TechStuff Classic: The National Facial Recognition Database

Are you an adult citizen in the US? If so, there's a 50% chance your face is in a law enforcement database. We look at this problematic law enforcement tool.

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Episode Transcript

Speaker 1 (00:04):
Welcome to tech Stuff, a production from iHeartRadio.

Speaker 2 (00:12):
Hey there, and welcome to tech Stuff. I'm your host,
John that Strickland.

Speaker 1 (00:15):
I'm an executive producer with iHeart Podcasts and how the
tech are you? It's time for another classics episode. This
episode originally published on May nineteenth, twenty seventeen. It is
called the National Facial Recognition Database. Pretty, I would say,
controversial topic. Well, let's listen in now. Before I dive

(00:40):
into the topic, I want to make a couple of
things very clear at the very beginning. First is I'm biased.
I think the use of facial recognition software is problematic
even if you have regulations in place. But I'm mostly
talking about unregulated use because really we haven't a establish

(01:00):
the rules and policies to guide the use of facial
recognition software in a law enforcement context. So that's problem
Number one is I have a very strong opinion about
this and I'm not going to shy away from that.
It's really unjustifiable to have unregulated use of facial recognition

(01:20):
software in law enforcement contexts. So I want to make
that clear out of the gate that I have this bias,
and if that's an issue, that's fair, But at least
I'm being honest, right, I'm not presenting this as if
it's completely objective, unbiased information. I own this. You don't
have to tell me. I know it already. Next, this

(01:43):
is largely going to be a US centric discussion so
that I can talk about details. But please know that
there are a lot of these types of systems all
over the world, not just in the United States, and
a lot of these places have similar issues to the
ones I'm going to be talking about here in the US.
I'll just be focusing more on US stories to make

(02:06):
specific points because this is where I live, and now
to explain what I'm actually talking about here. So, back
in twenty ten, the FBI undertook a project that cost
more than an estimated one point two billion dollars that's
billion with a B to replace what was called the
Integrated Automated Fingerprint System or IAFS. Now, if I had

(02:29):
been in place since nineteen ninety nine, and I've talked
about fingerprints in a previous episode, IAFS was an attempt
to create a usye database of fingerprint records so that
if you were investigating a crime and you had lifted

(02:50):
some prints from the crime, you could end up consulting
this database and see if there are any matches in
place to give you any leads on your investigation. The
twenty ten project the FBI undertook was meant to vastly
expand that capability by adding a lot more data to

(03:11):
the database, not just fingerprints, but other stuff as well,
and the new system is called the Next Generation Identification
or NGI. It includes not just fingerprints, but other biographical
data and biometrics information, including face recognition technology. So a
lot of images are included in this particular database. So

(03:34):
as part of this project, the FBI incorporated the Interstate
Photo System or IPS, so you have NGI IPS it
typically is how it's referred to now. That system includes
images from police cases as well as photos from civil
civic sources that are not necessarily related to crimes. That's

(03:56):
not the only way the FBI can scan for a
match of a photograph they've taken that relates to a
case in some way to this massive database, but more
on that in a little bit now. The general process
of searching for a match follows a pretty simple pattern,
although the details can be vastly different depending upon what

(04:18):
facial recognition software you are using at the time. So
you first start with an image related to a case,
and this is called the probe photo. It is the
one you are probing for lack of a better term,
you don't know the identity of the person in the photograph, typically,

(04:40):
or at least you might have suspicions, but you don't
necessarily know for sure. So you've got a picture of
an unknown person in this photograph. You then scan that
photo and you use facial recognition software to analyze the
picture and to try and find a match in this
larger database. It starts searching all of the images in
the database looking for any that might be a potential match.

(05:03):
Depending upon the system and the policies that are in use,
you could end up with a single photo return to you.
You could end up with dozens of photos, so these
would all be potential matches with different degrees of certainty
for a match. You might remember in episodes I've talked
about things like IBM's Watson that would come up with
answers to a question and assign a value to each

(05:26):
potential answer, and the one that had the highest value,
assuming it's above a certain threshold, would be submitted as
the answer. So it's not so much that the computer
quote unquote knows it has a match. It suspects a
match based upon a certain percentage as long as it's
over a threshold of certainty, or you might end up

(05:48):
with no photos at all. If no match was found
or nothing ended up being above that threshold, the system
might say, I couldn't match this photo with anyone who's
in the database. A study performed by researchers at Georgetown
University found that one in every two American adults has

(06:09):
their face captured in an image database that is accessible
by various law enforcement agencies, including but not limited to
the IPS. In fact, the IPS has a small number
of photos compared to the overall number represented by databases
across the US. Now, this involves agencies at all different levels, federal, state,

(06:33):
even tribal law for Native American tribes. That ends up
being about one hundred and seventeen million people in these databases,
many of whom, in fact large percentage of whom have
no criminal background whatsoever. Their images are also in these databases,

(06:54):
and this raises some big concerns about privacy and also accountability.
So in today's episode, we're going to explore how facial
recognition software works, as well as talk about the implementation
for law enforcement and the reaction to this technology, and
will probably listen to me get upset and a little

(07:14):
head up about the whole thing in general. All right,
So first, before we leap into the mess of law enforcement,
because it is a mess, that's just a fact, let's
talk first about the technology itself. When did facial recognition
software get started and how does it work? Well, it's
related to computer vision, which is a subset of artificial

(07:38):
intelligence research. If you look at artificial intelligence, a lot
of people simplify that by meaning, oh, this is so
that you can teach computers how to think like people.
But that's actually a very specific definition of a very
specific type of artificial intelligence. When you really look at
AI and you break it out, it involves a lot
of subsets of abilities. One of those is the ability

(08:01):
for machines to analyze imagery and be able to determine
what that imagery represents. In a way, you could argue
it's teaching computers how to understand pictures. It's also really challenging,
and this is one of the object lessons that I
use to teach people how Artificial intelligence is really tricky.

(08:25):
It requires more than just pure processing power. I mean,
processing power is important, but you can't solve all of
AI's problems just by throwing more processors at it. You
have to figure out from a software level how to
leverage that processing power in a way that gives computers
this ability to identify stuff based upon imagery. So a

(08:48):
computer might be able to perform far more mathematical operations
per second than even the cleverest of humans, but without
the right software, they can't identify the picture of a
seagull compared to say, a semi truck. You have to
teach the computer how to do this. So let's say
you develop a program that can analyze an image and
break it down into simple data to describe that image,

(09:13):
and then you essentially teach a computer what a coffee
mug looks like. You take a picture of a coffee mug,
you feed it to a computer, and you essentially say
this data represents a coffee mug. You then would have
to try and train the computer on what that actually means.

(09:36):
The computer does not now know what a coffee mug is.
It will recognize that specific mug in that specific orientation
under those specific lighting conditions, assuming that you've designed the
algorithm properly. But it's way more tricky than that. What
if in the image that you fed the computer, the

(09:56):
coffee mugs handle was facing to the left with respect
of the viewer, but in a future picture the handle
is off to the right instead of to the left,
or it's turned around so you can't see the handle
at all. It's behind the coffee mug. Well, if the
mug is bigger or smaller, or a different shape, well
if it's a different color. Image recognition is tough because

(10:18):
computers don't immediately associate different objects within the same category
as being the same thing. So if you teach me, Jonathan,
what a coffee mug is, and you show me a
couple of different examples saying, this is a coffee mug,
but this is also a coffee mug, even though it's
a different size and different shape and a different color,

(10:40):
I'll catch on pretty quickly and it won't take very
many coffee mugs for me to figure out. All Right,
I got the basic idea of what a coffee mug is.
I know what the concept of coffee mug is now,
But computers aren't like that. You have to feed them
thousands of images, both of coffee mugs and of not

(11:01):
coffee mugs, so that the computer starts to be able
to pick out the various features that are the essence
of a coffee mug versus things that are not related
to being a coffee mug. It takes hours and hours
and hours of work of training these computers to do it,
so it's a non trivial task, and this is true

(11:22):
of all types of image recognition, including facial recognition. Now,
to get around that problem, you end up sending thousands,
countless thousands, millions maybe of images of what you're interested
in while you're training the computer. And the nice thing
is computers can process this information very very quickly, so

(11:44):
while it takes a lot, it doesn't take relatively that long,
it's not as laborious a process as it could be
if computers were slower at analyzing information. So you might
remember a story that kind of illustrates the point. Back
in twenty twelve, there was a network of sixteen thousand

(12:05):
computers that analyzed ten million images, and as a result,
it could do the most important task any computer connected
to the Internet should be expected to do. It could
then identify cat videos because it now knew what a
cat was, or at least the features that define catness.
Catness as in the essence of being a cat, not

(12:27):
a character from Hunger Games. Even then, there were times
when a computer would get it wrong. Either it would
not identify a cat as being a cat, or it
would misidentify something else as being a cat because its
features were close enough to cat like for it to
fool the computer algorithm. A major breakthrough in facial recognition
algorithms happened way back in two thousand and one. That's

(12:48):
when Paul Viola and Michael Jones unveiled an algorithm for
face detection, and it worked in real time, which meant
that it could recognize a face that it would appear
on a webcam. And by recognized, I mean it recognized
that it was a face. It didn't assign an identity
to the face. It didn't say, Oh, that's Bob, It said, oh,

(13:11):
that is a face that is in front of the
webcam right now. The algorithm soon found its way into
open CV, which is an open source computer vision framework,
and the open source approach allowed other programmers to dive
into that code and to make changes and improvements, and
it helped a rapid prototyping of facial recognition software to

(13:36):
Other computer scientists who helped advance computer vision further were
Bill Triggs and Navnit de Lal, who published a paper
in two thousand and five about the histograbs of oriented gradients. Now,
that was an approach that looked at gradient orientation in
parts of an image, and essentially it describes the process
of viewing an image with attention to edge directions and

(13:57):
intensity gradients. That's a complicated way of saying the technique
looks at the totality of a person, and then a
machine learning algorithm determines whether or not that is actually
a person or not a person. A bit later, computer
scientists began pairing computer vision algorithms with deep learning and
convolutional neural networks or CNNs. To go into this would

(14:21):
require an episode all by itself. Neural networks are fascinating,
but they're also pretty complicated, and I've got a whole
lot of topics to cover today, so we can't really
dive into it. You can think of an artificial neural
network as designing a computer system that processes information in
a way that's similar to the way our brains do.
The computers are not thinking, but they are able to

(14:44):
process information in a way that mimics how we process information,
or a semi close approximation thereof that's a really kind
of weak way of describing it. But again, to really
go into detail will require a full episode all by itself. Typically,
facial recognition software uses feature extraction to look for patterns

(15:08):
in an image relating to facial features. In other words,
it searches for features that resemble a face, the elements
you would expect to be present in a typical face,
So eyes, nose, a mouth, that would be major ones. Right.
Then the software starts to estimate the relationships between those
different elements. How wide are the eyes, how far apart

(15:32):
are they from each other, How wide is the nose,
how long is the jawline, what shape are the cheekbones?
These sort of elements all play a part as points
of data, and different facial recognition software packages weight these
features in a different way. So it's not like I

(15:52):
could say all facial recognition software looks at these four
points of data as its primary source. It varies depending
on upon the algorithm that's been designed by various companies,
and part of the problem that we're going to talk
about is that law enforcement across the United States they
are not relying on a single facial recognition software approach.

(16:13):
Different agencies have different vendors that they work with, So
just because one might work very well doesn't necessarily mean
it's competitors work just as well. And that's part of
the problem. Now, all of these little points of data
I'm talking about, these notle points and how they relate
to one another, all of that gets boiled down into

(16:35):
a numeric code that you could think of as a
face print. This is supposed to be a representation of
the unique set of data that is a compilation of
all of these different points boiled down into numeric information itself.

(16:56):
Then what you would do is you would have a
database of face So if you wanted to find a match,
you would feed the image you have, the probe image
into this database, and the facial recognition software would analyze
the probe photo. It would end up assigning this numeric
value and would start looking through the database for other

(17:16):
numeric values that were as similar to that probe one
as possible and start returning those images as potential matches
or candidates. They tend to use the word candidate photos.
Otherwise you'll either get no match at all or you
get a false positive. You will end up getting an
image of someone who looks like the person whose image

(17:39):
you submitted, but is not the same person. That does happen,
And that's the basic way that facial recognition software works.
But keep in mind, different vendors use all their own
specific approaches, like I said, and some could be less
accurate than others. Some might be accurate for specific ethnicities
and not as accurate as other ones. That's a huge problem,

(18:03):
so it gets complicated. Even when I'm talking in more
general terms, you have to remember that there are a
lot of specific incidents and specific implementations of facial recognition
software that have their own issues. So I'm gonna be
as general as I can. I'm not going to call

(18:24):
out any particular facial recognition software vendors out there. I'm
more going to talk about the overall issues that various
organizations have had as they've looked into this topic. Now,
there are plenty of applications for facial recognition that have
nothing to do with identifying a person. I mentioned that
earlier that there was the one for a webcam that

(18:45):
could identify when a face was in front of the webcam.
This wasn't to identify anybody. It was again just to say, yes,
there's somebody looking into the webcam at this moment, which
by itself can be useful and have nothing to do
with identification. There are plenty of digital cameras out there
and camera phone apps that can identify when there's a

(19:06):
face looking at the camera, and again it's not necessarily
to identify that person, but rather to say, oh, well,
this is a face. The camera is most likely trying
to focus on this person, so let's make this person
the point of focus and not focus on something in
the background like a tree that's fifty yards back. Instead,
let's focus on the person who's in the foreground. So

(19:28):
that's pretty handy, and again there's nothing particularly problematic from
an identification standpoint, because that's not the purpose of it.
But then you also have other implementations, like on social media,
which allow you to do things like tag people based
upon an algorithm recognizing a person. So Facebook is a

(19:50):
great example of this. Right, if you upload a picture
of one of your Facebook friends onto Facebook chances are
it's giving you a suggestion to tag that photo with
the specific in mind. That may not be that problematic either,
depending upon how your friend feels about pictures being uploaded
to Facebook. Some people are very cautious about that, and

(20:14):
of course you know, I always recommend you talk to
anybody before you start tagging folks on Facebook photos, just
to make sure they're fine with it. I say that
as a person who has done it, and then notice
that some of my tags got removed by the people
I tagged later on, which taught me I should probably
ask first, rather than give them the feeling that they

(20:35):
need to go and remove a tag or two. We've
also seen examples of this simple implementation of facial recognition
going awry. Google's street View will blur out faces, for example,
in an effort to protect people's identity while street view
cars are out and about taking images. This makes sense.

(20:56):
Let's say that you are in a part of town
that you normally would not be in. For whatever reason,
you might not want your picture to be included on
Google street View, so that whenever anyone looks at that
street for that point forward, they see your face on there,
you know, coming out of I don't know a Wendy's.
Maybe you are a manager for burger King that would

(21:19):
look bad, or you know, lots of other reasons that
obviously can spring to mind as well. You don't want
to violate someone's privacy. But Google StreetView would also blur
out images that were not real people faces, like images
on billboards or murals. Sometimes if it had a person's
face on a mural, the face would be blurred out,

(21:40):
even though it's not a real person, it's just a
painting or In September twenty sixteen, c Neet reported on
an incident in which Google street View blurred out the
face of a cow. So Google was being very thoughtful
to protect that cow's privacy. But what about matching faces
to identities? So in some cases, again seemingly harmless if

(22:04):
you want to tag your friends, but when it comes
to law enforcement, things get a bit sticky, particularly as
you learn more about the specifics. And we'll talk about
that in just a second, but first let's take a
quick break to thank our sponsor. All right, let's first

(22:28):
start with the FBI's Interstate Photos System, or IPS, because
this one has perhaps the least controversial elements to it
when you really look at it, it's still problematic, but
not nearly as much as the larger picture. The system
contains both images from criminal cases like mugshots and things

(22:51):
of that nature, but it also includes some photos from
civil sources like ID applications, that kind of thing. When
the Government Accountability Office or GAO, they're gonna be a
lot of acronyms and initializations or initialisms, I should say
in this episode, so I apologize for that. But Government
Accountability Office they did a study on this matter just

(23:16):
in twenty sixteen, so not that long ago. They published
its report on facial recognition software use among law enforcements,
specifically the FBI because they're a federal agency, so they
were concerned with the federal use of this. The database
contained about thirty million photos at the time of the

(23:37):
GAO study, so thirty million pictures are in this database.
Most of those images came from eighteen thousand different law
enforcement agencies at all levels of government, that includes the
tribal law enforcement offices. About seventy percent of all the
photos in the database were mugshots. More than of the

(24:01):
photos in that database are from criminal cases, so that
means that less than twenty percent were from civil sources.
In addition to that, there were some cases, plenty of them,
where the database had images of people both from a
civil source and from a criminal source. So I'll give

(24:23):
you a theoretical example. Let's say that sometime in the
past I got nabbed by the cops for grand theft
auto because I play that game. But let's say that
I stole a car, which we already know is a
complete fabrication because I don't even drive. But let's say

(24:43):
I stole a car, and that I had moved the
car across state lines. It became a federal case. Therefore,
my criminal information is included. My mugshot would be included
in this particular database. On related note, my ID also

(25:04):
is in that database as a civil image, not as
a criminal image. Well, in my case, they would tie
those two images together because they refer to the same
person and I had been involved in a criminal act.
So while I would have an image in there from
a civil source, it would be filed under the criminal

(25:26):
side of things. This is important when we get to
how the probes work. Now, let's say you have been
perfectly law abiding this whole time, and that your ID
is also in this database, but it's just under the
civil side of things. Since you don't have any criminal background,
it's not connected to anything on the criminal side, So

(25:49):
when it comes to probes using the IPS, your information
will not be referenced because the FBI policy is when
it's running these potential matches with a photo that's been
gathered as part of the evidence for an ongoing investigation,
they can only consult the criminal side, not the civil side,

(26:12):
with the exception of any civil photos that are connected
to a criminal case, as in my example, those are
fair game. So it might run a match and it
turns out that my photo for my state given identification
card is a better match than the mugshot is. That's
going to be fine because those two things were both

(26:34):
attached to a criminal file in the first place. But
let's say that it would have matched up against you
since you didn't have a criminal background, and since the
only record in there was a civil source, the match
would completely skip over you. It wouldn't return your picture
because your image is off limits in that particular use

(26:56):
very important because it's an effort to try and make
sure this facial recognition technology is focusing just on the
criminal side, not putting law abiding citizens in danger of
being pulled up in a virtual lineup, at least not

(27:19):
using that approach. That's the problem is that that's not the
only way the FBI runs searches. In fact, that might
not be the primary way the FBI runs searches when
they're looking for a match to a photo that was
taken as part of evidence gathering in pursuing a case.

(27:40):
But let's say that you are an FBI agent and
you've got a photo, a probe photo, and you want
to run it for a match. What's the procedure. You
would send off your request to the NGI dash Ips Department,
and you would have to indicate how many potential photographs
you want back, how many candidates do you want. You

(28:02):
can choose between two candidate photos and fifty candidate photos.
These are photos of different individuals, by the way, not
just here's a picture of Jonathan on the beach. Here's
a picture of Jonathan in the woods. No, it's more like,
here's a picture of Jonathan. Here's a picture of a
person who's not Jonathan, but also kind of matches this
particular probe photo you submitted. And here are forty eight others.

(28:28):
The default is twenty, so if you don't change the
default at all, you will get back twenty images that
are potential candidates matching your probe photo, assuming that any
are found at all. It is possible that you submit
a probe photo and the system doesn't find any matches
at all, and which case you'll just get a null.

(28:49):
You might get less than what you asked for if
only a few had met the threshold for reliability. Now
we call them candidate photos because you're supposed to acknowledge
the fact that these are meant to help you pursue
a lead of inquiry. In a case, it is not

(29:11):
meant to be a source of positive identification of a suspect.
So in other words, you shouldn't run a facial recognition
software probe, get a result back and say that's our guy,
let's go pick him up. That's not enough. It's meant
to be the start of a line of inquiry, and

(29:35):
whether or not it gets used that way all the
time is another matter. But the purpose of calling it
candidate photo is to remind everyone this is not meant
to be proof of someone's guilt or innocence. The FBI
also allows certain state authorities to use this same database,
and different agencies have different preferences. So in the GAO

(30:00):
report that I talked about earlier, the authors noted that
law enforcement officials from Michigan, for example, would always ask
for the maximum number of candidate photos, particularly when they'd
use probe images that were of low quality. So let's
say you've got a picture captured from a security camera
and the lighting is pretty bad and perhaps the person

(30:21):
wasn't facing dead on into the camera. You might ask
for the maximum number of candidate photos to re return
to you, knowing that the image you submitted was low quality,
and therefore any match is only potentially going to be
the person you're actually looking for. And again, this is

(30:42):
all just to help you with the beginning of your investigation.
It's not meant to be the that's our guy moment
that you would see and say police procedural that would
appear on network television in primetime. The FBI I also
has a policy in that all returned candidate photos must

(31:04):
first be analyzed by human specialists before being passed on
to other law enforcement agencies. Up to that point, the
entire process is automatic, so you don't have people overseeing
the process once it's probing all of the database, but
once the results come in, human analysts, who are supposed
to be trained in this sort of thing, are supposed

(31:26):
to look at each of those returned candidates and determine
if whether or not they really do resemble the person
in the probe photo that was submitted in the first place,
and if they're not, they are not supposed to be
passed on any further down the chain. Now, so far,
this probably doesn't sound too problematic. The FBI has a

(31:47):
database containing both criminal and civil photographs, but when it
runs a probe, it can only use the criminal photos
or the civil ones that are attached to criminal files.
Candidate photos are supposed to only be used to help
start a line of inquiry, not to positively identify suspects,
and everything has to be reviewed by human being. That
sounds fairly reasonable. But even if you're mostly okay with

(32:09):
this approach, which still has some problems we'll talk about
in a bit, things get significantly more dicey as you
learn more about the FBI's policies. For example, they have
a unit called the Facial Analysis Comparison and Evaluation Services
or face FACE. This is a part of the Criminal

(32:32):
Justice Information Services Department CG. Rather I yeah, I can
spell justice with a G. It doesn't make sense. No,
the cjis. This is a department within the FBI, and
FACE can carry out a search far more wide reaching
than one that just uses the ngi IPS database. FACE

(32:55):
uses not only that database but also external databases when
conducting a search with a probe photo. So let's say again,
you're an FBI agent and you have an image that
you want to match. You want to find out who
this person is. Maybe it's just a person of interest,
doesn't even necessarily have to be a suspect. Could be that, hey,
maybe this person can tell us more about this thing

(33:17):
that happened later on. Well, you could follow the NGIIPS procedure,
which would focus on those criminal photographs, or you could
submit your image to face. Face then would search dozens
of databases holding more than four hundred eleven million photographs,

(33:38):
many of which are from civil sources. So NGIIPS has
thirty million, all of them together have four hundred eleven
million pictures. And again a lot of those pictures just
come from things like passport ID, driver's licenses, sometimes security clearances,

(34:02):
that sort of stuff. That's this database has a lot
of law abiding citizens who have no criminal record, and
the images have nothing to do with any sort of
criminal act, but they're in these databases. These external databases
belong to lots of different agencies, and both at the
federal level and state level. So you've got state police agencies,

(34:25):
You've got the Department of Defense, You've got the Department
of Justice, you have the Department of State, and again
it contains photos from licenses, passports, security ID cards, and more.
So your submission would then go to one of twenty
nine different biometric image specialists. They would take that probe
photo and run a scan through these various databases and

(34:46):
they would look for matches. Here's another problem. Each of
these systems has a different methodology for performing and returning
search results, which makes this even more complicated. For example,
I talked about how the ngi IPS system gives you
a return between two and fifty candidate photos. Right, Well,

(35:06):
the Department of State will return as many as eighty
eight candidate photos if they are all from visa applications
from people who are not US citizens. So you can
get up to eighty eight pictures from visa applicants, or
you could just get three images from US citizen passport applicants,

(35:27):
because that's a hard limit. They can only return three
candidate photos from US citizens who applied for passports, but
they can return up to eighty eight visa application photos.
The Department of Defense will will down all of their
candidates into a single entry. So, in other words, Diberna Defense,
if you query that database with your probe photo, you

(35:51):
will only get one image back, so they will call
all the other ones and give you the most likely
match out of all the ones that they find in
their search. Some states will do similar things where they
will narrow down which images they will return to you.
Some of them will just give you everything they've got.

(36:13):
Every match that comes up, they'll just return it back
to the FBI. So it's very complicated. You can't really
be sure what methods people are using to be certain
that the potential matches they have represent a good match,
a good chance that the person that they've returned is
actually the same one who is in the probe photo.

(36:38):
At any rate, you as an FBI agent, wouldn't get
all of these at all, all of these photos that
would come back, They would come back to that biometric
analyst over at face, So you send your request to
face face takes care of the rest. They get back
all these results. Then they go through the results they
get back and they whittle that down to one or

(37:00):
two candidate photos and they send those on to you,
the FBI agent. So by the time you get it,
you only see one or two out of the potentially
more than one hundred images that were returned on this search.
But you might ask, well, how frequently does this happen?

(37:20):
I mean, how often is the FBI looking at images,
including pictures of law abiding citizens in these virtual lineups.
It can't be that frequent, right, Well, again, according to
that GAO report, the FBI submitted two hundred fifteen thousand
searches between August twenty eleven, which is pretty much when

(37:41):
the program went into pilot mode and started to be
rolled out more widely, through December twenty fifteen two hundred
and fifteen thousand. From August twenty eleven to December twenty fifteen,
thirty six thousand of those searches were on state driver's
licensed databases. So it happens a lot thirty six thousand times.

(38:05):
Chances are if you are an adult in America, you
got like a coin flip situation that your image was
looked at at some time or another by an algorithm
comparing it to a probe photo in the pursuit of
information regarding a federal case or in some cases, state cases,
because the FBI has also allowed certain states law agencies

(38:29):
access to this approach. Now, according to the rules, the
FBI should have submitted some important documents to inform the
public of their policies and to lay down the regulations,
the rules, the processes that they would have to follow
in order for this to be fair, for it to

(38:49):
not encroach on your privacy or to violate civil liberties
or civil rights. Without those rules, the use of the
system is largely unread, which can lead to misuse, whether
it's intentional or otherwise. The Government Accountability Office specifically pointed
out two different types of notifications that the FBI either

(39:11):
failed to submit or was just very late in submitting.
The first is called a Privacy Impact assessment or PIA. Now,
as that name suggests, a PIA is meant to inform
the public about any potential conflicts with privacy with regards
to methods for collecting personal information. The FBI did submit

(39:33):
a PIA for its next generation system, but they did
it back in two thousand and eight when they first
launched the NGIIPS. According to the Government Accountability Office, the
FBI made enough significant changes to the system to warrant
another PIA that anytime you make a significant revision to

(39:55):
your personal information systems, you have to submit a new
because things have changed, and according to the GAO, the
FBI failed to do that for way too long. Now
ultimately the FBI would publish a new PIA, but by
that point, the Government Accountability Office said they had delayed

(40:18):
so long that it made it more problematic as a result,
because during the whole time that they were supposed to
have submitted this, they were actively using this system. It
wasn't like this was a system being tested. It was
actually being put to use in real cases. And that

(40:38):
kind of violates it, well, it doesn't. Kind of. It
violates a Privacy Act of nineteen seventy four, which states
that when you make these revisions, you're supposed to file
a PIA before you put it into use. According to
the GAO, the FBI failed to do so, and also
the longer you wait to file this the more entrenched though,

(41:00):
those uses come. So if you put a system in place,
you build everything out, you've actually taken the time to
do it, and then you publish a PIA any objections
that are raised, you could say, well, we've got a
system now, and it costs one point two billion dollars
to put it in place. It's going to cost more money,
taxpayer money for us to alter it, to remove it,

(41:23):
to change it. You could argue against any move to
amend the situation. And the GAO says, that's not playing
cricket or playing fair for my fellow Americans. So that's
a problem. But then there's another one. There's a second

(41:43):
type of report called a Systems of Records Notice or
sor in SORN. The Department of Justice was required to
submit a SORN upon the launch of NGIIPS, but didn't
do so until May fifth, twenty sixteen. The GAO criticized
both the FBI and the Department of Justice for failing
to inform the public of the nature of this technology

(42:04):
and how it might impact personal privacy. But wait, there's more.
The GAO report also accused the FBI of failing to
perform any audits to make certain the use of facial
recognition software isn't in violation of other policies, or even
to make sure it doesn't violate the Fourth Amendment rights
of US citizens. Now, for those of you who are

(42:26):
not US citizens, you might wonder what does this actually mean. Well,
the Fourth Amendment is supposed to protect us against unreasonable
search and seizure, and part of that means law enforcement
can't just demand to search you for no reason. And
some have argued that using facial recognition software without a
person's consent, using it invisibly and widespread essentially amounts to

(42:50):
crossing that line. Now, in the United States, we've got
plenty of examples of troublesome policies that seem to overstep
the bounds that are established by the Fourth Amendment. But
that's a tirade for an entirely different show, probably not
a tech stuff, maybe a stuff they don't want you
to know. There are a couple of laws in the
United States that are important to take note of here

(43:12):
besides that Fourth Amendment. One of them I just mentioned
the Privacy Act of nineteen seventy four, and the other
one is the e Government Act of two thousand and two.
The Privacy Act sets limitations on the collection, disclosure, and
use of personal information maintained in systems of records, including
the ones that law agencies use. The e Government Act

(43:32):
is the one that requires government agencies to conduct pias
to make certain that personal information is handled properly in
federal systems, and the GAO report alleges that the FBI
policy wasn't aligned with either of those. Now, part of
this accusation depends upon the fact that the FBI was
using face in investigations for years before they updated their SORN.

(43:53):
They're sworn. According to the Privacy Act, agencies must publish
a new SORN upon the establishment or revision of the
system of records. This is what I was talking about earlier,
except I think I said PIA earlier when actually I
met sor In. That's entirely my fault because I didn't
write in my notes and I was talking next to boraneously.
But SORN is what I should have said. The FBI

(44:16):
argued that it was continuously updating the database to refine
the system, but the GAO's argument was that you could
be continuously updating the system and argue, well, we don't
want to publish an sor in after every tiny revision
because it's wasteful and time consuming. The GAOS counter to

(44:36):
that is, yeah, but you were using this tool in
actual cases. If you were developing this, let's say, in
a department where you're not using real cases, you're just
gradually tweaking the system so that it's more and more
accurate in a controlled environment. That's one thing. But if
you're actively making use of the system in real world investigations,

(45:00):
you absolutely must adhere to these laws, because to do
otherwise is in violation to laws that are passing the
United States. So you can't have it both ways. You
can't continuously tweak a system and put it to official
use and not also file these reports. You could argue

(45:21):
the FBI was trying to have its cake and eat
it too, So the expression that I think I actually
use properly. All Right, we've got more to talk about,
but it's time for us to take another quick break
to thank our sponsor. All right, So, the Government Accountability

(45:44):
Office criticizes the FBI and various other agencies for failing
to establish the scope and use of its facial recognition technology.
But that's just the tip of the iceberg. Because the
GAO report goes on to make an equally troubling point
that the FBI had performed only a few studies on
how accurate these facial recognition systems were in the first place. So,

(46:08):
in other words, not only was this a poorly defined
and unregulated tool, but it's a tool of unknown accuracy
and precision, which is terrifying when you think about it now.
According to the report, the FBI did perform some initial
tests before they deployed the ngiibs, and then occasionally did

(46:28):
a couple of tests when they made some changes. But
there were problems with these tests. For one thing, they
were limited in scope and they didn't represent how the
system might be used out in the real world. When
they were actually running these tests, they ran on about
nine hundred thousand photographs in the database, so they took

(46:49):
a subset of the photos that they had. They took
nine hundred thousand of them, and they ran probe tests
using photos that they knew either were or were not
represented in that group of nine hundred thousand. However, you've
got to remember the full database is more than thirty
million images, so something that works on a smaller scale

(47:13):
may not work once you scale it up for another
The tests did not specify how often incorrect matches would
come back, so you didn't know how many false positives
were there because the FBI wasn't tracking false positives. They
were only concerned with how frequently they were getting a
match to an actual image. So the way they test

(47:37):
this is, you've got nine hundred thousand images, they've got
a probe image, They know for a fact that the
probe image is inside that database, and then they run
the search to see if the system sends that image back.
And their threshold was an eighty five percent detection rate
for a positive match. So, in other words, it went

(47:59):
like this, Let's say you need to conduct a test
of this system. This is one way you would determine
whether or not you had that eighty five percent detection rate.
Let's say you have one hundred probe photos that you've
taken of one person, and you know this person's face
is in that database. You know it's going to be

(48:19):
in among those nine hundred thousand or so images, So
then you submit your query. If you have an eighty
five percent detection rate, then eighty five of those probe
photos should come back with a match, and that match
should be the actual person you're looking for. That's what
they meant by an eighty five percent detection rate, that

(48:40):
eighty five percent of the time an image that is
in their database would be pulled due to a facial
recognition software search. Now, during this testing phase, the FBI
reported that they met this threshold. They used that subset
of actually was nine hundred and twenty six thousand photos

(49:00):
as their subset when they were testing it, and they
said that they had an eighty six percent detection rate,
So they actually were exceeding what they had set as
their threshold. But that just meant that eighty six percent
of the time, the actual match for a probe photos
showed up in a group of fifty candidate images, so

(49:21):
you would get forty nine other images that were not
your match. The match would be there eighty six percent
of the time along with forty nine other images. So
we know that the system works if you were asking
for the maximum number of candidates. Remember in the FBI system,
you can ask for between two and fifty, but fifty
is the max. But what happens if you ask for

(49:44):
fewer images? What if you said, no, I want twenty returns.
What's the accuracy, then the FBI can't tell you because
they do not know. According to the FBI, they did
not run tests to see what would happen if you
decrease the number of candidate photos you asked for. They
only ran tests on the maximum number of candidate photos.

(50:08):
And keep in mind, the default for any search is
twenty photos, so the default is less than what they tested,
and they never tried to see if the eighty six
percent detection rate held true at these lower numbers. That's
a huge issue. On top of that, the FBI didn't
go so far to determine how frequently its system would

(50:30):
return false positives to probes, so they never paid attention
to how many times they got responses that didn't reflect
and the actual identity. They didn't keep track of it. So,
according to the FBI, the purpose of the system is
to generate leads, not to positively identify persons of interest.
So it shouldn't come as a big surprise, or you

(50:51):
shouldn't even care if it returns a lot of false positives,
because hey, this technology isn't meant to be the smoking
gun that says, here's the evidence that will put this
person away. It's meant to just create a lead, So
why do you care how many false positives it returns?
As if being looped in on an official inquiry when

(51:16):
you had nothing to do with it isn't disruptive or
stressful or provoke anxiety. I don't know about you, guys,
but if I had a federal agent show up at
my door asking me weird questions about a case that
I had no connection to because my image had popped
up in one of these searches and I have nothing

(51:36):
to do with it, it just so happens that I
look enough like a photo that's being used in the
case to warrant this. I would probably find that pretty
disruptive in my life, so I would care about false positives. FBI,
at least according to this GAO report, apparently didn't think
it was that big a deal. Now, the GAO points

(52:00):
out that it is a big deal, and that they're
not the only ones to think so. The National Science
and Technology Council and the National Institute of Standards and
Technology both state then, in order to know how accurate
a system is, you need to know two pieces of information,
not just the detection rate, which the FBI claims is
eighty six percent at least when you're asking for fifty candidates,

(52:24):
but also the false positive rate. You have to know
both of them in order to understand how accurate a
system is, So only knowing one of those pieces of
information isn't enough to state this system is accurate or not.
You have to know both. So, not only does the
FBI not have a grasp on how accurate their system

(52:44):
is if you're asking for fewer than the maximum number
of candidates, they also don't know how often it returns
false positives. So the FBI has no way of knowing
how accurate this facial recognition software is that's being used
to actually further investigations for official investigations of the FBI

(53:07):
and also other state agencies that have access to the system,
That is beyond problematic. If you cannot say that the
system with any degree of certainty is above a certain
threshold of accuracy, why are you using it? Because? I mean,
it has the potential to dramatically impact people's lives and

(53:30):
potentially lead people down a pathway that could result in
false accusations and imprisonment. The person who is actually responsible
might totally get away with something because of this. This
is a real problem. And the thing is it might
be a perfectly accurate system, but we don't know that
because we haven't tested it. So until we test it,

(53:53):
we cannot just assume that it's accurate enough. That's not
when people's lives are at staate. This is where that
my bias doesn't so much creep in as it kicks
open the door and makes itself at home on your couch.
But I digress. The GAO report also goes into great

(54:15):
detail about how this accuracy really can have a clear
impact on people's privacy, their civil liberties, their civil rights.
They also cite the Electronic Frontier Foundation the EFF which
says that if a person is brought up as a
defendant in a case and it is revealed that they
were matched by a facial recognition system, it puts a

(54:38):
burden on the defendant to argue that they are not
the same person as was seen in a probe photo,
that they are not the same one that the system
has identified. And if you cannot reliably state how accurate
your system is because you don't know how frequently it
returns false positives, you have unfairly burned And the defendant,

(55:01):
Like if you were to say, if you're the FBI,
and you say, we have an eighty six percent detection rate,
but you don't admit, oh, by the way, we don't
know how many false positives we get on any given search.
The implication you have given is that we're pretty sure
that this is the right guy. And again they argue
that this is meant to be a point of inquiry,

(55:23):
but you could easily see how it could also be
used by a lawyer to argue that a defendant is
in fact the person responsible for a crime, and they
may not be. And because you don't know the accuracy
of the system, you can't using the system to argue
for it is irresponsible. There's no accountability there. Now. Not

(55:48):
only has the FBI failed to establish the accuracy of
its own NGIIPS system, it has also not assessed the
accuracy of all those external databases that are used whenever
they use the face approach. There are no accuracy requirements
for these agencies, so there's not like a threshold they
have to prove that they meet in order to be

(56:09):
part of this. That's a huge problem. While each agency
might be accurate with no testing procedure, in place, it's
impossible to be certain of that. And since these databases
include millions of people with no criminal background and they
all use different facial recognition software products, this is a
huge issue. You could be put in a virtual lineup

(56:31):
simply because you look enough like someone else that a
computer thinks you are in fact the same person. The
GAO report concludes with a host of recommendations for future actions,
including addressing the problem of the FBI being so slow
to publish those updated pias in a timely manner, and
create a means to assess each system's accuracy. The Department

(56:53):
of Justice read the report and then responded disagreeing with
several points that the GOAO report made, including arguing that
the FBI and the Department of Justice published information when
it made the most sense, when the system had been
tweaked and finalized. More or less. However, by that time, again,

(57:14):
they had been using that system for real world cases
throughout the entire process, So it seems to me to
be kind of a weak argument. You can't really say, like, hey,
it wasn't finished until then, that's when we published it.
If you also are saying, hey, we use that for
real zees to go after actual people. You can't have

(57:34):
it both ways and not maintain accountability at any rate.
So that kind of gets to the end of the
Government Accountability Office report, but that's not the end of
the story. In March twenty seventeen, Congress held some hearings
about this, and boy howdy, were some congress people very

(57:55):
upset with the FBI. On both sides of the aisle.
You had Democrats and Republicans really chastising the FBI for
their use of facial recognition software and arguing that it
could amount to an enormous invasion of privacy as well
as endangering the civil liberties of US citizens. So people
who have dramatically different political philosophies were agreeing on this point.

(58:21):
So it wasn't really a partisan issue in this case,
and it got pretty ugly, but probably not as ugly
as the Georgetown University report that was published in late
twenty sixteen. This is an amazing report. Both the Government
Accountability Office report and the Georgetown University report are available
for free online. I will warn you collectively, they're about

(58:42):
two hundred pages, so if you want some light reading
you can check it out. They are quite good, both
of them. And they're very accessible. Neither of them are
written in crazy legallees which will make it impossible to understand.
They're written in very plain English, as in the Georgetown
University report that was revealed that one in every two

(59:03):
American adults has their picture contained in a database connected
to law enforcement facial recognition systems. And this report goes
far beyond just that FBI to state all the way
down to state and local systems that are implementing their
own facial recognition databases, and many of them have no
understanding of how it might impact the civil liberties or
privacy of citizens. The report is the summary of a

(59:27):
study that lasted a full year with more than one
hundred records requests to various police departments. They looked at
fifty two different law enforcement agencies across the United States,
and the report assessed the risks to civil liberties and
civil rights because up until this report was filed, no
such study had been made, which is a huge problem.

(59:48):
You don't know the impact of the tool that you've
created until after it's been put in use for a while.
That's an issue. Ideally, you think all this out before
you implement the procedure and their findings were pretty upsetting.
For example, the report found that some agencies limit themselves
to using facial recognition within the framework of a targeted

(01:00:09):
and public use, such as using it on someone who
has been legally arrested or detained for a crime. And
in this case, you're talking about totally above board approach.
You're assuming that everyone is following the law as regards
to apprehending and charging a suspect with a crime, and

(01:00:31):
maybe that person is unwilling or unable to tell you
what their identity is, and in that case, you would
use this facial recognition software stuff in order to figure
out who you are dealing with. That's largely a legitimate
case the government. The Georgetown University study didn't say that's bad.

(01:00:53):
They actually said, no, that makes sense. It's targeted, it's public.
But you could have a more invisible approach, for example,
using facial recognition software in real time on a closed
circuit camera pointed at a city street, where you're literally
picking up people as they pass by. They're not people

(01:01:14):
of interest, they're just people going about their day. And
if you're running facial recognition software on such a feed,
you are potentially invading privacy and stepping on civil rights
and civil liberties.

Speaker 2 (01:01:28):
Hey, it's modern day, Jonathan here just cutting end to
say we will have more about the National Facial Recognition
Database after this break.

Speaker 1 (01:01:45):
So even if you were to argue that this real
time use where you're just looking at people as they
pass by, and maybe a little name pops up every
now and then as it as the system recognizes a
person that matches a file in the database, it's easy
to a scenario in which such a technology could be abused.
Either it picks up somebody mistakenly, it thinks it identifies someone,

(01:02:09):
but in fact it's a totally different person, and then
you end up establishing a person's location by mistake, like
it's not really where they were, but because the system
has identified a person as being at X place at
why time, you then have established supposedly that person's location,

(01:02:30):
when in fact that person might be across town or
not even in the same state. But it's because of
a misidentification in the system. That's one problem. But think
about this. Think of this is a scary scenario. Imagine
a situation in which a group of people are discriminated
against by a government agency. Let's say they have a
legitimate gripe. It's completely legitimate. They're victims of unfair treatment.

(01:02:55):
So a group of them at some of their allies
get together in a public place for peaceful protest, to
raise awareness of this issue and to confront the government
agencies that have discriminated against them. This is all perfectly
legal according to the US Constitution. They're not doing anything legal.
They're assembling on public grounds in order to practice free speech.

(01:03:20):
But it's not hard to imagine a government agency using
a camera with this sort of facial recognition software to
identify people who are in the crowd in order to
use that as leverage in the future for some purpose
or another, even if it's just to say we know
you were there, and to put that kind of pressure
on a person in order to essentially squelch people's freedom

(01:03:46):
of speech. So this is a First Amendment issue, not
just a Fourth Amendment issue. Now that might sound like
a dramatic scenario like something like Big brother Ish. It's orwellian,
but it's also entirely within the realm of possibility. From
a technological standpoint, there's nothing technologically oriented that would prevent
us from doing this or prevent an agency from doing this,

(01:04:08):
and even without the evil empire scenario in place, you
still have the problematic issue of treading on civil liberties
just by having such technology available and unregulated. You don't
have rules to guide this sort of stuff. The Georgetown
report found that only one agency out of the fifty

(01:04:29):
two that they looked at have a specific rule against
using facial recognition software to identify people participating in public
demonstrations or free speech in general. So only one agency
actually has rules against that. Now, that doesn't mean the
other fifty one agencies are regularly using this technology to

(01:04:52):
monitor acts of free speech, but it also doesn't mean
that they can't. They don't have rules against it. Only
one a agency out of the fifty two, people are
being watched and identified without any connection to a crime.
In these cases, it's pretty terrifying. The Georgetown report also
found that no state had yet passed a law to

(01:05:13):
regulate police use of facial recognition software. No state in
the US. They're fifty of them, and none of them
have passed any regulations, any laws to regulate the use
of facial recognition software. So without rules, how do you
argue whether someone's misused or abused a system, you have
to have rules so that you know what is allowed

(01:05:35):
and what is not allowed. With no rules, the implication
is that everything's allowed until it isn't. That's a huge
dangerous problem. The report also pointed out that most of
these agencies lacked any sort of methodology to ensure that
the accuracy of their respective systems was decent. The report

(01:05:59):
stated that of all the agencies they investigated, only two,
the San Francisco Police Department and the South Sound nine
to one one from Seattle, had made decisions about what
facial recognition software they were going to incorporate in their
office based off of accuracy rates. That was not a

(01:06:20):
consideration for all of the other agencies, at least not
the ones that they asked. Moreover, they report points out
that facial recognition companies are also trying to have it
both ways. So, for example, they cite a company called
fat Face First. Now face First advertises that has a
ninety five percent accuracy rate, but it simultaneously disclaims any

(01:06:45):
liability for failing to meet that ninety five percent accuracy rate.
So it's kind of like saying we guarantee these tires.
Tires are not guaranteed not quite like that, but similar.
So again, this is according to the Georgetown University report,
that's a problem for a company to sell itself on

(01:07:08):
a performance threshold, but then say, hey, you can't hold
us to that performance threshold that we sold you on.
That's a little dangerous there too. Then the report goes
on to state that the human analysts, you know, the
ones I was talking about earlier, that supposed to be
a safeguard. Human analysts are supposed to take the images

(01:07:32):
that are returned by these automated systems and manually review
them to make sure that they do or do not
match that probe photo. That was the whole thing to
begin with. But it turns out, according to this report,
those human analysts are not that accurate. In fact, they're
no better than a coin flip. Literally. The report sites

(01:07:56):
of study that showed that if analysts did not have
highly specialized training, they would make the wrong decision for
a potential match fifty percent of the time. Literally a
coin flip. That's ridiculous. Now, the report found only eight
agencies out of the fifty two used specialized personnel to

(01:08:18):
review images. In other words, people who presumably have actually
received that highly specialized training necessary to make more accurate
decisions regarding these photos, and the report states that there's
no formal training regime in place for examiners, which is
a major problem for a system that's already in widespread use.
So not only do you need highly specialized training, there's

(01:08:40):
no formalized approach to give or receive that highly specialized training.
So we know you need it, but we haven't developed
the best practices to actually deliver upon that. So meanwhile,
you've got human analysts who are making mistakes half the
time while reviewing these photo And if you wonder if

(01:09:02):
facial recognition systems would disproportionately affect some ethnicities over others,
the answer to that is resounding and dismaying yes. The
report found that African Americans would be affected more than
other ethnicities. According to an FBI co authored study that
was cited by this Georgetown University report, several facial recognition

(01:09:24):
algorithms are less accurate for Black people than for other ethnicities,
and there's no independent testing process to determine if there's
a racial bias in any of these facial recognition systems,
so no one has developed a test to make certain
that it is in fact accurate despite a person's age, gender,

(01:09:45):
or race, without being able to verify that it is
accurate across all parameters, you have opened up an enormous
can of worms, and you are disproportionately affecting people just
because of the race, because your system does not address
that properly. The report also points out that the information

(01:10:07):
about the systems in use had not been generally available
to the public. In fact, all of the fifty two
agencies they contacted, only four had publicly available use policies. So,
in other words, only four of the fifty two could
tell you what their general policy was as far as
facial recognition software goes. That's less than ten percent of

(01:10:31):
all of the agencies they looked at, and only one
of those agencies, which was San Diego's Association of Governments,
had legislative approval for its policy. All the others were
just self appointed policies that had not passed through any
kind of official legislative support. Finally, the report asserted that
most of these systems did not have an official audit

(01:10:54):
process to determine if or when someone misuses the systems.
Nine agencies were or that they did have a process,
but only one provided Georgetown with any evidence that they
had a working audit system, and that was the Michigan
State Police, by the way, who said, we have an
audit system, and here's proof that it actually works the

(01:11:15):
way we said it did. So good on you, Michigan
State for our having that system in place and being
able to back it up now. The Georgetown University report
also urged some major changes in the way law enforcement
uses facial recognition, including an appeal to Congress to create
clear regulations to define the parameters of when such a
system could be used. They also called for companies to

(01:11:37):
publish processes that test their products accuracy regardless of race, gender,
and age, to remove that possibility of bias. And if
we're being really super kind and generous toward law enforcement,
we could say this is just another case where technology
has clearly outpaced the law. We see that all the time,

(01:11:59):
driverless artificial intelligence, lots of different technologies are advancing far
faster than legislation can keep up with. All right, that's fair,
we see it happen. However, it's particularly troublesome that this
is happening within law enforcement that is already employing this
technology before we've developed the policies to guide it. It's

(01:12:23):
one thing to say someone's out here working on a
driverless car, and we need to start thinking about how
are we going to regulate that in the future. Maybe
right now we say you aren't allowed to operate your
driverless car until we figured this out. That's fair. It's
another thing to say, there's this technology that could potentially
impact people's lives and we're allowing law enforcement to use

(01:12:45):
it while we try and figure out the rules. That's
at best a problem. And as I said at the
top of the show, I'm really just talking about the
United States with particulars here, but this is happening all
around the world. There are lots of governments around the
world that are incorporating facial recognition software along with law enforcement.

(01:13:08):
So while I'm using specific US examples in this podcast,
the same is true for lots of other places. Of course,
the laws that protect the citizens can be different from
country to country, and in some cases there might not
be very many outlets for citizens to voice their concern
or it might even be dangerous to do so. But

(01:13:30):
this is something I think we need to be aware of.
I'm not generally the kind of person who tells you
that you're being watched or you know, you should be paranoid.
But I'm also not the person to just sit back
and let something go on when I feel like it's
potentially more of a problem than a solution.

Speaker 2 (01:13:51):
Well that was it for the episode I did on
the National Facial Recognition Database back in twenty seventeen. It's
a topic I should definitely revisit. Obviously, there's so much
going on here. There are so many concerning things about it,
from surveillance states, to privacy and security concerns, to the
fact that we've been seeing lots of companies try and

(01:14:13):
use facial recognition to match people against databases to varying
degrees of success, and that for people of color in particular,
those degrees of success are not good. And I think
there's a lot that we need to talk about as
far as this goes, when it comes to things like,
you know, individual rights and authoritarian abuse of these kind

(01:14:37):
of technologies, and I think we do need to have
another update on this, so I will put that on
my list. I hope that you are all well, and
I'll talk to you again really soon. Tech stuff is
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(01:15:01):
Apple Podcasts, or wherever you listen to your favorite shows.