Speaker 1: Welcome Stuff to Blow your Mind. A production of I
Speaker 1: Heart Radios has to works. Hey, welcome to Stuff to
Speaker 1: Blow your mind. My name is Robert Lamb and I'm
Speaker 1: Joe McCormick, and I want to start off today by
Speaker 1: proposing a scenario for you to imagine. What if your
Speaker 1: body included a personal search bar. I know that sounds weird,
Speaker 1: that might be hard to picture, but just try to
Speaker 1: imagine your body. Your physical body has a digital interface
Speaker 1: that maybe anybody within a hundred feet of you can access.
Speaker 1: What if your physical body included a searchable database of
Speaker 1: pretty much everything you ever did or posted on the Internet.
Speaker 1: So whether you're out at a bar with your friends,
Speaker 1: or you're sitting on the subway on your way to work,
Speaker 1: or you're sitting in your car in traffic, or taking
Speaker 1: part in a protest march, or working out of the gym,
Speaker 1: or you're on a date, whatever, anybody who could see
Speaker 1: you would instantly have the ability to look up your
Speaker 1: personal information. They could find out your name, your resume,
Speaker 1: your contact info, your workplace, home address, maybe find all
Speaker 1: publicly available photos of you that are out there on
Speaker 1: Instagram or whatever. Maybe everything you've ever posted on Twitter,
Speaker 1: or on Facebook or whatever other social media, and more broadly,
Speaker 1: basically just everything you've done on the internet, purchasing history,
Speaker 1: search history, as well as your location history anywhere you've
Speaker 1: physically taken your phone with GPS enabled. I think most
Speaker 1: of us would probably recoil in horror at the idea
Speaker 1: that we would ever lose the ability to be anonymous
Speaker 1: in a public place. But perhaps the horrifying part of
Speaker 1: imagining this scenario is that I think what I'm describing
Speaker 1: is not only fairly plausible in some preliminary ways, this
Speaker 1: is already the case, at least in principle. All the
Speaker 1: foundations and support structure of this horrible hypothetical world are laid,
Speaker 1: and really all that's left to do is just kind
Speaker 1: of tighten the screws on it. And one thing we
Speaker 1: know about this world is that there there's no shortage
Speaker 1: of would be screw tighteners out there, Nope, especially if
Speaker 1: you can make some money by tightening screws, which you
Speaker 1: very often can. Uh So, yeah, we we should already
Speaker 1: know that there is very little privacy in the modern
Speaker 1: technology sphere. Our phones, our social media accounts are advertiser
Speaker 1: i d s, which are used to track us across
Speaker 1: the Internet. These sources are already used to create profiles
Speaker 1: in which the disparate types of our personal identifying information
Speaker 1: get correlated with each other and used to serve us
Speaker 1: ads or manipulate us on social media. But the leap
Speaker 1: into physical space, where all of our information is easily
Speaker 1: linked now to our physical body wherever we are, whether
Speaker 1: we like it or not, is the frontier that's currently
Speaker 1: being pushed and at a very rapid pace. Now there
Speaker 1: are multiple ways to make this link, of course, you know,
Speaker 1: so linking our digital profiles and all the the associated
Speaker 1: data with our physical bodies. A very simple one would
Speaker 1: be with the tracking devices that pretty much all of
Speaker 1: us carry with us at all times, the mac addresses
Speaker 1: on our phones and our mobile devices. But one of
Speaker 1: the most powerful developing techniques is for the technosphere to
Speaker 1: recognize you in physical space the same way that your
Speaker 1: friends and your family do by your face. Now, one
Speaker 1: thing to keep in mind about this is that, of course,
Speaker 1: just because say you're thermostat in your house can recognize
Speaker 1: your face, that is not necessarily in and of itself
Speaker 1: a bad thing. In some cases that could be very helpful,
Speaker 1: or even it could be seen as a way to uh,
Speaker 1: you know, to safeguard the temperature of your home, uh,
Speaker 1: that sort of thing, like you could have some sort
Speaker 1: of a security feature. And as we as we proceed
Speaker 1: through these episodes, we're going to try and keep that
Speaker 1: in mind. We're gonna we're gonna try not to color
Speaker 1: the technology as inherently vile or inherently uh prone to misuse.
Speaker 1: But the story of technology is that it is both
Speaker 1: light and dark. Well, yeah, and that I think there
Speaker 1: is a big difference between something being inherently vile versus
Speaker 1: prone to misuse. There are a lot of things that
Speaker 1: are that are created with perfectly good intentions in mind.
Speaker 1: You can think of lots of great reasons to do
Speaker 1: most of the worst technological stuff imaginable. You know, we've
Speaker 1: talked before on the show. We make no secret our
Speaker 1: distaste for a lot of things about social media. But
Speaker 1: of course, you know, you can see the good side
Speaker 1: of something like Facebook, Yeah, or very broadly, you can
Speaker 1: think of something like banking. Banking in many respects allows
Speaker 1: humans to do amazing things things they wouldn't or otherwise
Speaker 1: be able to do. Uh, to buy larger uh, you know,
Speaker 1: to buy a property, you know, to buy a vehicle,
Speaker 1: to start a business, start a business, and so forth.
Speaker 1: But at the same time, very terrible things have been
Speaker 1: done and are still being done under the broad tent
Speaker 1: of banking. Yeah, and we can help better protect ourselves
Speaker 1: from those outcomes by better understanding banking so that we
Speaker 1: can regulate it properly, which doesn't always get done, but
Speaker 1: you know that that, at least in theory, that is
Speaker 1: the way to protect yourself. And so today we're gonna
Speaker 1: be taking that same point of view to a series
Speaker 1: of episodes about facial recognition science and technology. And this
Speaker 1: is a subject I've wanted to talk about for a
Speaker 1: while because obviously this is an issue of increasing significance today.
Speaker 1: But actually, just after we landed on this topic, I
Speaker 1: came across a brand new story in The New York
Speaker 1: Times that serves as a really good anchor on why
Speaker 1: this issue is incredibly relevant today. And this article is
Speaker 1: called The Secretive Company that Might end Privacy as we
Speaker 1: know it by cashmir Hill, published in the New York
Speaker 1: Times January. Yeah. It uh. It concerns a small technology
Speaker 1: company called clear View AI, which just the title of
Speaker 1: that it sounds like it could be fine, right, sounds
Speaker 1: very transparent, doesn't clear View AI, But of course this
Speaker 1: is a facial recognition based artificial intelligence company. Uh So,
Speaker 1: so what is clear View in their own words? What
Speaker 1: do they say about themselves? Uh? To read from their
Speaker 1: marketing materials quote. Clear View is a new research tool
Speaker 1: used by law enforcement agencies to identify perpetrators and victims
Speaker 1: of crimes. Clear Views technology has helped law enforcement track
Speaker 1: down hundreds of at large criminals, including pedophiles, terrorists, and
Speaker 1: sex traffickers. It is also used to help exonerate the
Speaker 1: innocent and identify the victims of crimes, including child sex
Speaker 1: abuse and financial fraud. Now, on the surface of things,
Speaker 1: that sounds absolutely air tight, right. It describes the technology
Speaker 1: that is used by the appropriate agencies to protect the
Speaker 1: innocent and to go after the guilty. But then again,
Speaker 1: that can be used to sell a lot of things
Speaker 1: in the world, of course. So what they advertise is
Speaker 1: that this app helps law enforcement identify perpetrators and protect
Speaker 1: victims of crime, and of course, in some cases that
Speaker 1: may very well be true. Obviously, it would be pointless
Speaker 1: to deny that facial recognition technology the ability to take
Speaker 1: a picture of somebody's face and then find out tons
Speaker 1: of stuff about who they are and how you can
Speaker 1: find them. You know, be pointless to deny that in
Speaker 1: many cases that would be useful and beneficial to law enforcement.
Speaker 1: But it is also, of course just so easy to
Speaker 1: see how a tool like this could be terrible, both
Speaker 1: in its successes and in its failures. So, first of all,
Speaker 1: of course, it could fail in catastrophic ways, say with
Speaker 1: like false matches when police are looking for a perpetrator,
Speaker 1: but of course you know that's something that can happen
Speaker 1: with human witnesses too, right, But then it could also
Speaker 1: be used effectively if it correctly identifies people too amazingly
Speaker 1: insidious ends. So how does it work. It's actually it
Speaker 1: sounds pretty simple in terms of its user interface. Specifically,
Speaker 1: what this tool does is it matches an input photo
Speaker 1: of a face with a huge database of existing photos
Speaker 1: scraped from the internet, and then it will provide links
Speaker 1: to the places that those images from the internet were
Speaker 1: originally found. So very simple example, I take a photo
Speaker 1: of you, and then I feed it into the tool,
Speaker 1: and then it comes back with other photos of you
Speaker 1: and links to the places where those photos were found,
Speaker 1: maybe your Facebook page, a YouTube video, you're in and
Speaker 1: so forth, and so when it works, it will provide
Speaker 1: a direct link between your anonymous face from a picture
Speaker 1: and the digital locations where all of your personal information
Speaker 1: may be logged online. Now, we're not going to look
Speaker 1: too far under the hood of exactly how the you know,
Speaker 1: the underlying technology of this sort of thing works right now. Uh,
Speaker 1: there are a number of ways that facial recognition algorithms
Speaker 1: can actually work, but a very common way is using
Speaker 1: neural networks. Yes, and uh and and for this For
Speaker 1: for like a nice succinct description of how this works,
Speaker 1: I'd like to refer to Max tag marks most recent
Speaker 1: book Life three point oh, which deals at length with
Speaker 1: AI and the potential the threats posed by by AI
Speaker 1: in the future. It's a it's a wonderful book. But
Speaker 1: in this this one section, he's just summarizing how this
Speaker 1: kind of facial recognition works. Uh. And he writes, uh
Speaker 1: that neural networks have been quote trained to input numbers
Speaker 1: representing the probability that the image depicts various people. Here,
Speaker 1: each artificial neuron and on on an illustration they're depicted
Speaker 1: as circles. Computes a weighted sum of the number sent
Speaker 1: into it via connections or lines in this image from above,
Speaker 1: applies a simple function and passes the results downward each
Speaker 1: subsequent layer of computing. Higher level features. Typical face recognition
Speaker 1: network contain hundreds of thousands of urons. Uh. The figure
Speaker 1: shows merely a handful for clarity, so uh. In the
Speaker 1: the visual representation the tech mark includes here you see
Speaker 1: all these circles and interconnected lines representing how the you know,
Speaker 1: the neural network is functioning. But then it begins, then
Speaker 1: we apply this to the facial features. It starts with
Speaker 1: with sort of general features and sort of blurry shapes,
Speaker 1: and then to more specific features, and then tying those
Speaker 1: features together and then eventually getting to an output probability
Speaker 1: of actual facial matches. Yeah. And as with many other
Speaker 1: neural networks that are trained on large data sets, to
Speaker 1: you know, match values together or produce an output, why
Speaker 1: given input x uh you know, given a training method,
Speaker 1: there will often so. The way these things are trained
Speaker 1: is that you you know, you feed them a lot
Speaker 1: of examples of the kind of output you want, and
Speaker 1: slowly they refine their own rules internally. The rules that
Speaker 1: happened at each of these layers of neurons to manipulate
Speaker 1: numbers and values as they pass through the neural network
Speaker 1: in order to give the output that closely matches whatever
Speaker 1: you've trained it to come up with. But that that
Speaker 1: means that like you can train potentially an effective neural
Speaker 1: network without yourself really understanding very well exactly what's happening
Speaker 1: at each layer throughout throughout the network. Now, I think
Speaker 1: it is possible to like sort of get in there
Speaker 1: and try to dig into it and and see what's
Speaker 1: going on, if you've really got the time and expertise.
Speaker 1: But but it can be relatively opaque as far as
Speaker 1: computer programs go. It doesn't necessarily work like a normal
Speaker 1: computer program has lines of code that any programmer who
Speaker 1: knows the language can read through and figure out what's
Speaker 1: going on easily. But to come back to the cash
Speaker 1: Mirror Hill article and UH clear view AI uh. One
Speaker 1: thing that's important to point out is that this is
Speaker 1: by no means the first facial recognition app or tool,
Speaker 1: nor is it the first used by law enforcement. It's
Speaker 1: particular value that the thing that it's doing that's somewhat
Speaker 1: new is in its database of images, which again have
Speaker 1: been scraped from organic sources like Facebook and YouTube. Previously,
Speaker 1: law enforcement facial recognition matching programs were often weaker and
Speaker 1: more limited to smaller databases of government photos, say mug
Speaker 1: shots or driver's licenses. And of course there's the potential that,
Speaker 1: you know, smaller training or matching material will make any
Speaker 1: machine learning process weaker at at coming up with the
Speaker 1: results you want. Yeah. All this kind of ties in
Speaker 1: with what I often think of its kind of like
Speaker 1: this title pool illusion of the Internet, that feeling that
Speaker 1: a lot of us had I think still have sometimes
Speaker 1: but also especially early on, this feeling that we were
Speaker 1: engaging in something segmented from the general population. You know.
Speaker 1: But the thing about tide pools, of course, is that
Speaker 1: eventually the tide rolls in and then you realize that
Speaker 1: you're actually connected to the wider internet. Uh so you know,
Speaker 1: not just your friends or your family or your fandom,
Speaker 1: but also uh, you know, law enforcement, criminals, politics, all
Speaker 1: just churning around in the same grim ocean of numbing obscenity.
Speaker 1: I think that's a really excellent metaphor. Yeah, there is
Speaker 1: some somehow the Internet was very easily able to create
Speaker 1: a sense of isolated, walled off gardens that we were
Speaker 1: living in which were at the time totally public you know, um,
Speaker 1: early days of various social networking sites, fan forums, all
Speaker 1: that kind of thing, you know, whatever it was that
Speaker 1: gave people a sense that they were in a little
Speaker 1: private space, you know, that their little corner, their little room.
Speaker 1: But of course it's the Internet. What's happening there is public,
Speaker 1: and the consumers of what's happening there may be completely
Speaker 1: invisible to you. Right. So in this case, with the
Speaker 1: previous models of facial identification, the data sets they were
Speaker 1: depending on, we're basically title pools, like here's the title
Speaker 1: pool of of mug shots. Here's the driver's license, uh,
Speaker 1: title pool, and that's what we're feeding on. But basically
Speaker 1: clear View comes around, and this is a company that
Speaker 1: is saying, well, let's just use the whole ocean. What's
Speaker 1: stopping is from using the whole ocean. So this is
Speaker 1: a company using the assets of various social media and
Speaker 1: in general visual media companies on the web to do
Speaker 1: the sorts of things that those companies have been loath
Speaker 1: to do, or if at least been you know, publicly
Speaker 1: opposed to doing. Because technically, as as pointed out in
Speaker 1: Hills New York Times, article. Um, you know, there is
Speaker 1: there is an argument that what they're doing here, what
Speaker 1: what this company, in any company that's that's engaging in
Speaker 1: this kind of like broad sampling, that they may be
Speaker 1: violating the terms of service for these various websites. Sure, yeah,
Speaker 1: automatically scraping imagery and data from Facebook. Say, I think
Speaker 1: there was at least the allegation that that could be
Speaker 1: a violation of Facebook's terms of service, but it didn't
Speaker 1: really seem to bother the clear view people right right,
Speaker 1: And I think when Hill reached out to Facebook representative,
Speaker 1: they said, well, we may look into that, and so
Speaker 1: it's kind of an open question. But you know, a
Speaker 1: lot of this also comes down to something we discussed
Speaker 1: in our our look at during uh Lanier's ten arguments
Speaker 1: for deleting your social media accounts right now, because it
Speaker 1: concerns your data, data that you have, in all likelihood
Speaker 1: given away to companies like Facebook, Twitter and others simply
Speaker 1: to be a part of the interconnectedness that they sold us. Now,
Speaker 1: a lot of the time when we're discussing such a data,
Speaker 1: we're discussing behavioral information, right your likes, your dislikes, your
Speaker 1: arousal patterns concerning posts and advertisements. But in addition to this,
Speaker 1: you also sold the devil your face. Yeah, I mean
Speaker 1: he he's the devil in this case is simply promising
Speaker 1: not to do anything unbecoming with your face. But but
Speaker 1: Hell is highly populated. Even if you have good reason
Speaker 1: to trust this particular devil to which you've already entrusted
Speaker 1: your face and perhaps the faces of your family members,
Speaker 1: your loved ones, deceased loved ones, your children. Uh, you know,
Speaker 1: there are countless others that they will make no such promises,
Speaker 1: steal your face right off your head. Yeah, and and
Speaker 1: they may have little concern for, you know, the values
Speaker 1: that were in place during the initial purchase. It's something
Speaker 1: that I think about coming up again and again with
Speaker 1: sharing data on the internet. So you share your data
Speaker 1: with a company, and you maybe trust that company today
Speaker 1: to protect your data. But what if so say that
Speaker 1: company hangs onto your data for a while and then
Speaker 1: they get new management that you don't trust as much,
Speaker 1: but they've got it, Uh, you can't get it back.
Speaker 1: Or maybe they have a security breach and somebody just
Speaker 1: happens to steal your data from them. It's like, well,
Speaker 1: you would have trusted the company maybe, but now somebody
Speaker 1: else has got it, and you can see how in
Speaker 1: a world like that, Uh, it could start to feel
Speaker 1: like maybe hopeless or pointless or futile for people who
Speaker 1: say are in a position to make money off of
Speaker 1: not being very careful about people's privacy. Uh. You know,
Speaker 1: it's like, you know, what's the point. Everything eventually gets
Speaker 1: out there anyway. And this kind of point of view
Speaker 1: was sort of articulated by some of the people quoted
Speaker 1: in Hill's article. For example, there's a figure named David
Speaker 1: Scalzo who was an early investor and investor in this company,
Speaker 1: Clear View AI uh, and Scalzo is quoted in the
Speaker 1: article saying, I've come to the conclusion that because information
Speaker 1: constantly increases, there's never going to be privacy. Laws have
Speaker 1: to determine what's legal, but you can't ban technology. Sure
Speaker 1: that might lead to a dystopian future or something, but
Speaker 1: you can't ban it. So, you know, this is it
Speaker 1: at once one of those statements that seems very pragmatic
Speaker 1: but also entirely self serving, because true the story of
Speaker 1: technolo alogy is that its advanced cannot really be stopped.
Speaker 1: You have to think ahead as best we can and
Speaker 1: prepare our laws and our moral code to dealing with
Speaker 1: emerging technologies. We've talked about this before, for instance, in
Speaker 1: as far as genetic technology is concerned, But this particular
Speaker 1: quote also sounds a lot like, Hey, it's gonna happen
Speaker 1: either way, so I might as well be the person
Speaker 1: to make some money off of it. It totally agree,
Speaker 1: I mean, I agree that it is difficult to stop
Speaker 1: technological progress. That if you know, if one group of
Speaker 1: people isn't working on it, maybe a less ethical group
Speaker 1: of people might be somewhere, But that's not an excuse
Speaker 1: to be the person who creates the synthetic supervirus that
Speaker 1: you know who like genetically engineers, captain trips flu or whatever. Like. Also,
Speaker 1: you could use this logic about almost any bad thing.
Speaker 1: It's kind of like saying, yeah, you know, there's no
Speaker 1: way to totally eliminate pollution. Some people are always going
Speaker 1: to find a way to pollute and harm the environment,
Speaker 1: so you might as well just go hog while just
Speaker 1: dump it all. Like. So, it's true that you can't
Speaker 1: stop everything harmful to the environment with regulation, but you
Speaker 1: can really slow it down. You can present major obstacles
Speaker 1: to the worst types of offenses. And likewise, I think
Speaker 1: it would be very difficult to completely stop the advancing
Speaker 1: capabilities of AI, including facial recognition, but you can certainly
Speaker 1: slow it. You can certainly limit its potentially harmful uses
Speaker 1: by banning those uses and punishing offenders. Now, on the
Speaker 1: other hand, you could think, well, yeah, you could do that,
Speaker 1: but this would be so helpful to law enforcement in
Speaker 1: some cases. You know, so would the ability to search
Speaker 1: any house you wanted without a warrant, right right, I mean,
Speaker 1: this is the same argument that has often been part
Speaker 1: of the reasoning for enhanced interrogation and torture, is that, well,
Speaker 1: it can help us get the bad guys, it can
Speaker 1: help us in this situation. And then also in all
Speaker 1: of these arguments are is also the the idea that well,
Speaker 1: if you have nothing to hide, if you are truly
Speaker 1: a good and supportive member of society, then what if
Speaker 1: you have to what do you have to worry about? Anyway?
Speaker 1: But but it kind of comes down to the data issue. Well,
Speaker 1: you trust the person who has your data now, but
Speaker 1: do you trust the person who have your data tomorrow?
Speaker 1: You trust the government of today, but governments change, yeah,
Speaker 1: I mean, and No, nobody actually in practice believes this
Speaker 1: what do you have to hide argument? It's just something
Speaker 1: you would say. I mean, like, if anybody ever says that,
Speaker 1: just immediately demand them to give you their email password.
Speaker 1: Me like, yeah, just let me read all your email.
Speaker 1: I mean, what's the problem, wild inspector? Right, I think
Speaker 1: you'll find everything in order. Uh. But yeah, so, I
Speaker 1: mean obviously, society has often decide to regulate police power
Speaker 1: in ways that I mean that are truly inconvenient to
Speaker 1: law enforcement, because they decide that in some cases there
Speaker 1: are types of privacy and other civil liberties that matter
Speaker 1: more than prosecuting offenses at the maximum efficiency. Yeah. Alright, Well,
Speaker 1: on that note, we're going to take a quick break. Okay,
Speaker 1: we're back. So again, we're in the middle of this
Speaker 1: first episode of this exploration we're doing of of facial
Speaker 1: recognition science and technology, and we've been talking about this
Speaker 1: New York Times article that just came out last week
Speaker 1: by Cashmere Hill about a facial recognition technology company called
Speaker 1: clear View AI. Now, broadly speaking, the two big advantages
Speaker 1: of clear View AI are that it first of all,
Speaker 1: pulls from an extensive database of images. So we're talking
Speaker 1: three billion photos in a database versus the four hundred
Speaker 1: and eleven million searchable through the FBI's database. These stats
Speaker 1: according to clear View marketing materials reviewed by Cashmere here
Speaker 1: Hill for that New York Times article. And then secondly,
Speaker 1: it boasts a robust enough facial recognition engine built up
Speaker 1: from academic work by others on artificial intelligence, image recognition
Speaker 1: and machine learning. UM that it and it and it
Speaker 1: it does not require high quality or complete facial images
Speaker 1: to produce matches. So like the you know, the I
Speaker 1: guess the ideal example would be you could have somebody
Speaker 1: going into a bank and robbing it with their face
Speaker 1: partially covered, and then this would potentially be able to
Speaker 1: match that partial face to a full face and say,
Speaker 1: a Facebook profile, at least according to what the company
Speaker 1: and some of its satisfied customers in law enforcement have
Speaker 1: been alleging. Yeah. Like one example of a successful match
Speaker 1: that Hill mentions h is um matching an individual to
Speaker 1: a face in a mirror in someone else's gym photo.
Speaker 1: What yeah, and uh, you know, details of the you know,
Speaker 1: presumed guilt or innocence of that particular individual aside, I
Speaker 1: think this is notable in that gyms are often considered
Speaker 1: to be photo taboo places, and it's certainly as far
Speaker 1: as like other people working out in the gym go.
Speaker 1: You know, this is I'm not, you know, an expert
Speaker 1: on jim etiquette, but it is my understanding that you
Speaker 1: shouldn't even you know, accidentally photographed someone else the gym.
Speaker 1: But obviously it does happen. Uh, it just it just
Speaker 1: you should not have your phone out snap and picks
Speaker 1: at the gym unless it's unless it's your private gym
Speaker 1: and you're the only person there, yeah, or unless it
Speaker 1: catches bad guys, because what do you have to hide. Um.
Speaker 1: That being said, the people at the company do admit
Speaker 1: that of course, like you know it, it still has flaws.
Speaker 1: There's still things that can't do. Yeah, Like, for instance,
Speaker 1: a lot of it is leaning on eye level photos,
Speaker 1: the kind of photos that you see and say a
Speaker 1: linked in profile photo, as opposed to the sort of
Speaker 1: ceiling level security camera footage that it is often involved
Speaker 1: in these scenarios, right right, uh yeah, I mean, And
Speaker 1: it's so it's running into the same kind of problems
Speaker 1: that we could talk about this later in the episode
Speaker 1: that human beings sometimes have with less familiar faces. I mean,
Speaker 1: this is a A known thing about human face perception
Speaker 1: and facial recognition within the brain is that, uh, we
Speaker 1: are much better at recognizing very familiar faces under unfair
Speaker 1: ruble conditions, like a partial face, a face it a
Speaker 1: weird angle, facing bad lighting. We can do that a
Speaker 1: lot better if it's a familiar face. Then if it's
Speaker 1: a relatively unfamiliar face, right, I mean, even things like
Speaker 1: our face in a mirror versus our face in a photo.
Speaker 1: You know, things like that can be distorting or if
Speaker 1: you or or more more directly, I find it with
Speaker 1: someone else's face reflected in a mirror. I'm definitely not
Speaker 1: used to seeing that, and that'll throw me off sometimes.
Speaker 1: Do you ever do the inversion test? This is another
Speaker 1: weird quirk of of the brain, trying to see if
Speaker 1: you recognize photos of people's heads upside down. I know
Speaker 1: we've talked about that before in the podcast, but I
Speaker 1: haven't really put it to the test in my own life.
Speaker 1: There's a This is just a total side note. There's
Speaker 1: a very funny thing known as the Thatcher effect. It
Speaker 1: has to do with um, the fact that so if
Speaker 1: you look at somebody's head upside down, but with their
Speaker 1: eyes right side up. A lot of times people look
Speaker 1: at that and they don't even notice anything's wrong with
Speaker 1: the photo. So, like, the head is upside down, but
Speaker 1: the eyebrows are like over the eyes because the eyes
Speaker 1: are still in the correct orientation. Um. But then if
Speaker 1: you flip that whole thing to where the head is
Speaker 1: right side up but the eyes are upside down, it
Speaker 1: looks unbelievably grotesque, Like you will burst out, you'll make
Speaker 1: noise when you see it. Look it up. That's your effect.
Speaker 1: But anyway, back to the story about clear View. So
Speaker 1: the company claims its product finds matches for an input
Speaker 1: photo up to seventy percent of the time, and of
Speaker 1: course Hill notes in the article that we can't be
Speaker 1: sure how often false matches turn up. She quotes Claire Garvey,
Speaker 1: who is a researcher at Georgia University's Center on Privacy
Speaker 1: and Technology, who says, quote, we have no data to
Speaker 1: suggest this tool is accurate. The larger the database, the
Speaker 1: larger the risk of misidentification because of the Doppelganger effect.
Speaker 1: They're talking about a massive database of random people they've
Speaker 1: found on the Internet. The Doppelganger effect being not that
Speaker 1: that vengeful German spirits are are actually invading the database,
Speaker 1: but that there's just a going to be the larger
Speaker 1: the pool of people, the more people they're gonna look
Speaker 1: very much like like others. There's gonna be more similarity
Speaker 1: between an increasing a pool of individuals. Right. But at
Speaker 1: the same time, anecdotal reports from a number of law
Speaker 1: enforcement officers have claimed that this tool was effective at
Speaker 1: identifying real perpetrators from photos alone, and there have been
Speaker 1: plenty of other examples in recent years of supposedly effective
Speaker 1: facial recognition technology provided by other companies that have been
Speaker 1: used to UH to allegedly capture perpetrators of crimes done
Speaker 1: in public places in New York, in the UK, certainly
Speaker 1: you know, in in countries with a very strong surveillance state,
Speaker 1: like in China. And we can come back to more
Speaker 1: about that in later episodes, I think, But as a
Speaker 1: personal anecdote in this reported story, Hill at one point
Speaker 1: has the company's founder use the app on a picture
Speaker 1: of her, and she claims that the tool quote returned
Speaker 1: numerous results dating back a decade, including photos of myself
Speaker 1: that I had never seen before. When I used my
Speaker 1: hand to cover my nose, and the bottom of my face.
Speaker 1: The app still returned seven correct matches for me. So
Speaker 1: I think we can assume that failures, including both false
Speaker 1: and negatives and false positives are surely occurring at some rate.
Speaker 1: But it's also clear that this thing at least works
Speaker 1: some of the time. Yeah, and that's enough to help
Speaker 1: it get picked up by law enforcement. Also, it helps
Speaker 1: that there was a it seems like there's a pretty
Speaker 1: sizeable outreach campaign from the company to to to market
Speaker 1: the technology to law enforcement. Yes, and and we should say,
Speaker 1: I mean, we're not going to hash out everything they
Speaker 1: get into in the article, but the company has arrived
Speaker 1: at law enforcement as their primary customers. Before that, they
Speaker 1: tried to market it in all kinds of ways, including
Speaker 1: you know, for like personal use to like private security things,
Speaker 1: and for like commercial use and even then like political
Speaker 1: opposition research and stuff. Yeah, but this, uh, but you
Speaker 1: can definitely see the advantage to law enforcement here, because
Speaker 1: a detective, for instance, has their disposal a number a
Speaker 1: limited number of talents and tools that are useful and
Speaker 1: attempting to solve a case, and adding this to the
Speaker 1: to the toolkit is no brainer. Because you know, larger
Speaker 1: issues of stability of the platform aside, you know some
Speaker 1: of these legal issues we potential legal issues we're discussing earlier. Um,
Speaker 1: you know, this would be something you could use in
Speaker 1: congress with other techniques. You know, you could say, all right,
Speaker 1: this face seems to match up with this individual. We
Speaker 1: also know that this individual was in the correct you know,
Speaker 1: vicinity at the time. You know, you could lean on
Speaker 1: your other detective tools then to to actually make the case.
Speaker 1: That's not to say there's not potential for misuse here,
Speaker 1: but uh, I'm just saying you can. You can definitely
Speaker 1: see the appeal and how if everything is working perfectly,
Speaker 1: it would be an effective law and forcement tool. Yeah,
Speaker 1: and however effective it actually is, it's clear that this
Speaker 1: and similar tools are increasingly popular with law enforcement in
Speaker 1: countries all over the world. If you're one of those
Speaker 1: people who feels like pumping the brakes on this kind
Speaker 1: of technology, what could actually be done about it? Well,
Speaker 1: Hill quote somebody named al Ghadari, a privacy professor at
Speaker 1: Stanford Law School who says, bluntly quote, absent of very
Speaker 1: strong federal privacy law, we're all screwed. Uh. And He's
Speaker 1: not alone. There are plenty of privacy experts today advocating
Speaker 1: the point of view that facial recognition technology, or at
Speaker 1: least some specific uses of it, aren't just something that
Speaker 1: maybe we should be a little concerned about, there's something
Speaker 1: that needs to be banned outright. Um. For example, Hill
Speaker 1: also quotes somebody named Woodrow Hertzog, who is a professor
Speaker 1: of law and computer science at Northeastern University. Uh and
Speaker 1: hart Sog says, quote, we've relied on industry efforts to
Speaker 1: self police and not embrace such a risky technology, but
Speaker 1: now those damns are breaking because there's so much money
Speaker 1: on the table. I don't see a future where we
Speaker 1: harness the benefits of face recognition technology without the crippling
Speaker 1: abuse of the surveillance that comes with it. The only
Speaker 1: way to stop it is to ban it. So whether
Speaker 1: we should do that, or if so, what form that
Speaker 1: band should take, what whatever, is the best way to
Speaker 1: address it. I think it is at least clear that
Speaker 1: this is a very pressing and uh like time sensitive issue,
Speaker 1: that that is of urgent public concern right now. Yeah, because, again,
Speaker 1: as the author points out, I don't think any of
Speaker 1: us want to live in a world where any stranger
Speaker 1: can you know, surreptitiously take a photo of our face
Speaker 1: and then face searches and get all this data on us.
Speaker 1: You know, I don't want for us to build that
Speaker 1: kind of world for our children, who, more than any
Speaker 1: of us, never had a chance to opt out of
Speaker 1: this uh, this face trade, you know. And that's the
Speaker 1: that's the pressing to think about, you know, because because
Speaker 1: you're not thinking about that when you when you share
Speaker 1: images of your child on on Facebook or Instagram or
Speaker 1: whatever it happens to be. Um, you know, you you
Speaker 1: just you're wanting to celebrate that this person exists at all,
Speaker 1: but you're you're laying the groundwork from like you know,
Speaker 1: age zero Hola onward right, Uh, like this is their
Speaker 1: their digital history. We were talking before we came into
Speaker 1: the studio about like if a person wanted to do
Speaker 1: something about this, what could you do? You can't unpost
Speaker 1: photos of yourself and data that has already been scraped.
Speaker 1: But I wonder if maybe you could try to come
Speaker 1: up the works by constantly just polluting the Internet with
Speaker 1: false pictures of you that are not you. So you're
Speaker 1: like sort of like deep facing enough to where you um,
Speaker 1: you've obscured the visual record of yourself. Maybe yeah, like
Speaker 1: if you, I guess it would then depend on the
Speaker 1: all those new images being taken in and causing enough
Speaker 1: confusion and the identification of you. But I don't know,
Speaker 1: or perhaps like altering your facial appearance with enough regularity
Speaker 1: that there is no concise version of you, or or
Speaker 1: at least making it to wear. Then the the AI
Speaker 1: would have to work a lot harder. It would have
Speaker 1: to have like a broader definition of what you look like,
Speaker 1: to the point that maybe it excuse with maybe it
Speaker 1: enhances the doppelgang or effect, like I'm thinking about you know,
Speaker 1: like you just you know, each day you inject a
Speaker 1: different portion of your face with collagen or something, or
Speaker 1: maybe not collagen, but maybe just saying lead, well, I mean,
Speaker 1: does this lead to if you, I mean, this sounds ridiculous,
Speaker 1: but does this lead to a future where everybody starts
Speaker 1: walking around with a broad array of interchanging masks? Yeah,
Speaker 1: and will and then you have an enhanced um laws
Speaker 1: against the wearing of masks. I mean, masks are outlat
Speaker 1: in a lot of places and and a lot of
Speaker 1: events for for a reason. Yeah, you're not supposed to
Speaker 1: drive a car wearing a mask. That's um, this does
Speaker 1: remind me. There's a there's an excellent show on Hulu,
Speaker 1: a titled Future Man that is a it's it's a
Speaker 1: it's a comedy. It's a satire with a lot of
Speaker 1: nostalgia for various you know, sci fi franchises. But there
Speaker 1: is a scene in one of the episodes where an
Speaker 1: individual knows that a facial recognition system is looking for him,
Speaker 1: so he gets himself beat up first so that his
Speaker 1: face is then all swelled and distorted and it and
Speaker 1: then it cannot make a match for him, and he's
Speaker 1: able to sneak past the guards. I don't think that's
Speaker 1: a sustainable strategy. It's not. And more to the point, yeah,
Speaker 1: we should not even we should not have to even
Speaker 1: entertain that possibility to to hold on to, uh, you know,
Speaker 1: our sense of privacy. Yeah. Now, the fact that we're
Speaker 1: talking about this story in the New York Times about
Speaker 1: clear View is it's just a result of timing. It's
Speaker 1: like this one specific company is not the entirety of
Speaker 1: the end of privacy problem, nor of the facial recognition
Speaker 1: technology landscape in particular, Another company could do the same thing.
Speaker 1: Other copycats I'm sure are already getting in there. Uh,
Speaker 1: it's just one high profile example of of the potential
Speaker 1: already being put to use that that's getting a lot
Speaker 1: of attention in the past couple of weeks. Yeah, in
Speaker 1: part two. You have to read the full article for
Speaker 1: the details. But it's also like their key individuals that
Speaker 1: are notable that are tied into its funding. Yes, there's that,
Speaker 1: and and of course there's the ominous way it ends,
Speaker 1: which is the idea that it will soon probably be
Speaker 1: rolled out not just to law enforcement, but to be
Speaker 1: a publicly usable app, you know, which I guess is
Speaker 1: the sort of the scenario we were describing at the
Speaker 1: beginning of the episode, just having a publicly available personal
Speaker 1: search bar tool. Yeah. Well, well, markin me down for
Speaker 1: being against that. Yes, alright, time to take a quick break,
Speaker 1: but we'll be right back with more than thank alright,
Speaker 1: we're back. So, of course, I also want to make
Speaker 1: the case that facial recognition technology is not necessarily always
Speaker 1: a dangerous or scary or ominous thing. I mean, I
Speaker 1: think there are some uses of it that one could
Speaker 1: quite easily find benevolent or even delightful. And one of
Speaker 1: the ways I was thinking about this was many of
Speaker 1: its developing uses in non human animals, not all because
Speaker 1: some of its uses in non human animals are also
Speaker 1: like kind of horrifying, but some of the ones that
Speaker 1: non human animals are pretty great. I was reading an
Speaker 1: article in New York Magazine from October called Here's a
Speaker 1: list of every animal humans currently monitor using facial recognition
Speaker 1: technology by Mac dagaron As A complete list is probably
Speaker 1: wildly out of date at this point because this was,
Speaker 1: but a few of the entries include things like there's
Speaker 1: a Norwegian fish farming company called Sermac Group as that
Speaker 1: commissioned a system for facial recognition of salmon which would
Speaker 1: use distinctive patterns of spots around the eyes, mouth, and
Speaker 1: gills of individual salmon to build individual digital medical records
Speaker 1: associated with each fish. And this would be for the
Speaker 1: purpose of fighting epidemics of parasitic sea lice primarily. Okay,
Speaker 1: so not merely just presenting it with with the dish
Speaker 1: when you ordered it a restaurant, but I will take
Speaker 1: the baked salmon and please include it's complete medical history. Yes,
Speaker 1: this salmon was named Jeffrey. Here's his Facebook profile, you
Speaker 1: know on on salmon Facebook. Oh, but that comes back.
Speaker 1: Of course, facial recognition technology is being deployed to keep
Speaker 1: individual track of all kinds of livestock like cows and
Speaker 1: chickens for maybe medical reasons or reasons having to do
Speaker 1: in the case of cows, reasons having to do with tracking,
Speaker 1: like periods of peak milk output and stuff like that.
Speaker 1: But there are also stories about conservation efforts to non
Speaker 1: invasively monitor wild populations of vulnerable animals by way of
Speaker 1: facial recognition, which if that works, that sounds awesome. Like
Speaker 1: I was looking at article in Scientific American that described
Speaker 1: efforts to use facial recognition to track wild lions through
Speaker 1: a platform called the Lion Identification Network of Collaborators or link.
Speaker 1: That's interesting. It reminds me of I want to say,
Speaker 1: like a decade ago, maybe a little a little further
Speaker 1: back in time. Uh, there was a piece I read
Speaker 1: about tracking whale sharks, and whale sharks all have you know,
Speaker 1: distinctive patterns on their you know that's sort of the
Speaker 1: top of their heads that area, and uh, you know,
Speaker 1: it doesn't mean anything, you know, much to humanize. But
Speaker 1: I think at the time they were utilizing NASA technology
Speaker 1: that was aimed at making sense of of the stars
Speaker 1: like astronomical um computation systems to then to make sense
Speaker 1: and sort of track um identify at any rate these
Speaker 1: various whale sharks. So this would but this sort of
Speaker 1: thing would be I think an even better method of
Speaker 1: doing that because because you're you're probably dealing with it
Speaker 1: with creatures in all these cases that uh, you know,
Speaker 1: there's a they definitely are not all identical. There are differences,
Speaker 1: but we just may not have the eye for it,
Speaker 1: whereas technology can be can be used to say the
Speaker 1: Sharper eye for chicken identity exactly, and you know, it
Speaker 1: has the advantage of not having to physically tag the
Speaker 1: animal in some way, which can be difficult to do
Speaker 1: or it can be harmful to the animal right or
Speaker 1: day are dangerous to the individuals doing the tagging. Yeah,
Speaker 1: um and so apparently so you've got this one with lions,
Speaker 1: the link project, But there are similar things that have
Speaker 1: been attempted with tigers, elephants, even whales. You mentioned whale sharks,
Speaker 1: but with like actual mammal whales, with a project that
Speaker 1: an article in the Atlantic called Facebook for whales. I
Speaker 1: hope it's not as addictive for whales as it is
Speaker 1: for humans. That was a joke, but you didn't laugh.
Speaker 1: That's okay. But similar technologies have been proposed and tested
Speaker 1: to help link people with lost pets, including cats and dogs.
Speaker 1: That seems like a great use of this. Yeah, like,
Speaker 1: you know, certainly, I'm on enough social media boards where
Speaker 1: their pet owners and occasionally pedicle missing. And then there's
Speaker 1: this whole back and forth where like like, oh my
Speaker 1: my orange cat is missing that it looks like this,
Speaker 1: and then somebody would be like, well does he look
Speaker 1: like this? And I think I saw him in the backyard,
Speaker 1: and someone else is like, oh, I think I saw
Speaker 1: him over here across town, and nobody can be for sure, right,
Speaker 1: because it's hard to get close to a stray cat
Speaker 1: in some cases, or or an escaped cat or a
Speaker 1: feral cat that is then misidentified as a lost cat.
Speaker 1: But if you had the ability, if you had some
Speaker 1: sort of app infrastructure where your your cats missing, fine,
Speaker 1: you upload them to this database and then when someone
Speaker 1: finds a cat, they just take a picture of it
Speaker 1: and it tells you if that cat is missing. Like
Speaker 1: that would be that would be great. That would cut
Speaker 1: out a lot of the the the anguish and the
Speaker 1: work that goes with having a runaway pet. I agree
Speaker 1: that sounds great, and maybe I'm suffering from a lack
Speaker 1: of imagination, but I'm I'm thinking in cases like that,
Speaker 1: that's a case where I think the the risks to
Speaker 1: the cats privacy would be far outweighed by the benefits
Speaker 1: of people finding their lost animals, because we know how
Speaker 1: cats are. They don't give a damn about privacy. Now
Speaker 1: they have, Yeah, they have a whole different set of
Speaker 1: set of values. Now. Another sort of quirk of timing
Speaker 1: here as we were putting this episode together, in fact,
Speaker 1: is we're sort of finishing our no for this episode.
Speaker 1: This morning, I actually read a new blog post, uh
Speaker 1: titled depth of Field Fails by Janelle Shane at ai
Speaker 1: weirdness dot Com. Oh, we've talked about her blog on
Speaker 1: the show before because it came up in the pair
Speaker 1: of episodes we did called flat a Sex Mackinah, which
Speaker 1: was about why it's so funny when machines fail. Yes, yeah, yeah,
Speaker 1: this is and I imagine a lot of you have encountered, uh,
Speaker 1: you know, the various scenarios she's run with, like AI
Speaker 1: s coming up with names for Halloween costumes or names
Speaker 1: for I think at one point evenel like She's they
Speaker 1: were using it to come up with not only names,
Speaker 1: but actual recipes for cocktails, yes, recipes for foods, also
Speaker 1: names for dishes. We talked about D and D. Character
Speaker 1: biography is generated, and and spells names for spells. Remember
Speaker 1: remember song of the Darn Daving Fire. Yeah, there's even
Speaker 1: one about cat names. But in this particular post, um
Speaker 1: Shane the research scientists. She tests out the facial recognition
Speaker 1: AI that is employed by Skype for its blur My
Speaker 1: Background for All Calls feature. Now, the curious thing about
Speaker 1: this is I've been using Skype. We've been using Skype
Speaker 1: here on the show for years for interviews, and I
Speaker 1: guess I just I don't dig in deeply enough or
Speaker 1: read emails because I didn't realize this was a feature
Speaker 1: at all until today. I think we always let somebody
Speaker 1: smarter than us figure out how to use Skype and
Speaker 1: there we'll just do the talking. But but it, but
Speaker 1: it totally makes sense as a feature because perhaps you
Speaker 1: do have an ideal environment set up for a call
Speaker 1: with a business friendly background behind you, but maybe you don't.
Speaker 1: Maybe you have a fridge with a bunch of notes
Speaker 1: stuck to it with magnets, and maybe some of those
Speaker 1: are like bills or you know, or they have some
Speaker 1: data on there that you you wouldn't even want there
Speaker 1: to be a chance somebody might be able to decipher it,
Speaker 1: or a bookshelf full of occult tones that you don't
Speaker 1: want people to know you've been researching that that's a
Speaker 1: good one. Or perhaps you're at work and there's a
Speaker 1: marker board full of you know, of data back there
Speaker 1: might be something you don't want out, Or perhaps you
Speaker 1: have some distracting art up there in the wall and
Speaker 1: you don't want to compromise the interior of your own
Speaker 1: home so that you can do a skype call. You
Speaker 1: don't want to have to like take things off the
Speaker 1: wall in order to do this, or you're in one
Speaker 1: of those weird Roman mass toilets, whatever the case may be.
Speaker 1: The AI then can auto blur all of that out
Speaker 1: for you, But to do so, it has to be
Speaker 1: able to tell the difference between the face of the
Speaker 1: collar and mere objects in the background. Uh. And it's
Speaker 1: in this case the AIS definition of a face is
Speaker 1: pretty broad. As Shane discovers, it will allow ancient Egyptian
Speaker 1: illustrations to to come through unblurred, to be the face,
Speaker 1: to be the face. So it's like, okay, this call
Speaker 1: is being made by this individual from ancient Egypt isis
Speaker 1: but also increasingly abstract depictions of a human face. Uh.
Speaker 1: So it showed various works of art and it's some
Speaker 1: of them were really abstract, and it was like, all right,
Speaker 1: that's a face. Sure that will work, just like monk
Speaker 1: the scream, Yes, yeah exactly. Um. Also stuffed giraffes. It
Speaker 1: had a problem with the like the horns of the draffe,
Speaker 1: but but not so much the face of the stuffed giraffe. Uh.
Speaker 1: It gets a little confused with life size plastic skeletons, however,
Speaker 1: and also cats can throw it off as well. But
Speaker 1: so do check out that that blog post. It's it's
Speaker 1: amusing and also insightful. But all this is certainly I
Speaker 1: think an example of facial recognition AI doing something that's
Speaker 1: not only helpful but could actually help you with your privacy. Now,
Speaker 1: one thing I think that would be different there is
Speaker 1: that that's facial recognition in the sense of recognizing a
Speaker 1: face as opposed to a background, versus recognizing whose face
Speaker 1: a picture is of right. But then again you could
Speaker 1: easily imagine like that being an upgrade or being a
Speaker 1: situation where if you had a more robust um facial
Speaker 1: recognition um AI that was then used on some sort
Speaker 1: of skypelike system, you might actually go ahead and have
Speaker 1: a feature where the caller's face was logged and therefore
Speaker 1: it would blur out any face that was not the
Speaker 1: authorized users face. So that way of you know, there
Speaker 1: are other employees walking by in the background getting coffee,
Speaker 1: they're not going to show up on your call. If
Speaker 1: you're you know, significant other walks by in the background,
Speaker 1: they're not going to show up on the call, etcetera.
Speaker 1: So even with this, I mean, when I'm in these
Speaker 1: kind of scenarios in my head, I'm always wondering if
Speaker 1: there are freaky applications that I'm just not being imaginative
Speaker 1: enough to to get to. Yet, We'll let me put
Speaker 1: on my black mirror neural lace cap for a second
Speaker 1: and think, um how about a simple case where law
Speaker 1: enforcement wants to access an unblurred background. Now, I'm not
Speaker 1: sure to what extent that's even possible with this technology,
Speaker 1: but what if, say, you know, a government agency made
Speaker 1: a claim for a need to override the auto blurring
Speaker 1: features utilized by others, so they would just have blanket
Speaker 1: power to do this. So you think you're blurring your background,
Speaker 1: but actually you're Yeah, nobody can see, not if you're
Speaker 1: on the phone with you know, with with with someone
Speaker 1: who's actually a government employee or whoever happens to have
Speaker 1: the magic key in this scenario. Oh, I guess it's
Speaker 1: kind of like how you think you can have your
Speaker 1: phone turned off, or you think you can have GPS
Speaker 1: turned off, but in fact it is still location tech. Yeah, yeah,
Speaker 1: that's sort of thing. And again I don't have a
Speaker 1: detailed enough knowledge of this particular software. I'm not saying
Speaker 1: not not not applying this directly to the Skype scenario here,
Speaker 1: but just sort of thinking in general. Um, now, in
Speaker 1: this particular case, I'm also assuming that the broad definition
Speaker 1: of a face is in place, at least in part
Speaker 1: to avoid situations where a human beings face is blurred
Speaker 1: because the AI can't handle, say, facial disfiguration. Because I
Speaker 1: think we can understand why we wouldn't want an AI
Speaker 1: like this to lean heavily on norms to promote ideals
Speaker 1: about who and who doesn't have a face? Sure though
Speaker 1: this in considering this with face recognition, and we get
Speaker 1: into some some interesting and you know, at times disturbing territory.
Speaker 1: Matthew Galt had an article in Vice last February titled
Speaker 1: Facial recognition software regularly miss genders trans people, detailing how
Speaker 1: these systems were simply not built with trans or non
Speaker 1: binary people in mind, and can quote continue to reinforce
Speaker 1: existing biases? Oh yeah, I mean, as with a lot
Speaker 1: of things, I think sometimes there is an illusion that
Speaker 1: machines somehow will be free from applying biases to humans
Speaker 1: that that humans apply to each other. But I think
Speaker 1: we we've got ample evidence now that that is not
Speaker 1: the case. That human biases get quite easily mapped onto
Speaker 1: artificial intelligence through assumptions used in the in the creation
Speaker 1: of these algorithms, or through the data sets they're trained on.
Speaker 1: Right and then, and as Galt explores in the article,
Speaker 1: like part of it too is just like who's building
Speaker 1: these programs, You know, it's built being built by programmers
Speaker 1: and engineers, people that are that that may just not
Speaker 1: have have ever really given serious study to some of
Speaker 1: like say the gender issues that are you know inherent
Speaker 1: to the problem. He also mentions how past databases have,
Speaker 1: for instance, misidentified black people in criminal databases and even
Speaker 1: in some cases had they failed to see black people
Speaker 1: at all. Yeah, like say, if they are trained primarily
Speaker 1: on data sets with lighter skinned faces. In fact, just
Speaker 1: last December December of twenty nineteen, the National Institute of
Speaker 1: Standards and Technology in the US tested a hundred and
Speaker 1: eighty nine facial recognition algorithms from ninety nine developers, which
Speaker 1: includes like some big name developers, and found that they
Speaker 1: were far less accurate at identifying African American and Asian
Speaker 1: faces compared to Caucasian faces, and African American females were
Speaker 1: even more likely to be misidentified. Uh. Now this was
Speaker 1: reported in various places. Uh, but the article I was
Speaker 1: reading about it BBC News text article facial recognition fails
Speaker 1: on race Government study says Yeah, I've read about several
Speaker 1: cases like this. Uh. I mean, I think it's just
Speaker 1: so important for people, especially working in the technology space,
Speaker 1: to remember, you know, don't fall for the myth that
Speaker 1: it's unbiased just because it's a machine and not a person.
Speaker 1: People's biases end up in the machine. The rules come
Speaker 1: from us. Okay, well, I think we're gonna have to
Speaker 1: call the first episode right there. But when we come
Speaker 1: back in the in the next in the series of episodes,
Speaker 1: we're gonna be definitely talking about facial recognition in the
Speaker 1: organic brain, and we'll be moving on more to the
Speaker 1: history of technological facial recognition. We'll get to talk about Greebel's.
Speaker 1: We love Greebel's. Greebles were new to me, but there's
Speaker 1: a whole world. We could do a whole podcast on Greebel's.
Speaker 1: You might think they were new to you, they weren't
Speaker 1: new to you. We've talked about Greebeles, we have talked
Speaker 1: about Greebles have the most delectable spikes. Okay, if you're curious,
Speaker 1: you'll have to come back next time to find out.
Speaker 1: Come back for the Greebel's. In the meantime, if you
Speaker 1: want to check out other episodes of Stuff to Blow
Speaker 1: your Mind, you can find us anywhere you find podcasts.
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Speaker 1: out and just tell a friend about the show. That
Speaker 1: also helps. And don't forget our other podcast, Invention. Invention
Speaker 1: is a journey through human techno history. Oh yeah, I
Speaker 1: feel like we've been doing a lot most of our
Speaker 1: technology stuff on invention these days, and so uh so
Speaker 1: I'm glad to be getting back into the techno space
Speaker 1: a little bit on Stuff to Blow your Mind today. Yeah, absolutely,
Speaker 1: even if it is for a kind of dystopian sci
Speaker 1: fi topic like this. Anyway, huge things as always to
Speaker 1: our excellent audio producer Seth Nicholas Johnson. If you would
Speaker 1: like to get in touch with us with feedback on
Speaker 1: this episode or any other, to suggest a topic for
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