic science. Yeah, exactly, Yeah,
Speaker 1: there are a lot of methods of forensic science that
Speaker 1: have been vastly overestimated in their in their confidence. Now, finally,
Speaker 1: another reason I was thinking it might be useful to
Speaker 1: present as another human instead of just trying to make
Speaker 1: yourself anonymous is it's not hard at all to imagine
Speaker 1: scenarios were being unscannable or being anonymous, will itself be
Speaker 1: a problem, will restrict your rights, make you a target, etcetera. Like,
Speaker 1: the anonymity could attract attention rather than discouraging it. So
Speaker 1: the alternative would be to appear as a real scannable person,
Speaker 1: but not yourself. Right, Wearing a faceless mask in public
Speaker 1: is gonna draw more attention than looking like someone else. Yes,
Speaker 1: even even if it wasn't like that great a mask
Speaker 1: you know, uh it would, it would still it was
Speaker 1: still potentially draw less attention. Now back to Thomas's article,
Speaker 1: Thomas writes, that. Of course, you know, the simplest method
Speaker 1: of fighting facial recognition is what would normally be called occlusion,
Speaker 1: hiding all or part of your face. But again, this
Speaker 1: is more difficult and more complicated than it sounds. So
Speaker 1: let's say you want to walk around in public with
Speaker 1: your face completely hidden behind a cloth or a zip
Speaker 1: up hood or something. You know that there are there
Speaker 1: are actually people selling basically backwards hoodies, you know, front
Speaker 1: zip hoodies that cover your entire head. First of all,
Speaker 1: is that legal in a lot of places? No, in
Speaker 1: a lot of places in for example, Europe and Canada.
Speaker 1: In the US, it's illegal to cover your entire face
Speaker 1: in public. But even if these laws were changed or
Speaker 1: you're in a place where that's not illegal, is this
Speaker 1: socially feasible. We'll come back to that in a minute.
Speaker 1: But okay, let's say you decided it is not practical
Speaker 1: to completely occlude your entire face. What if you just
Speaker 1: cover part of your face? Unfortunately, Thomas writes that a
Speaker 1: lot of facial recognition software is good enough now to
Speaker 1: make partial covering of the face ineffective as a defense.
Speaker 1: Uh quote. For example, a bala clava, which leaves the
Speaker 1: most important facial features exposed the eyes, the mouth, the
Speaker 1: nose may not actually do much to prevent a person
Speaker 1: from being identified. Researchers have found that by using a
Speaker 1: deep learning framework trained on fourteen key facial points, they
Speaker 1: were able to accurately identify partially occluded faces most of
Speaker 1: the time. This includes wearing glasses, scarves, hats, or fake beards. Yeah.
Speaker 1: I mean when you're getting down to things like like
Speaker 1: the measurement between your eyes, you know, stuff like that.
Speaker 1: I mean, it's it's probably going to be visible. And
Speaker 1: most of these facial inclusion methods and it's it's not
Speaker 1: something you can easily mess with, you know, I mean
Speaker 1: short of like massive facial injury. Uh, I can't think
Speaker 1: of anything much that's going to alter that measurement. Yeah,
Speaker 1: and they're apparently multiple measures like that of a face.
Speaker 1: You know, as long as you want to have your
Speaker 1: eyes and your mouth and stuff exposed, is there are
Speaker 1: probably going to be systems, especially in the near future,
Speaker 1: that will be fairly accurate identifying you. Anyway. Now, another thing,
Speaker 1: as I mentioned a minute ago, there's some evidence that
Speaker 1: three D printed masks based on other people's faces can
Speaker 1: be pretty effective, but they might not remain effective for long. Remember,
Speaker 1: of course, we've got these pa D algorithms, the presentation
Speaker 1: attack detections, and they're developing quickly. And also, I mean,
Speaker 1: is that anything close to practical for regular people? Like
Speaker 1: it seems more like an option that might be available
Speaker 1: to a professional spy, but not just as somebody trying
Speaker 1: to live their life. Now, there's another interesting method that
Speaker 1: at least Thomas mentions in her article, which is confusing
Speaker 1: the computer into believing it is not looking at a
Speaker 1: face at all, attacking not the facial recognition stage, but
Speaker 1: the facial detection stage. I hadn't so much thought about that,
Speaker 1: and I think that's a really good point. One solution
Speaker 1: along these lines is widely known as c V dazzle,
Speaker 1: which stands for Computer Vision Dazzle dazzle kind of in
Speaker 1: the same sense it's used in like a military context.
Speaker 1: You would put dazzle on the side of a ship
Speaker 1: in a context of warfare, which are like different lines
Speaker 1: going in different ways that apparently make it harder to
Speaker 1: look at the ship and determine its speed and it's bearing,
Speaker 1: like like some of the more like lightning bolty camouflage designs.
Speaker 1: One sees not not necessarily be dazzled, which is the
Speaker 1: first thing that came to my mind, like just bedazzle
Speaker 1: your face. Well that it's funny because that does kind
Speaker 1: of come in but but yeah, I think it's based
Speaker 1: on the idea of a visual dazzle, and so what
Speaker 1: it involves for people is altering the appearance of your
Speaker 1: head to stop facial detection algorithms from flagging it as
Speaker 1: a face. And the most common ways to do this
Speaker 1: are with makeup, with hair styling, and coloring with facial
Speaker 1: accessories like hair clips and stick on rhinestone. So there
Speaker 1: is some bedazzling going on. Robert I included a few
Speaker 1: examples here for you to look at. These are from
Speaker 1: a website cv dazzle dot com. We offers some explicit
Speaker 1: style guides that people can use. Oh wow, these are great.
Speaker 1: I mean these look like futuristic hair and makeup designs
Speaker 1: that you might see in like Blade Runner or something. Yes,
Speaker 1: now again here, clearly, the the purpose of these designs
Speaker 1: is not just to make your face look different. It
Speaker 1: is to try to make your face look like something
Speaker 1: other than a face, which could but I can see
Speaker 1: proving potentially difficult coming back again to that that blog
Speaker 1: post that we discussed in one of the earlier episodes,
Speaker 1: um about about trying to to fool the Skype facial
Speaker 1: recognition software. Yeah, the background blurring thing. Yeah yeah, so
Speaker 1: like you know, even the stuff giraffe was getting recognized
Speaker 1: as mostly a face. Uh you. So it's it's a
Speaker 1: more difficult challenge than than one might think. But but again,
Speaker 1: these visual examples from c v Dazzle dot com I
Speaker 1: think will be very informative if you can't quite picture it,
Speaker 1: look these up, yeah totally. Um. Now, a lot of
Speaker 1: them involved things like, um, sort of different streaks of
Speaker 1: light and dark in the hair and in lines across
Speaker 1: the face, and makeup hair partially covering the face in
Speaker 1: a lot of cases, things stuck on the face that
Speaker 1: kind of make it make the shape look different. Thomas
Speaker 1: quotes Kristoff Bush, one of the authors of that study
Speaker 1: we mentioned earlier, and he says, quote from an academic
Speaker 1: research perspective, the makeup attack is gaining more attention. However,
Speaker 1: this kind of attack demands good makeup skills in order
Speaker 1: to be successful, and that's a really good point. In
Speaker 1: other words, it's not good enough just to do some
Speaker 1: unusual things to your hair and put different colored makeup
Speaker 1: on at random. The face design needs to be specially
Speaker 1: tailored to obscure and break up specific like lines and
Speaker 1: shapes and points on the face in order to make
Speaker 1: it sabotage the detection stage. So if if you want
Speaker 1: to know what these specific techniques are, you can find
Speaker 1: guides online from people who study the issue. Now, I
Speaker 1: think this is really cool, and you know, being being
Speaker 1: somebody who's into I guess weirdness myself, like I like
Speaker 1: these styles. Like if I saw somebody wearing these styles,
Speaker 1: I would think it was cool. But I I think
Speaker 1: we should be real and say these styles are in
Speaker 1: daily life simply not socially feasible for a lot of people,
Speaker 1: maybe most people. Well, for one thing, socially, they're going
Speaker 1: to have the opposite effect. Instead, you're going to draw
Speaker 1: attention to yourself from humans, even if you are obscuring
Speaker 1: the attention of the machine. Yeah, that's exactly right. And
Speaker 1: in addition to that, a lot of people's friends, families,
Speaker 1: especially workplaces will probably not be okay with them styling
Speaker 1: their hair and makeup deliberately to make their face look
Speaker 1: as unlike a face as possible. I'm okay with it
Speaker 1: around here. Yeah, me too. I don't know if the
Speaker 1: bosses would be okay, right, You can easily imagine like
Speaker 1: a very conservative or a governmental kind of employer, who
Speaker 1: would you know, we're gonna have a very firm idea
Speaker 1: of what your hair and your your makeup should consist of.
Speaker 1: And again, if you're not allowed to wear, say, pajamas
Speaker 1: in public, I can't imagine this would lie either. And
Speaker 1: there's another complication actually that that makes this even more difficult.
Speaker 1: So the CV dazzle method is only effective at fighting
Speaker 1: face detection technologies that rely on visible light, and not
Speaker 1: all do Thomas sites. For example, Apple's Face i D
Speaker 1: which act which actually uses infrared light. The system detects
Speaker 1: sort of more about the underlying contours and like bone
Speaker 1: structure of your face, and it is not easily thrown
Speaker 1: off by unusual patterns of light and dark colors. The
Speaker 1: CV dazzle wouldn't necessarily affect a system like that, Uh,
Speaker 1: though there are other methods you could maybe use. Apparently,
Speaker 1: you might be able to protect your face from infrared
Speaker 1: detection by, for instance, wearing a hat that projects infrared
Speaker 1: light on your face in weird patterns, as demonstrated by
Speaker 1: one study from Chinese and American researchers in But again, like,
Speaker 1: is that realistic that that people would be able to
Speaker 1: do that? Thomas mentions another method that I like overwhelming
Speaker 1: to re action. Uh, kind of like a visual denial
Speaker 1: of service attack on facial recognition. The solution here is
Speaker 1: pretty simple. You cover yourself in lots of images of faces,
Speaker 1: shirt scarf, ear rings, all with pictures of faces on them.
Speaker 1: Would this always work? Probably not, but it will be
Speaker 1: effective with some systems. And she ends her article by
Speaker 1: mentioning again like major problems with existing facial recognition technology
Speaker 1: that we've already alluded to, like huge numbers of false matches,
Speaker 1: errors that skew along race and gender lines, all kinds
Speaker 1: of problems like that. She ends up saying, quote, the
Speaker 1: real solution to issues around facial recognition the tech community
Speaker 1: working with other industries and sectors to strike an appropriate
Speaker 1: balance between security and privacy in public spaces. Uh, Which
Speaker 1: that may be an answer, but I mean I wonder
Speaker 1: will that be good enough. In the first episode of
Speaker 1: the series, we discussed several figures who have called for
Speaker 1: either strong regulations or even outright bands on face recognition technology.
Speaker 1: So I think next we should look at that as
Speaker 1: a solution, maybe after we come back from a break.
Speaker 1: All right, we'll be right back, thank thank alright, we're back.
Speaker 1: So we were just talking about the the individual approaches
Speaker 1: to fighting facial recognition, like things you could do to
Speaker 1: disrupt your own image and or potentially full facial recognition device.
Speaker 1: But now we're getting more into bands and regulations, the
Speaker 1: broader governmental legislative moves that could be made to keep
Speaker 1: this kind of technology from getting out of control. Yeah,
Speaker 1: And there was one article I was reading that I
Speaker 1: thought was pretty straightforward and made a very good case.
Speaker 1: It was by Evans Sellinger and Woodrow heart Zog, published
Speaker 1: in The New York Times in October seventeen nineteen. It
Speaker 1: was called, what happens when employers can read your facial expressions?
Speaker 1: That's that's a great question, um uh. So Sellinger is
Speaker 1: a professor of philosophy at the Rochester Institute of Technology,
Speaker 1: and heart Soog is a professor of law and computer
Speaker 1: science at Northeastern University. And they are responding to the
Speaker 1: fact that, of course many many are calling for a
Speaker 1: band on this technology. Uh, and this is one of
Speaker 1: these rare cases left in in the US politics, at
Speaker 1: least where there is actually some bipartisan agreement. Apparently, some
Speaker 1: right wing politicians like Jim Jordans have expressed concern. Speaking
Speaker 1: to NPR in July twent nineteen, he said he thought
Speaker 1: it was time for a time out on this technology
Speaker 1: and that we needed to put safeguards in place before
Speaker 1: we went forward with developing it. Meanwhile, you've got left
Speaker 1: wing leaders Alexandria Cassio Cortez and Bernie Sanders have called
Speaker 1: have called for regulation or bands on facial recognition. I
Speaker 1: think uh Sanders announced the band as part of a
Speaker 1: criminal justice reform agenda for his presidential campaign. So all
Speaker 1: over the map, people are throwing up flags and saying
Speaker 1: we stop this is this is scary, We need to
Speaker 1: do something about it, and that at least is reassuring.
Speaker 1: Let's just hope that that can maintain that. You know
Speaker 1: that there there remains bipartisan support. It doesn't wind up
Speaker 1: politicized in way or the other. But but certainly, the
Speaker 1: idea of your face not becoming part of a massive database,
Speaker 1: the idea of not being in a perpetual police lineup,
Speaker 1: I feel like like that is going to strike a
Speaker 1: chord with with with with with with most demographics in America. Well,
Speaker 1: I think it's one of those weird things where there
Speaker 1: is some bipartisan support, but there's also just not nearly
Speaker 1: enough awareness. So like a few people on different parts
Speaker 1: of the political spectrum are all sort of in agreement
Speaker 1: about this, like wait a minute, we need to do something,
Speaker 1: But it's not a lot of people overall, right, and
Speaker 1: if all, and if the only thing you've really heard
Speaker 1: has been like say, pitched from say a company that
Speaker 1: is specializing in this, perhaps with a law enforcement focus,
Speaker 1: like you just you just might think, oh, well that
Speaker 1: sounds fine, say, you know, find missing people and stop criminals. Sure,
Speaker 1: I'll sign off on that those are good things. These
Speaker 1: are good things, but it's not the complete picture, right
Speaker 1: uh So they also the authors here note that many
Speaker 1: local governments have already formally restricted their government agencies, including police,
Speaker 1: from using it, so nothing really strong has happened at
Speaker 1: the national level, but like San Francisco, Oakland, Berkeley, Somerville, Massachusetts,
Speaker 1: and some other local areas have have put a ban
Speaker 1: in place. So the authors of this piece, Hertzog and
Speaker 1: Sellinger here argue that it is not enough just to
Speaker 1: limit the ways in which facial recognition is used, or
Speaker 1: to say, restrict government agencies such as police departments, from
Speaker 1: buying these tools. They say that, unfortunately, the only way
Speaker 1: to actually protect ourselves is to enact a complete and
Speaker 1: total ban on the technology. Quote, we must ban facial
Speaker 1: recognition in both public and private sectors before we grow
Speaker 1: so dependent on it that we accept its inevitable harms
Speaker 1: as necessary for progress. Perhaps over time, appropriate policies can
Speaker 1: be enacted that justify lifting a ban, but we doubt it.
Speaker 1: So I think they make a pretty good case, and
Speaker 1: and we'll get to a little bit more about it
Speaker 1: in a second. But you know, you might be wondering,
Speaker 1: like if there is actually some bipartisan agreement that facial
Speaker 1: recognition could be devastating to our basic liberties, like what's
Speaker 1: the hold up? You know, what's the problem. And so
Speaker 1: of course, the authors note that in general, the United
Speaker 1: States is very reticent to enact bands on technology like
Speaker 1: with with one of the few counter examples being various
Speaker 1: types of malware. And I think that's good because I
Speaker 1: think it makes sense to think of facial recognition technology
Speaker 1: as a form of cultural malware seeps in makes copies
Speaker 1: of itself. You know, you get it as a byproduct
Speaker 1: of something you wanted to download. But of course there's
Speaker 1: another thing. The authors don't really speculate about these motives,
Speaker 1: but obviously one big hurdle is that there's just a
Speaker 1: lot of money to be made in this sector, and
Speaker 1: even worse, there are significant sunk costs. Like many extremely
Speaker 1: talented people and powerful institutions have already devoted significant resources
Speaker 1: and time to improving facial recognition software. And we know
Speaker 1: that humans do not like abandoning sunk costs. Once you've
Speaker 1: already invested in something, you're kind of your psychologically stuck
Speaker 1: with it. You know, do you want to throw all
Speaker 1: the all that work in the trash now? Right? Right?
Speaker 1: You know, companies that have we have created this kind
Speaker 1: of technology that are looking for ways to expand new
Speaker 1: markets that they can they can get into new new uses,
Speaker 1: and certainly when you have uses that in and off
Speaker 1: themselves are advantageous things like making payments, personal security um
Speaker 1: and just the you know, the broad broadly speaking, the
Speaker 1: idea of finding missing persons and catching criminals. Yeah, I
Speaker 1: mean that makes all those things in isolation make they
Speaker 1: make a good case, right, And that's actually the first
Speaker 1: to main argument. So the authors of this piece outline
Speaker 1: three major arguments that are used by the advocates of
Speaker 1: facial recognition, and the first one is exactly that is that, well,
Speaker 1: there might be some harms, but they argue the potential
Speaker 1: benefits outweigh the harms. Uh. You know, again, think about
Speaker 1: the benefits to law enforcement alone. Think of all the
Speaker 1: violent criminals that could be called, Think of all the
Speaker 1: missing persons that could be found. Uh. And on top
Speaker 1: of that, think about some of the obvious consumer demand
Speaker 1: that there would be for public versions of the software.
Speaker 1: I mean, it's the kind of thing that, like, we
Speaker 1: would be terrified about the thought of people using it
Speaker 1: on us, but might be really excited to use it
Speaker 1: on others. You know. It's just like this, uh, this
Speaker 1: basic failure to like reverse the situation and apply the
Speaker 1: same rules you would want applied to yourself to other people.
Speaker 1: So under this mindset, instead of being banned, the people
Speaker 1: who say, you know, the benefits outweigh the harms, they
Speaker 1: would probably say, well, facial recognition should be lightly regulated.
Speaker 1: You know, maybe we could require transparency and make sure
Speaker 1: consumers are aware when face data is being harvested for
Speaker 1: recognition purposes. You know you can't do it surreptitiously. Uh
Speaker 1: And the authors here disagree, they write, quote, notice and
Speaker 1: choice has been an abysmal failure. Social media companies, airlines,
Speaker 1: and retailers overhyped the short term benefits of facial recognition
Speaker 1: while using unreadable privacy policies and vague disclaimers that make
Speaker 1: it hard to understand how the technology endangers users privacies
Speaker 1: and freedom. Uh So, you know, like, does anybody actually
Speaker 1: read the end user license Agreement? Of course not No,
Speaker 1: I mean, like, even if we did, would some vague
Speaker 1: legal phrasing about ownership of face data cause us to
Speaker 1: actually forego participation in technological trends that everybody around us
Speaker 1: is adopting. I mean again, of course not, Like, even
Speaker 1: if the harms vastly outweigh the benefits. The benefits are
Speaker 1: immediate and concrete, and the harms are long term and abstract.
Speaker 1: Exactly the kinds of cases where we are so bad
Speaker 1: at like making informed decisions, like would you like a
Speaker 1: slice of pizza right now? Just be aware that you
Speaker 1: know it may compromise the integrity of your identity in
Speaker 1: some way that's difficult to picture. Now. The next counter
Speaker 1: argument they explore is that the idea that strong fears
Speaker 1: about new technologies are overreactions, and we've you know, we've
Speaker 1: looked at lots of ways that this can absolutely happen.
Speaker 1: We've discussed it on this show and also a lot
Speaker 1: on invention. Think about the panic about a racer. Just
Speaker 1: remember that, yes, yes, definitely, the idea that oh, racers
Speaker 1: are are exist. All my writings will be a race
Speaker 1: or remember some of the panics that came with the
Speaker 1: advent of photography, right, yeah, and just the general idea
Speaker 1: of a future shock, you know, the idea that that
Speaker 1: rapidly advancing technology is overwhelming and uh um yeah, that
Speaker 1: is a subject unto itself. Yeah, So this argument would
Speaker 1: be that things sometimes just seems scary and provoke shock
Speaker 1: because they're new, but once we get used to it,
Speaker 1: it's great. Uh. The authors disagree. They do not think
Speaker 1: this is the case with facial recognition. They argue that
Speaker 1: the backlash is not just hyperventilating about something that's unfamiliar.
Speaker 1: In very concrete ways, facial recognition does have a unique
Speaker 1: power to create a world with pervasive automated surveillance in
Speaker 1: a way that's disempowering to an individuals in almost uncountable ways,
Speaker 1: they write, quote, big companies, government agencies, and even your
Speaker 1: next door neighbors will seek to deploy it in more places.
Speaker 1: They'll want to identify and track you. They'll want to
Speaker 1: categorize your emotions and identity. They will want to infer
Speaker 1: where you might shop, protest, or work, and use that
Speaker 1: information to control and manipulate you, to deprive you of opportunities.
Speaker 1: It's likely that the technology will be used to police
Speaker 1: social norms. People who skip church or jaywalk will be
Speaker 1: noticed and potentially ostracized. And you'd better start practicing your
Speaker 1: most convincing facial expressions otherwise during your next job interview,
Speaker 1: a computer could code you as a liar or a malcontent. Now,
Speaker 1: remember in the last episode when we talked about like
Speaker 1: these services that are being sold as like identifying people's
Speaker 1: emotions through facial recognition, which there is a major study
Speaker 1: that really undercut that and said these things are not
Speaker 1: very accurate, but that doesn't mean they're not going to
Speaker 1: be used. Right, and in terms of policing social norms,
Speaker 1: again go back to the pajamas because it's it's slightly
Speaker 1: hilarious as the pajama cases. It is also frightening because
Speaker 1: it is a firm example of facial recognition technology being
Speaker 1: used to police social norms. Yes. Uh. And then the
Speaker 1: third counter argument they look at is basically the argument
Speaker 1: that Bruce Schneyer was making when we referenced his article earlier. Uh.
Speaker 1: It's that facial recognition technology is just one branch of
Speaker 1: a broader privacy and civil liberty debate. Um, and we
Speaker 1: we need to focus on all surveillance, not just this
Speaker 1: particular technology. So how about you know, things that identify
Speaker 1: you by your gate, the way you move, or about
Speaker 1: retinal scanning or brain scanning or anything like that. And
Speaker 1: I'd say about this one, well, you know, it's true
Speaker 1: like other technologies could represent the kind of threat to
Speaker 1: privacy and freedom that facial recognition presents. And some of
Speaker 1: those are scary and awful too. You know, we're not
Speaker 1: saying like don't, um, don't ban gate recognition scanning, or
Speaker 1: you know, don't scan my brain while I'm at a restaurant. Yeah, exactly.
Speaker 1: I mean I would say facial recognition is going special
Speaker 1: attention because it's already here, like people are selling these programs. Uh.
Speaker 1: And another thing is that, you know, different technologies are
Speaker 1: slightly different, and they require different regulatory schemes. You know,
Speaker 1: the authors here point out that the law singles out automobile, spyware,
Speaker 1: medical devices, and a bunch of other different kinds of
Speaker 1: technologies with their own laws and rules. They're not all
Speaker 1: covered under one type of law. We've got individual agencies
Speaker 1: for like airplanes and for phones and stuff. But then
Speaker 1: another thing is that faces are central to our identities.
Speaker 1: In the first episode, you know, we joked about going
Speaker 1: around constantly changing masks, but is this socially feasible? I mean,
Speaker 1: be realistic, Like we need to see each other's faces
Speaker 1: in order to see each other. Seeing faces is the
Speaker 1: soul of human life. Yeah. One of the one of
Speaker 1: the whole aspects of the whole bummer situation that we've
Speaker 1: discussed involving social media and just electronic culture in general,
Speaker 1: is that we don't see each other's faces, not not
Speaker 1: the living face, not the face that shows expression and humanity.
Speaker 1: We just see the manufactured faces. And uh, we don't
Speaker 1: want to live in a world of physically manufactured faces
Speaker 1: because we've could created a technology and allowed it to
Speaker 1: get out of hand. Yeah. Uh. And so I think
Speaker 1: often with issues like this and and something you run
Speaker 1: into with a lot of these counter arguments that these
Speaker 1: things that are in favor of facial recognition, or we
Speaker 1: argue in ways that fall into a trap of talking
Speaker 1: about just what's possible or what's technically true, rather than
Speaker 1: what's realistic and what's practical. Like could you protect people
Speaker 1: with opt in methods where they have to sign a
Speaker 1: ula disclosing that they've surrendered their privacy? I mean in theory, yes,
Speaker 1: but in practice we know we just know this doesn't work,
Speaker 1: you know, we all just click. I agree, And again,
Speaker 1: the benefits are immediate and concrete, and the harms are
Speaker 1: long term and abstract. Yeah, I want to see what
Speaker 1: I'll look like as an old man on my iPhone
Speaker 1: right now. I don't care where this software, where this
Speaker 1: app is coming from, and where of this facial data
Speaker 1: might or might not be going. Yeah, and I'm obviously
Speaker 1: i'd advise people not to do that. But if you
Speaker 1: react to that with the mentality of will you signed
Speaker 1: the contract, You've got nothing to complain about. That just
Speaker 1: doesn't take the experience of human life seriously, right, And
Speaker 1: it's nothing one of these cases which to to um
Speaker 1: to summon the words of William Gibson of technology making
Speaker 1: packs with the devil possible, things that were only the
Speaker 1: wild imaginings of of people in the past we bring
Speaker 1: to life with technology making AI demons, making it possible
Speaker 1: to to to sell your face to some faceless entity.
Speaker 1: I think that's exactly right. I mean, this is like,
Speaker 1: this is somehow a case of like a horrible fantasy
Speaker 1: becoming reality. Um. And you know, and this could be
Speaker 1: the case with a lot of things. So it's not
Speaker 1: just the u LA agreements about you know, about people
Speaker 1: arguing about like what's technically possible without just acknowledging what's realistic.
Speaker 1: What are people really going to do with their lives?
Speaker 1: Same thing is, you know, you could argue, well, if
Speaker 1: you don't like it, you could go around with masks
Speaker 1: on or something in order to opt out. I mean
Speaker 1: you could try that. But are people actually practically gonna
Speaker 1: want to live that way? Yeah? I mean not only
Speaker 1: living with a mask, but living among the masks, because God,
Speaker 1: we've talked about this before, just the psychology of of
Speaker 1: masks and what happens when you wear one, and the
Speaker 1: group think of mask wearing and and generally the idea
Speaker 1: of encountering a bunch of people wearing masks or the
Speaker 1: same mask is a frightening a proposition and they are
Speaker 1: countless historical examples of that. Yeah. So the authors here conclude, quote,
Speaker 1: we support a wide ranging ban on this powerful, powerful technology,
Speaker 1: but even limited prohibitions on its use in police body cams,
Speaker 1: d m V databases, public housing, and schools would be
Speaker 1: an important start. And they say the public is ready
Speaker 1: for this, and the actions by San Francisco, Somerville, Berkeley,
Speaker 1: and Oakland show it. Our society does not have to
Speaker 1: allow the spread of new technology that endangers our privacy.
Speaker 1: And I guess it, just speaking for myself, I I
Speaker 1: am highly convinced by this point of view. I mean, like,
Speaker 1: I think, if you ultimately in the future were really
Speaker 1: confident that you wanted to change your mind and move
Speaker 1: forward with facial recognition technology, you could lift a ban.
Speaker 1: But like, you can't undo it once the technology is
Speaker 1: out there and and it's coming really fast. Absolutely, yeah.
Speaker 1: I mean on the other side is yes, so you
Speaker 1: can think of various situations where you put bands in
Speaker 1: place and then something bad happened, something summone's up a
Speaker 1: great deal of fear in a nation and allows us
Speaker 1: to slide back into this situation and say, give up
Speaker 1: various privacies and rights in the name of feeling a
Speaker 1: little less afraid. But that doesn't mean it's not worth
Speaker 1: fighting for now, you know, before the fear, because imagine
Speaker 1: how much further we would sink into into the fear. Uh,
Speaker 1: you know if we already had all these technologies in place,
Speaker 1: eroting and taking aware of freedoms. This is a topic
Speaker 1: I think it's worth spreading the word about. I mean, like,
Speaker 1: this is something a lot of people just probably aren't
Speaker 1: thinking about at all, and it's coming really fast. So uh,
Speaker 1: it's worth raising the awareness. Yeah, yeah, worth uh worth
Speaker 1: recording three episodes that I must admit are not fun episodes.
Speaker 1: The middle one was a lot of fun. I thought, okay,
Speaker 1: the middle one. The middle one was more fun because
Speaker 1: we're just talking about the basic biological facial recognition and
Speaker 1: parts of this one we're at least entertaining that that
Speaker 1: Wired article was was a very fun but also you know,
Speaker 1: disturbing read. Uh, but it is. This is a troubling topic.
Speaker 1: I am. I am more troubled for having researched it
Speaker 1: and recorded it. But I think we should be troubled
Speaker 1: by things like this, and it gives us the It
Speaker 1: gives us the space from which to to act, uh,
Speaker 1: you know, hopefully just by spreading the word about it
Speaker 1: and uh and you know, getting to a place where
Speaker 1: we have some of these protections in place for our
Speaker 1: own faces. That's right, don't build the nightmare mask land right,
Speaker 1: save your face, Do not go gentle into that good
Speaker 1: night right rage, rage against the scanning of the face. Yes,
Speaker 1: I will stress though. This was the third of a
Speaker 1: three part series, So if you didn't listen to the
Speaker 1: other two, go back and listen to them because it
Speaker 1: will help fill in all the pieces for you totally.
Speaker 1: But also it's worth pointing out that this is such
Speaker 1: a bleeding edge issue that even though you'll be listening
Speaker 1: to this episode mere days after we recorded it, uh,
Speaker 1: there's just there's just gonna be more and more coverage.
Speaker 1: There's gonna be you know, new movements happening, new new studies,
Speaker 1: sadly new rollouts that are going to meet with controversy.
Speaker 1: So um, just keep that in mind, especially if you
Speaker 1: end up coming back to this episode same months from now.
Speaker 1: We're in the frenzy period right now, all right in
Speaker 1: the meantime, if you want to check out other episodes
Speaker 1: of Stuff to Blow Your Mind, you can find us
Speaker 1: wherever you get your podcasts. If you want to just
Speaker 1: jump on over to the I Heart listing for the show,
Speaker 1: go to stuff to Blow your Mind dot com and
Speaker 1: you will be redirected. But wherever you get the show,
Speaker 1: make sure that you rate, review, and spread the word,
Speaker 1: because that's how we keep the show alive. Hughes, thanks
Speaker 1: as always to our excellent audio producer Seth Nicholas Johnson.
Speaker 1: If you'd like to get in touch with us with
Speaker 1: feedback on this episode or any other, to suggest topic
Speaker 1: for the future, or just to say hello, you can
Speaker 1: email us at contact at stuff to Blow your Mind
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