1 00:00:00,280 --> 00:00:04,720 Speaker 1: From UFOs to psychic powers and government conspiracies. History is 2 00:00:04,800 --> 00:00:09,119 Speaker 1: riddled with unexplained events. You can turn back now or 3 00:00:09,240 --> 00:00:12,399 Speaker 1: learn the stuff they don't want you to know. M 4 00:00:25,040 --> 00:00:27,280 Speaker 1: welcome back to the show. My name is Matt. They 5 00:00:27,360 --> 00:00:30,159 Speaker 1: called me Ben. You are you? And that makes this 6 00:00:30,280 --> 00:00:33,519 Speaker 1: stuff they don't want you to know. Listeners, you're probably 7 00:00:33,600 --> 00:00:37,520 Speaker 1: wondering whatever happened to the other guy on the show. 8 00:00:37,640 --> 00:00:41,479 Speaker 1: Don't worry. Our third amigo, Nol, will be back in 9 00:00:41,720 --> 00:00:45,840 Speaker 1: due time. Today, however, we've pulled a switcheroo on you. 10 00:00:45,840 --> 00:00:49,760 Speaker 1: You see, this is not the ordinary episode of stuff 11 00:00:49,760 --> 00:00:52,320 Speaker 1: they don't want you to know. Today we are joined 12 00:00:52,360 --> 00:00:56,080 Speaker 1: by a very special and returning guest, Ladies and gentlemen, 13 00:00:56,160 --> 00:00:59,920 Speaker 1: our resident tech expert, Jonathan Stricklands. I'd like to thank 14 00:01:00,040 --> 00:01:03,760 Speaker 1: This is an extraordinary episode of stuff they don't want 15 00:01:03,800 --> 00:01:06,720 Speaker 1: you to know, and uh, I am here to talk 16 00:01:06,760 --> 00:01:10,160 Speaker 1: about stuff they legit do not want you to know. 17 00:01:10,560 --> 00:01:13,720 Speaker 1: That's true, That's true, Jonathan, thank you for coming. You know. 18 00:01:13,920 --> 00:01:17,920 Speaker 1: One thing, one thing that we can say about Jonathan 19 00:01:18,040 --> 00:01:20,720 Speaker 1: off the bat is that he has a face that 20 00:01:20,880 --> 00:01:24,080 Speaker 1: is more recognizable than most. You host a show called 21 00:01:24,120 --> 00:01:27,240 Speaker 1: tech stuff. You host a show called forward thinking, You've 22 00:01:27,280 --> 00:01:32,040 Speaker 1: worked with everyday science on brain stuff, and you regularly 23 00:01:32,640 --> 00:01:36,200 Speaker 1: hit the streets on C S and E three. Yep, 24 00:01:36,319 --> 00:01:39,920 Speaker 1: these are all true. I I often will get maybe 25 00:01:39,959 --> 00:01:44,520 Speaker 1: not often, but frequently enough, get recognized simply because my 26 00:01:44,600 --> 00:01:47,520 Speaker 1: face has been in a lot of places and associated 27 00:01:47,560 --> 00:01:50,760 Speaker 1: directly with my identity. It's not just that, Hey, who's 28 00:01:50,800 --> 00:01:54,240 Speaker 1: that guy in the background of every Avengers movie or whatever. 29 00:01:54,560 --> 00:01:57,760 Speaker 1: I'm someone who my face is tied to who I 30 00:01:57,840 --> 00:02:02,120 Speaker 1: am frequently enough that people recognize me for the person 31 00:02:02,240 --> 00:02:06,960 Speaker 1: I am. Most people, I would argue, probably don't experience 32 00:02:07,000 --> 00:02:09,200 Speaker 1: that to any great degree, apart from you know, they're 33 00:02:09,240 --> 00:02:13,280 Speaker 1: they're close friends and acquaintances that sort of thing whatever, 34 00:02:13,440 --> 00:02:16,000 Speaker 1: and their EXAs. Yeah, the enemies that they have ranked 35 00:02:16,040 --> 00:02:18,800 Speaker 1: up as people may or may not be aware. Ben 36 00:02:18,840 --> 00:02:23,040 Speaker 1: and I are longtime enemies. I declared you as such 37 00:02:23,200 --> 00:02:25,960 Speaker 1: within my first couple of months of working at How 38 00:02:26,000 --> 00:02:28,600 Speaker 1: Stuff Works. Well. Yeah, but also, to be fair, I 39 00:02:28,639 --> 00:02:33,840 Speaker 1: push people. Matt can attest to that. So most people 40 00:02:33,880 --> 00:02:37,200 Speaker 1: would think there's not a real great chance they would 41 00:02:37,240 --> 00:02:40,200 Speaker 1: be recognized whenever they go out in public. But here's 42 00:02:40,240 --> 00:02:42,280 Speaker 1: an interesting fact that you may or may not know. 43 00:02:42,400 --> 00:02:46,280 Speaker 1: In the United States, one out of every two adults 44 00:02:47,200 --> 00:02:52,560 Speaker 1: is actually their faces actually tied to their identity in 45 00:02:52,760 --> 00:02:59,000 Speaker 1: some law enforcements database. That law enforcement agency might be local, 46 00:02:59,280 --> 00:03:03,799 Speaker 1: might be federal. Most of them are linked together loosely 47 00:03:04,080 --> 00:03:06,840 Speaker 1: in the sense that if one law enforcement agency wants 48 00:03:06,840 --> 00:03:13,000 Speaker 1: to run a recognition UH test on a suspect photo, 49 00:03:13,600 --> 00:03:17,160 Speaker 1: they can get that done within other law enforcement agencies. 50 00:03:17,200 --> 00:03:21,800 Speaker 1: So you can, in theory, have half of the adult 51 00:03:21,840 --> 00:03:25,799 Speaker 1: population of the United States as your virtual lineup when 52 00:03:25,840 --> 00:03:29,320 Speaker 1: you are looking for a match on a photo. So 53 00:03:29,480 --> 00:03:32,520 Speaker 1: if you are an adult in the United States, there's 54 00:03:32,560 --> 00:03:35,440 Speaker 1: a coin flip chance that your faces in one of 55 00:03:35,480 --> 00:03:38,240 Speaker 1: these databases. If you've committed a crime, or if you've 56 00:03:38,240 --> 00:03:41,400 Speaker 1: been accused of committing a crime in the United States, 57 00:03:41,760 --> 00:03:44,480 Speaker 1: there's essentially a chance that your face is in one 58 00:03:44,480 --> 00:03:47,840 Speaker 1: of these databases. So that's what we're talking about today. 59 00:03:47,920 --> 00:03:50,000 Speaker 1: Ladies and gentlemen. You will remember at the close of 60 00:03:50,000 --> 00:03:54,360 Speaker 1: our previous episode, we asked you to consider how many 61 00:03:54,400 --> 00:03:57,480 Speaker 1: times your face has appeared on a camera? Right, how 62 00:03:57,480 --> 00:04:01,360 Speaker 1: many times a day does a camera perceive your visage. 63 00:04:01,880 --> 00:04:05,160 Speaker 1: I would argue that a lot of people don't want 64 00:04:05,400 --> 00:04:10,920 Speaker 1: to be recognized casually. For one reason or another perfectly legitimate, 65 00:04:11,200 --> 00:04:17,200 Speaker 1: perfectly legal reasons. Yeah, if I'm if I'm walking around 66 00:04:17,320 --> 00:04:20,880 Speaker 1: a place and I just want to duck into a store, 67 00:04:21,200 --> 00:04:23,919 Speaker 1: I don't necessarily want the entire world to know that 68 00:04:24,040 --> 00:04:26,320 Speaker 1: I went into that store. Let's say that I'm shopping 69 00:04:26,440 --> 00:04:29,760 Speaker 1: for a surprise gift for my wife, totally innocent, nothing 70 00:04:29,800 --> 00:04:34,200 Speaker 1: wrong about that. Then I don't necessarily want that information 71 00:04:34,200 --> 00:04:36,360 Speaker 1: to get back to my wife and thus ruin the surprise. 72 00:04:37,000 --> 00:04:40,320 Speaker 1: Or it could be, it could be anything that you 73 00:04:40,360 --> 00:04:43,440 Speaker 1: would argue is on the further end of the spectrum. 74 00:04:43,480 --> 00:04:49,600 Speaker 1: At any rate, we have a certain expectation of privacy 75 00:04:49,760 --> 00:04:52,159 Speaker 1: in here in the United States that I think is 76 00:04:52,240 --> 00:04:55,240 Speaker 1: largely based off of wishful thinking at this point. So 77 00:04:55,480 --> 00:05:03,920 Speaker 1: what is now phil esophagal Yeah, waxing about privacy or 78 00:05:03,960 --> 00:05:06,760 Speaker 1: the nature of it or how long it's existed. Is Uh, 79 00:05:08,200 --> 00:05:10,960 Speaker 1: it's sort of a hobby Matt and I on the show. 80 00:05:11,760 --> 00:05:15,880 Speaker 1: I guess we should start, as the Mad Hatter said 81 00:05:15,920 --> 00:05:19,520 Speaker 1: at the beginning, Sure, So what what do we mean 82 00:05:19,560 --> 00:05:23,840 Speaker 1: when we say facial recognition? What is this? So this 83 00:05:23,960 --> 00:05:27,000 Speaker 1: was something that people were starting to work on very 84 00:05:27,080 --> 00:05:30,599 Speaker 1: seriously in the nineties and early two thousand's because it 85 00:05:30,680 --> 00:05:33,880 Speaker 1: required a great deal of processing power back in those 86 00:05:33,960 --> 00:05:37,000 Speaker 1: days to achieve what now we can do much more 87 00:05:37,000 --> 00:05:41,040 Speaker 1: simply with new types of computer architecture. But your basic 88 00:05:42,279 --> 00:05:45,559 Speaker 1: approach has been the same, which is that you create 89 00:05:45,600 --> 00:05:50,800 Speaker 1: a program and algorithm that identifies special anchor points on 90 00:05:50,880 --> 00:05:55,400 Speaker 1: an image, such as a face, and identifies this looks 91 00:05:55,400 --> 00:05:57,640 Speaker 1: like an eye. So I'm going to assume that this 92 00:05:57,720 --> 00:06:00,400 Speaker 1: is an eye. So here's the other eye, Here the nose, 93 00:06:00,480 --> 00:06:05,120 Speaker 1: here's the mouth. Based upon the relationship of these various 94 00:06:05,120 --> 00:06:09,839 Speaker 1: components within a face, I would assign a numeric value 95 00:06:10,480 --> 00:06:13,440 Speaker 1: more or less a numeric value to this. It's called 96 00:06:13,440 --> 00:06:17,000 Speaker 1: a face print, but it's essentially the sum total of 97 00:06:17,040 --> 00:06:21,400 Speaker 1: all the different features that that particular algorithm is looking for. 98 00:06:21,600 --> 00:06:23,760 Speaker 1: And just to get this out of the way, there 99 00:06:23,760 --> 00:06:25,880 Speaker 1: are a lot of different algorithms out there by a 100 00:06:25,920 --> 00:06:28,360 Speaker 1: lot of different companies, so not everyone does it exactly 101 00:06:28,400 --> 00:06:31,599 Speaker 1: the same way. Everyone claims that there's the best, but 102 00:06:31,720 --> 00:06:36,039 Speaker 1: they all use similar but not identical methods. You then 103 00:06:36,760 --> 00:06:40,240 Speaker 1: take this information, this face print, this numeric value you 104 00:06:40,279 --> 00:06:44,720 Speaker 1: have created, and you run it against a database of 105 00:06:45,040 --> 00:06:49,680 Speaker 1: previous face prints that are tied to known individuals so 106 00:06:49,800 --> 00:06:53,840 Speaker 1: the picture you take is called a probe photo. That is, 107 00:06:53,920 --> 00:06:56,200 Speaker 1: the photo you are probing the database to see if 108 00:06:56,240 --> 00:06:58,760 Speaker 1: you can find a match. You look and see if 109 00:06:58,800 --> 00:07:01,640 Speaker 1: you find a close nume wreck match between the two 110 00:07:02,200 --> 00:07:05,280 Speaker 1: the probe photo and all of your your collected images 111 00:07:05,320 --> 00:07:07,600 Speaker 1: that are in your database that you have, you know 112 00:07:07,640 --> 00:07:11,920 Speaker 1: who those belong to. So photo pro photo is of 113 00:07:11,960 --> 00:07:14,520 Speaker 1: a person of interest. Might not be a suspect, but 114 00:07:14,640 --> 00:07:16,680 Speaker 1: it's certainly someone you would like to talk to if 115 00:07:16,720 --> 00:07:20,320 Speaker 1: you had a chance, because they're involved in a investigation 116 00:07:20,320 --> 00:07:24,480 Speaker 1: in some way. You look against the database, you try 117 00:07:24,480 --> 00:07:28,080 Speaker 1: and find a match. Numerically. At that point, at least 118 00:07:28,120 --> 00:07:31,200 Speaker 1: with most systems, but not withal you would then have 119 00:07:31,400 --> 00:07:35,440 Speaker 1: a human being, a real life person, comparing these images 120 00:07:35,480 --> 00:07:38,360 Speaker 1: to each other to see if they actually represent a 121 00:07:38,400 --> 00:07:41,480 Speaker 1: match of this photo looks like yes, it is in 122 00:07:41,520 --> 00:07:43,960 Speaker 1: fact the person who's in the database. Because people can 123 00:07:44,000 --> 00:07:47,760 Speaker 1: look very similar. People can look very similar, and but 124 00:07:47,920 --> 00:07:51,800 Speaker 1: depending upon how the algorithm breaks down the face, it 125 00:07:51,880 --> 00:07:56,520 Speaker 1: may mistakenly think that two people are the same, even 126 00:07:56,560 --> 00:07:58,920 Speaker 1: if you were to look at them in person and say, well, 127 00:07:58,920 --> 00:08:01,080 Speaker 1: that's clearly not the same person. I mean, I can 128 00:08:01,120 --> 00:08:03,800 Speaker 1: tell that's not the same person. There are times where 129 00:08:03,800 --> 00:08:07,040 Speaker 1: that happens. Uh. It also has a lot of other factors, 130 00:08:07,120 --> 00:08:10,360 Speaker 1: such as the picture you take is the person facing 131 00:08:10,560 --> 00:08:13,040 Speaker 1: face onto the camera. If it's at a slight angle, 132 00:08:13,120 --> 00:08:16,480 Speaker 1: that changes things as well. Right. Uh, there have been 133 00:08:16,600 --> 00:08:20,400 Speaker 1: advances in algorithms where it's able to take better guesses 134 00:08:20,440 --> 00:08:24,360 Speaker 1: as to the three dimensionality of a face. But previously 135 00:08:24,920 --> 00:08:27,480 Speaker 1: it was very much a two dimensional image, which meant 136 00:08:27,480 --> 00:08:31,200 Speaker 1: that you could fool it. You could fool it either 137 00:08:31,240 --> 00:08:34,439 Speaker 1: on purpose or by accident. So this doesn't have to 138 00:08:34,480 --> 00:08:38,520 Speaker 1: do with law enforcement. But in Japan they incorporated facial 139 00:08:38,559 --> 00:08:43,440 Speaker 1: recognition software in vending machines that sold cigarettes because the 140 00:08:43,480 --> 00:08:46,720 Speaker 1: idea was that if you appeared to be too young, 141 00:08:47,520 --> 00:08:50,200 Speaker 1: the vending machine wouldn't sell cigarettes to you. But if 142 00:08:50,240 --> 00:08:53,960 Speaker 1: you just held up a photo of someone who was 143 00:08:54,080 --> 00:08:57,240 Speaker 1: old enough to purchase cigarettes, it could. Because it couldn't 144 00:08:57,240 --> 00:09:02,080 Speaker 1: detect depth, it was entirely just two dimensionals. Is this 145 00:09:02,200 --> 00:09:05,800 Speaker 1: a face of a person over what is eighteen or whatever? Right? 146 00:09:05,880 --> 00:09:08,640 Speaker 1: And now it's gotten to a point where because of 147 00:09:08,760 --> 00:09:10,800 Speaker 1: camera sophistication, there are a lot of cameras that can 148 00:09:10,840 --> 00:09:14,600 Speaker 1: incorporate two lenses, which gives it that that by optic 149 00:09:15,480 --> 00:09:18,800 Speaker 1: a view, a parallax view that can simulate Yeah, exactly, 150 00:09:19,000 --> 00:09:22,120 Speaker 1: you can simulate that three dimensional vision. And there are 151 00:09:22,120 --> 00:09:24,920 Speaker 1: algorithms that can take advantage of that. That has been 152 00:09:24,960 --> 00:09:28,560 Speaker 1: decreased somewhat. It is not as easy to fool at 153 00:09:28,600 --> 00:09:33,599 Speaker 1: least a sophisticated machine, but it can still happen now earlier, 154 00:09:33,760 --> 00:09:39,240 Speaker 1: at the very beginning. Yeah, you gave everybody a pretty 155 00:09:39,480 --> 00:09:43,880 Speaker 1: harrowing statistic. Yeah, the what did you call a coin 156 00:09:43,920 --> 00:09:46,400 Speaker 1: flip chance. Yes, there's a coin flip chance that if 157 00:09:46,440 --> 00:09:48,120 Speaker 1: you are an adult in the US, your face is 158 00:09:48,160 --> 00:09:51,760 Speaker 1: in one of these database. If you've never done anything wrong. Yeah, 159 00:09:51,800 --> 00:09:53,880 Speaker 1: you don't have to do anything, we really, all you 160 00:09:53,920 --> 00:09:56,960 Speaker 1: have to have done is applied for some sort of 161 00:09:57,120 --> 00:10:05,240 Speaker 1: license or accreditation that also includes your photo, passport, driver's license, 162 00:10:05,320 --> 00:10:07,880 Speaker 1: State I D. There are a lot of security clearances 163 00:10:07,880 --> 00:10:10,680 Speaker 1: that do this. So let's let's back up a little 164 00:10:10,679 --> 00:10:13,200 Speaker 1: bit and and let me explain the landscape because it 165 00:10:13,240 --> 00:10:16,480 Speaker 1: is complicated, right, It's not as simple as there's this 166 00:10:16,559 --> 00:10:20,040 Speaker 1: one massive computer system at Langley and that's where your 167 00:10:20,040 --> 00:10:23,120 Speaker 1: face is. It's much more distributed than that, much like 168 00:10:23,160 --> 00:10:28,320 Speaker 1: the Internet itself. So the FBI has the interstate Photos system. 169 00:10:28,320 --> 00:10:32,840 Speaker 1: This was sort of a an evolution of their national 170 00:10:33,000 --> 00:10:36,080 Speaker 1: fingerprint database. And I think you would say, all right, well, 171 00:10:36,080 --> 00:10:39,600 Speaker 1: I understand the fingerprint database. They've collected fingerprints from various 172 00:10:39,640 --> 00:10:43,840 Speaker 1: criminal investigations. This speeds things along when they are investigating 173 00:10:43,840 --> 00:10:48,120 Speaker 1: a new crime. This, however, is more about faces, less 174 00:10:48,120 --> 00:10:52,480 Speaker 1: about fingerprints. So now what we're looking at is a 175 00:10:52,559 --> 00:10:56,880 Speaker 1: system where if the FBI uses their normal method of operations, 176 00:10:58,200 --> 00:11:00,680 Speaker 1: in theory, at least you probably you would feel a 177 00:11:00,720 --> 00:11:05,040 Speaker 1: little less creepy about all this. And that's because if 178 00:11:05,040 --> 00:11:08,080 Speaker 1: they're using their basic method of running a search, what 179 00:11:08,200 --> 00:11:11,040 Speaker 1: the FBI would do is take a probe photo from 180 00:11:11,080 --> 00:11:15,440 Speaker 1: an investigation. They would then run it against their database, 181 00:11:15,440 --> 00:11:20,559 Speaker 1: which includes both criminal and civil photos. The civil photos, 182 00:11:20,600 --> 00:11:23,680 Speaker 1: some of those are tied to some of the criminal photos. 183 00:11:23,679 --> 00:11:25,920 Speaker 1: If they have the same picture of a person from 184 00:11:25,960 --> 00:11:29,960 Speaker 1: a criminal investigation and from say a license that they 185 00:11:30,120 --> 00:11:32,960 Speaker 1: had applied for that then both of those will be 186 00:11:32,960 --> 00:11:36,000 Speaker 1: tied together in the database. When they run a search, 187 00:11:36,160 --> 00:11:39,120 Speaker 1: the search only looks for matches in the criminal side. 188 00:11:39,440 --> 00:11:42,160 Speaker 1: It cannot look for matches on the civil side. So 189 00:11:42,320 --> 00:11:47,800 Speaker 1: if your ideas in the FBI's UH interstate photosystem. But 190 00:11:47,880 --> 00:11:52,079 Speaker 1: it's only in there as an example of a civil photo, 191 00:11:52,120 --> 00:11:54,640 Speaker 1: and you have no criminal record to tie it to, 192 00:11:55,920 --> 00:11:59,400 Speaker 1: it should not pull you as a result. So this 193 00:11:59,520 --> 00:12:03,880 Speaker 1: leads to an immediate question. We're talking about the division 194 00:12:03,960 --> 00:12:09,800 Speaker 1: between civil and criminal images here facial images, at least 195 00:12:09,800 --> 00:12:13,640 Speaker 1: as it pertains to the FBI. Do they not automatically 196 00:12:13,679 --> 00:12:19,840 Speaker 1: correlate because of a matter of ability or because of 197 00:12:19,880 --> 00:12:25,079 Speaker 1: a matter of legislation? They do it as a policy 198 00:12:25,160 --> 00:12:28,280 Speaker 1: saying we we don't want to, we don't want to 199 00:12:28,320 --> 00:12:30,760 Speaker 1: bring in the civil photos. Trust us. But this was 200 00:12:30,800 --> 00:12:33,920 Speaker 1: the basic search. I haven't gotten to their other search, 201 00:12:34,559 --> 00:12:38,080 Speaker 1: and we'll dive into that after a word from our 202 00:12:38,200 --> 00:12:54,120 Speaker 1: sponsor and we're back. So for everybody who is just 203 00:12:54,360 --> 00:12:57,000 Speaker 1: now becoming acquainted with us, we've we've heard all of 204 00:12:57,000 --> 00:13:00,600 Speaker 1: this stuff people have seen like crime show c S 205 00:13:00,600 --> 00:13:05,199 Speaker 1: I or whatever, and uh, facial recognition and action films 206 00:13:05,320 --> 00:13:10,480 Speaker 1: and facial recognition and science fiction. And what we've talked 207 00:13:10,480 --> 00:13:15,720 Speaker 1: about so far is a basic search. We've explained the technology, 208 00:13:16,400 --> 00:13:19,440 Speaker 1: and we've explained a little bit about the evolution. I 209 00:13:19,440 --> 00:13:22,920 Speaker 1: think it would surprise people how quickly this has evolved. 210 00:13:25,240 --> 00:13:31,120 Speaker 1: But what's after the basic search? So all right, the 211 00:13:31,240 --> 00:13:36,160 Speaker 1: FBI S General m O is using this this criminal 212 00:13:36,240 --> 00:13:41,120 Speaker 1: database and avoiding the civil database uh when they're doing 213 00:13:41,200 --> 00:13:45,600 Speaker 1: their own searches. But they also have a division called 214 00:13:45,600 --> 00:13:51,480 Speaker 1: the Facial Analysis, Comparison and Evaluation Services or FACES. I 215 00:13:51,800 --> 00:13:55,000 Speaker 1: want to do the acronymic FACES is kind of like 216 00:13:55,040 --> 00:13:58,640 Speaker 1: there their specialty division where they want to run a 217 00:13:58,679 --> 00:14:04,520 Speaker 1: more uh more extensive search. So the FACES operators can 218 00:14:04,640 --> 00:14:07,760 Speaker 1: run a search about that that incorporates both criminal and 219 00:14:07,880 --> 00:14:13,600 Speaker 1: civil uh sources, and they can work with local state 220 00:14:13,720 --> 00:14:16,760 Speaker 1: law enforcement agencies, will not just state, but tribal law 221 00:14:16,880 --> 00:14:19,880 Speaker 1: enforcement agencies as well. In the United States, they have 222 00:14:21,560 --> 00:14:25,400 Speaker 1: a they have this ability to work with all of them, 223 00:14:25,440 --> 00:14:27,600 Speaker 1: to partner with them. Um, it's kind of a tip 224 00:14:27,640 --> 00:14:29,880 Speaker 1: for tat sort of approach. Whenever anyone needs to run 225 00:14:29,880 --> 00:14:32,120 Speaker 1: one of these, they can run it up the chain 226 00:14:32,240 --> 00:14:35,200 Speaker 1: and see if they can get a bigger net to 227 00:14:35,200 --> 00:14:39,280 Speaker 1: to pull from. Right, So what will happen is you 228 00:14:39,320 --> 00:14:42,880 Speaker 1: would send a request to the FACES department. They could 229 00:14:42,960 --> 00:14:45,480 Speaker 1: run it against not only the FBI's database, which is 230 00:14:46,000 --> 00:14:49,840 Speaker 1: it's extensive, but that is not the one in every 231 00:14:49,880 --> 00:14:54,960 Speaker 1: two Americans databases that that statistic comes from the collection 232 00:14:55,120 --> 00:14:58,480 Speaker 1: of databases across the United States at all different levels 233 00:14:58,520 --> 00:15:03,920 Speaker 1: of law enforcements, So against state, tribal, city, UH, federal, 234 00:15:04,000 --> 00:15:06,800 Speaker 1: all of those different levels combined, that's where you get 235 00:15:06,800 --> 00:15:10,800 Speaker 1: the one in two. But the FBI essentially has access 236 00:15:10,840 --> 00:15:14,000 Speaker 1: to most of that because of these relationships they've developed 237 00:15:14,000 --> 00:15:17,160 Speaker 1: with various other entities. So you would send a request 238 00:15:17,280 --> 00:15:23,600 Speaker 1: to FACES. FACES would then handle the request sent out 239 00:15:23,640 --> 00:15:27,240 Speaker 1: to other agencies, right so within they would do the 240 00:15:27,240 --> 00:15:30,040 Speaker 1: the FBI search, but they would also say, hey, State 241 00:15:30,040 --> 00:15:33,400 Speaker 1: of Utah, can you do this search? City of Las 242 00:15:33,480 --> 00:15:35,560 Speaker 1: Vegas can you do this search? You know what we're 243 00:15:35,560 --> 00:15:38,520 Speaker 1: gonna expand this, California can you do this search? And 244 00:15:38,560 --> 00:15:43,520 Speaker 1: you start again casting a wider net. Well, you start 245 00:15:43,560 --> 00:15:48,240 Speaker 1: running into problems very very quickly with this approach. Well, 246 00:15:48,800 --> 00:15:52,800 Speaker 1: multiple multiple avenues. Let's start with technology. You remember earlier 247 00:15:52,840 --> 00:15:57,880 Speaker 1: I mentioned that no, no facial recognition algorithm does the 248 00:15:58,040 --> 00:16:00,360 Speaker 1: approach the exact same way as a different at one, 249 00:16:00,480 --> 00:16:04,760 Speaker 1: right like, So it's not like there's just one way 250 00:16:05,000 --> 00:16:09,120 Speaker 1: to achieve facial recognition technology and some may be better 251 00:16:09,160 --> 00:16:12,160 Speaker 1: than others. And the real problem is that we don't 252 00:16:12,400 --> 00:16:16,240 Speaker 1: really know because most of these companies haven't had a 253 00:16:16,320 --> 00:16:19,720 Speaker 1: third party come in and evaluate the software for accuracy. 254 00:16:20,080 --> 00:16:23,440 Speaker 1: So there's no UM, there's no quality assurance, there's no 255 00:16:23,480 --> 00:16:29,240 Speaker 1: accountability at all. So one from a technology standpoint, you 256 00:16:29,280 --> 00:16:32,920 Speaker 1: cannot be certain that everyone is on the same playing ground, 257 00:16:32,960 --> 00:16:35,080 Speaker 1: like they're on the same level. They may all be 258 00:16:35,200 --> 00:16:38,520 Speaker 1: using different technologies, some of which may be more effective 259 00:16:38,560 --> 00:16:41,840 Speaker 1: than others. That's problem number one. Just to add on 260 00:16:41,920 --> 00:16:48,720 Speaker 1: to that, one of the larger problematic trends in many 261 00:16:49,040 --> 00:16:55,120 Speaker 1: uh many government agencies attempt to implement technology is the 262 00:16:55,360 --> 00:17:01,400 Speaker 1: arrival and persistence of outdated legacy stuff because they're locked 263 00:17:01,400 --> 00:17:06,320 Speaker 1: into some service agreement or contract for or they just 264 00:17:06,359 --> 00:17:09,320 Speaker 1: don't have budget for. Like if you're running if you're 265 00:17:09,359 --> 00:17:14,199 Speaker 1: running old servers, old computers, and they aren't capable of 266 00:17:14,320 --> 00:17:19,400 Speaker 1: running the most up to date versions of software that's 267 00:17:19,440 --> 00:17:24,480 Speaker 1: out there, then yeah, that's yet another layer of technology issues. Yes, so, 268 00:17:25,480 --> 00:17:29,840 Speaker 1: ladies and gentlemen, if this sounds kind of weird to you, um, 269 00:17:30,000 --> 00:17:34,320 Speaker 1: one of the one of the moments in American pop 270 00:17:34,359 --> 00:17:41,719 Speaker 1: culture when the average non military, non law enforcement officer 271 00:17:41,760 --> 00:17:44,960 Speaker 1: person learned about this was actually watching the wire when 272 00:17:45,000 --> 00:17:48,240 Speaker 1: they had to use typewriters. Do you remember that? And 273 00:17:48,359 --> 00:17:52,000 Speaker 1: this is just another example of this. So not only 274 00:17:52,640 --> 00:17:59,720 Speaker 1: are their various forms of facial recognition algorithms and technology, 275 00:18:00,080 --> 00:18:03,800 Speaker 1: some of which may be proprietary, all of it is proprietary. 276 00:18:03,920 --> 00:18:08,959 Speaker 1: And not only did these not all let's play nice together, 277 00:18:09,000 --> 00:18:14,440 Speaker 1: I guess or agree at times. But some are I'm 278 00:18:14,560 --> 00:18:19,719 Speaker 1: overwhelmingly certain. Some are pretty outmoded. Yeah, effectively obsolete. Some 279 00:18:19,760 --> 00:18:23,879 Speaker 1: of them are. And again you don't necessarily know unless 280 00:18:23,920 --> 00:18:27,720 Speaker 1: you are getting regular updates from all the different agencies 281 00:18:27,760 --> 00:18:30,320 Speaker 1: out there. There are thousands of these law enforcement agencies 282 00:18:30,400 --> 00:18:32,960 Speaker 1: right Like, it's not we're not talking about others. There's 283 00:18:32,960 --> 00:18:35,200 Speaker 1: fifty of them, because there's fifty states. No, there's way 284 00:18:35,320 --> 00:18:37,960 Speaker 1: more than that. And you're talking about databases that are 285 00:18:38,000 --> 00:18:40,840 Speaker 1: not necessarily directly connected to one another. So each one 286 00:18:41,000 --> 00:18:43,679 Speaker 1: is its own little island with its own little like. 287 00:18:43,760 --> 00:18:46,240 Speaker 1: Some of them may be using the same facial recognition software. 288 00:18:46,359 --> 00:18:48,679 Speaker 1: Some of them may be using different versions of the 289 00:18:48,720 --> 00:18:53,760 Speaker 1: same vendor software. Now, if if they all worked, if 290 00:18:53,800 --> 00:18:57,040 Speaker 1: all the facial recognition software worked flawlessly, this would not 291 00:18:57,080 --> 00:18:58,760 Speaker 1: be an issue. It wouldn't matter if you went and 292 00:18:58,800 --> 00:19:01,600 Speaker 1: bought from Company A versus Company B, or if you 293 00:19:01,640 --> 00:19:04,480 Speaker 1: had Company a's one point over version versus company as 294 00:19:04,520 --> 00:19:07,560 Speaker 1: one point five version. If they all work perfectly, that's 295 00:19:07,560 --> 00:19:10,359 Speaker 1: not an issue. But we don't know because we haven't 296 00:19:10,400 --> 00:19:14,120 Speaker 1: had these third party audits for a lot of those technologies. 297 00:19:14,160 --> 00:19:16,359 Speaker 1: You get a lot of claims, Like you'll see a 298 00:19:16,359 --> 00:19:20,200 Speaker 1: company say we're accurate, and then you say, well, well, 299 00:19:20,200 --> 00:19:23,720 Speaker 1: where's the proof of that? And they say, trust us, 300 00:19:23,760 --> 00:19:25,800 Speaker 1: And then you say, well, can we hold you to 301 00:19:25,880 --> 00:19:27,840 Speaker 1: that in court? And like, oh no, our terms of 302 00:19:27,880 --> 00:19:31,600 Speaker 1: service say you can't. We can't be legally held accountable 303 00:19:31,640 --> 00:19:34,440 Speaker 1: for the claim that we make that it is accurate. 304 00:19:35,040 --> 00:19:37,520 Speaker 1: That's an issue, right, So that's all technology. There's another 305 00:19:37,560 --> 00:19:43,320 Speaker 1: technology issue that gets super uncomfortable. What's that most of 306 00:19:43,320 --> 00:19:47,800 Speaker 1: the time, facial recognition software is not equally accurate across 307 00:19:47,840 --> 00:19:55,119 Speaker 1: all ethnicities because the way people choose these algorithms and 308 00:19:55,160 --> 00:19:57,399 Speaker 1: the way they designed the faces, you know, the facial 309 00:19:57,440 --> 00:20:00,800 Speaker 1: recognition that the elements that they're looking for, or it 310 00:20:00,840 --> 00:20:03,200 Speaker 1: may be that in one ethnicity you see a greater 311 00:20:03,280 --> 00:20:08,160 Speaker 1: variety of the features that were chosen to be the 312 00:20:08,240 --> 00:20:12,200 Speaker 1: elements that you're looking for versus others. So within one 313 00:20:12,240 --> 00:20:16,240 Speaker 1: ethnicity you might see a greater variation in something like cheekbones. 314 00:20:16,320 --> 00:20:21,399 Speaker 1: Let's say the cheekbone size, the cheekbone, like the prominence. Yeah, 315 00:20:22,080 --> 00:20:24,560 Speaker 1: that kind of stuff might be really important with or 316 00:20:24,600 --> 00:20:26,960 Speaker 1: at least there might be a greater variety within one 317 00:20:27,040 --> 00:20:30,720 Speaker 1: ethnicity and less in another. Well, if your technology is 318 00:20:30,760 --> 00:20:34,520 Speaker 1: focusing on that and it doesn't focus so much on 319 00:20:34,560 --> 00:20:37,840 Speaker 1: the other qualities that have a greater variety within a 320 00:20:37,880 --> 00:20:40,879 Speaker 1: different ethnicity, you're gonna get a lot of false positives 321 00:20:41,440 --> 00:20:43,719 Speaker 1: because the technology you're looking at, if it's looking at 322 00:20:43,760 --> 00:20:47,359 Speaker 1: a subject from that ethnicity that doesn't have great representation 323 00:20:47,400 --> 00:20:50,320 Speaker 1: in your technology, you're gonna get a lot of false 324 00:20:50,640 --> 00:20:55,280 Speaker 1: positive identifications because the tech can It's like, it's like 325 00:20:55,520 --> 00:20:58,560 Speaker 1: that that statement about someone being racist. They cannot tell 326 00:20:58,640 --> 00:21:01,840 Speaker 1: the difference between two different individuals from the same race. 327 00:21:01,880 --> 00:21:04,720 Speaker 1: They don't see the difference because they have become so 328 00:21:06,200 --> 00:21:11,240 Speaker 1: ethnically minded of their own ethnicity. They only recognize the 329 00:21:11,240 --> 00:21:15,760 Speaker 1: differences that are representative of their respect by ethnicity. And 330 00:21:15,960 --> 00:21:21,320 Speaker 1: this is what's what's fascinating and terrifying about this. I 331 00:21:21,880 --> 00:21:25,160 Speaker 1: think the most immediate fascinating and terrifying thing is that 332 00:21:25,200 --> 00:21:30,960 Speaker 1: we see again that software is limited by the creator. 333 00:21:31,119 --> 00:21:37,480 Speaker 1: Technology is limited by we all make these amazing tools 334 00:21:38,520 --> 00:21:41,600 Speaker 1: based on our own parameters, we very quickly make an 335 00:21:41,600 --> 00:21:45,119 Speaker 1: assumption that something that's that's working on what appears to 336 00:21:45,160 --> 00:21:48,560 Speaker 1: be cold, hard logic is infallible, right, but that that 337 00:21:48,640 --> 00:21:53,919 Speaker 1: emotion that things like uh A rationality don't play a 338 00:21:53,960 --> 00:21:55,760 Speaker 1: factor except for the fact that they played a factor 339 00:21:55,800 --> 00:21:59,200 Speaker 1: when we made this stuff, and it didn't necessarily come 340 00:21:59,240 --> 00:22:02,560 Speaker 1: from a conscious decision. It may have been something that 341 00:22:02,680 --> 00:22:05,520 Speaker 1: just in the design elements, people were trying to figure out, 342 00:22:05,520 --> 00:22:07,879 Speaker 1: all right, well, what components are the ones we need 343 00:22:07,920 --> 00:22:10,280 Speaker 1: to really focus on in order for our technology to work, 344 00:22:10,320 --> 00:22:12,800 Speaker 1: And some guy leaned in, took off his clan hood 345 00:22:12,840 --> 00:22:15,680 Speaker 1: and said, cheek bones. Yeah, that's a very I mean, 346 00:22:15,920 --> 00:22:19,280 Speaker 1: probably not quite as a as extreme as that, but 347 00:22:19,680 --> 00:22:24,000 Speaker 1: the point being that it there have been some some claims, 348 00:22:24,040 --> 00:22:26,879 Speaker 1: at least I don't know about any deep studies, but 349 00:22:27,000 --> 00:22:33,320 Speaker 1: claims that facial recognition technology disproportionately affects people of UH 350 00:22:33,800 --> 00:22:39,520 Speaker 1: specific ethnicities, particularly black people. They have been uh disproportionately 351 00:22:39,520 --> 00:22:43,840 Speaker 1: affected by this false positives. Yeah, absolutely, yeah. And and 352 00:22:43,880 --> 00:22:48,000 Speaker 1: also there it's that goes down a road that is 353 00:22:49,080 --> 00:22:51,560 Speaker 1: far beyond the scope of this episode, because we'd have 354 00:22:51,600 --> 00:22:56,800 Speaker 1: to start talking about the vast gap uh in the 355 00:22:56,880 --> 00:22:59,440 Speaker 1: experience of being say, a white person in the United 356 00:22:59,480 --> 00:23:03,159 Speaker 1: States and being the subject of a law enforcement investigation 357 00:23:03,200 --> 00:23:06,600 Speaker 1: and being a black person. There's huge issues there that 358 00:23:06,640 --> 00:23:09,399 Speaker 1: really go beyond what I'm talking about here. Uh. And 359 00:23:09,440 --> 00:23:11,640 Speaker 1: I'm not gonna jump into that because I'm sure you've 360 00:23:11,640 --> 00:23:14,040 Speaker 1: covered those sort of things in previous episodes. If you're 361 00:23:14,119 --> 00:23:17,240 Speaker 1: interested in learning more about that, you can look at 362 00:23:17,280 --> 00:23:20,680 Speaker 1: the Center on Privacy and Technology at Georgetown Law. They 363 00:23:20,680 --> 00:23:24,159 Speaker 1: have a website called Perpetual Lineup dot org and it 364 00:23:24,240 --> 00:23:27,280 Speaker 1: goes through a lot of what you've been talking about. Yeah, 365 00:23:27,400 --> 00:23:29,919 Speaker 1: it's it is a real thing. I want to stress 366 00:23:30,000 --> 00:23:33,600 Speaker 1: that it is a real thing. People sometimes will think 367 00:23:33,640 --> 00:23:37,359 Speaker 1: that I've encountered people with resistance to it because they 368 00:23:37,359 --> 00:23:41,440 Speaker 1: don't like the idea of an inherently unfair system. It 369 00:23:41,520 --> 00:23:45,679 Speaker 1: goes against some of their um their own personal values, 370 00:23:45,720 --> 00:23:50,359 Speaker 1: which I completely understand. But it is a real thing. Yeah. Yeah. Well, Also, 371 00:23:50,600 --> 00:23:56,400 Speaker 1: every this is gonna be Ben's blanket statement for the episode. 372 00:23:57,000 --> 00:23:59,919 Speaker 1: Always end up having one, and please write to me 373 00:24:00,000 --> 00:24:05,720 Speaker 1: if you disagree. All systems are inherently, on some level unfair. Yes, yeah, 374 00:24:05,760 --> 00:24:09,399 Speaker 1: because systems parse and we don't have. We don't have 375 00:24:09,480 --> 00:24:14,119 Speaker 1: a perfect way of incorporating every possible life experience and 376 00:24:14,160 --> 00:24:18,040 Speaker 1: perspective when developing a system. Systems are meant to sort 377 00:24:18,160 --> 00:24:22,000 Speaker 1: things into different categories. Typically, it's like a bureaucracy. If 378 00:24:22,000 --> 00:24:24,680 Speaker 1: you know how the bureaucracy works, you can very quickly 379 00:24:24,760 --> 00:24:27,720 Speaker 1: move through a bureaucracy. But if you don't, your experience 380 00:24:27,800 --> 00:24:32,640 Speaker 1: is going to be painfully laborious. And also, I mean, 381 00:24:32,880 --> 00:24:36,280 Speaker 1: what else can we expect if if every system is 382 00:24:36,480 --> 00:24:45,640 Speaker 1: inherently imperfect, we can't even as a species get our 383 00:24:45,840 --> 00:24:52,240 Speaker 1: collection of people's face pictures correct. And we're pouring millions 384 00:24:52,240 --> 00:24:56,320 Speaker 1: into this. Let me let me throw another issue here. Yes, 385 00:24:56,359 --> 00:25:00,240 Speaker 1: so we had technology. It's it's all proprietary business only 386 00:25:00,240 --> 00:25:06,480 Speaker 1: work well together. It's disproportionately discriminatory, leading to not only 387 00:25:06,560 --> 00:25:09,600 Speaker 1: false positives, but in some cases wrongful of rest and 388 00:25:09,680 --> 00:25:13,520 Speaker 1: imprisoned and uh and Bennet's about to get a whole 389 00:25:13,520 --> 00:25:17,760 Speaker 1: lot worse. Yes, you're right, it's time for a word 390 00:25:17,800 --> 00:25:30,120 Speaker 1: from our sponsor. And we returned. It wasn't that bad. 391 00:25:30,240 --> 00:25:34,159 Speaker 1: I I actually chuckled a little bit, but it was 392 00:25:34,320 --> 00:25:38,280 Speaker 1: a tease. We weren't talking. We weren't talking about the 393 00:25:38,320 --> 00:25:43,600 Speaker 1: ad break. We were talking about yet another disturbing aspect 394 00:25:44,119 --> 00:25:46,919 Speaker 1: of the emergence of facial recognition. Yeah, I wish, I 395 00:25:46,960 --> 00:25:49,240 Speaker 1: wish I could say there's only maybe one or two 396 00:25:49,320 --> 00:25:53,240 Speaker 1: layers left. But honestly, this this is this is a 397 00:25:53,400 --> 00:25:57,679 Speaker 1: mire of issues. So remember I said, we have thousands 398 00:25:57,720 --> 00:26:02,160 Speaker 1: of different agencies, all with various databases, all using various 399 00:26:02,160 --> 00:26:06,200 Speaker 1: types of facial recognition software. The FBI can tap many 400 00:26:06,359 --> 00:26:09,600 Speaker 1: of them when doing one of these searches. Um on 401 00:26:09,680 --> 00:26:13,199 Speaker 1: top of this, you don't have you don't have a 402 00:26:13,200 --> 00:26:16,359 Speaker 1: lot of regulations in place for most of these agencies 403 00:26:16,400 --> 00:26:21,840 Speaker 1: on how they use this technology. So there are no rules, 404 00:26:21,880 --> 00:26:25,040 Speaker 1: which means there's no accountability there either. It means that 405 00:26:25,119 --> 00:26:28,640 Speaker 1: your face could be pulled into a virtual lineup. Essentially, 406 00:26:29,040 --> 00:26:31,800 Speaker 1: it could be pulled up as a hit on one 407 00:26:31,840 --> 00:26:36,160 Speaker 1: of these facial recognition searches. And because many of these 408 00:26:36,200 --> 00:26:39,879 Speaker 1: places have no rules or regulations, you are not necessarily 409 00:26:39,880 --> 00:26:42,119 Speaker 1: going to be alerted to this fact. You may not 410 00:26:42,240 --> 00:26:44,439 Speaker 1: be aware that your face is even there in the 411 00:26:44,480 --> 00:26:48,600 Speaker 1: first place. The first you may hear of it is 412 00:26:48,680 --> 00:26:50,960 Speaker 1: if it goes far enough for for an agent to 413 00:26:51,000 --> 00:26:54,960 Speaker 1: come and come knocking and ask like, Hey, your face 414 00:26:55,000 --> 00:26:57,080 Speaker 1: popped up when we ran this search. We need to 415 00:26:57,119 --> 00:26:58,560 Speaker 1: talk to you about this thing that you may or 416 00:26:58,600 --> 00:27:03,000 Speaker 1: may not have any connection to. You better have an alibi. Better. 417 00:27:03,359 --> 00:27:08,480 Speaker 1: So this is incredibly disturbing that because there are no 418 00:27:08,600 --> 00:27:13,960 Speaker 1: rules and no accountability, you can't as a citizen take 419 00:27:14,000 --> 00:27:17,640 Speaker 1: any steps because they didn't break rules. The rules didn't 420 00:27:17,640 --> 00:27:19,840 Speaker 1: exist before we started recording. I said, it's kind of 421 00:27:19,880 --> 00:27:21,840 Speaker 1: like if there were no laws, I could walk into 422 00:27:21,880 --> 00:27:24,760 Speaker 1: your house and take whatever I wanted because there's no 423 00:27:24,880 --> 00:27:27,240 Speaker 1: law against stealing as long as I have If might 424 00:27:27,480 --> 00:27:31,440 Speaker 1: makes right, then this is your time because there's only policy. Yeah, 425 00:27:31,520 --> 00:27:35,119 Speaker 1: there's there's policy internally for these agencies, and even that 426 00:27:35,200 --> 00:27:38,080 Speaker 1: doesn't match up from agency agency. Yeah, there are eighteen 427 00:27:38,160 --> 00:27:43,040 Speaker 1: states that have memorandums of agreement on this with the 428 00:27:43,080 --> 00:27:46,360 Speaker 1: FBI specifically, and and there are more states that they're 429 00:27:46,400 --> 00:27:49,600 Speaker 1: looking at adding to this all the time. But again, 430 00:27:50,160 --> 00:27:53,440 Speaker 1: within each individual agency, they may have very different policies. 431 00:27:53,560 --> 00:27:56,680 Speaker 1: Some of them have been trying to be responsible, right, 432 00:27:56,920 --> 00:27:59,920 Speaker 1: some of them have done third party audits of the 433 00:28:00,160 --> 00:28:02,600 Speaker 1: systems that sort of thing. That's great, that's at least 434 00:28:02,640 --> 00:28:05,840 Speaker 1: a step in the right direction. But until we actually 435 00:28:05,880 --> 00:28:10,600 Speaker 1: make rules, either at the state or federal level. We 436 00:28:10,720 --> 00:28:14,080 Speaker 1: don't have any again, any accountability of something were to 437 00:28:14,119 --> 00:28:16,359 Speaker 1: go wrong, Like, how do you complain if there's no 438 00:28:16,480 --> 00:28:20,879 Speaker 1: rule against it? Right? Well, one of the policies that 439 00:28:21,119 --> 00:28:25,680 Speaker 1: some of these agencies have is that before you can 440 00:28:25,720 --> 00:28:29,919 Speaker 1: return results to the FBI or to the investigative agent, 441 00:28:30,840 --> 00:28:33,959 Speaker 1: it has to first pass through humans who look at 442 00:28:33,960 --> 00:28:37,240 Speaker 1: the pictures and determine whether or not they make a 443 00:28:37,280 --> 00:28:40,560 Speaker 1: good match. So each of these agencies also have different 444 00:28:40,960 --> 00:28:44,800 Speaker 1: agreements as to how many results they will return to 445 00:28:45,520 --> 00:28:49,719 Speaker 1: whatever agent. Right Like, uh, in the FBI, it's I 446 00:28:49,760 --> 00:28:54,040 Speaker 1: believe between two and fifty, with the default being twenty results. 447 00:28:54,520 --> 00:28:56,920 Speaker 1: But some places they'll be like no, we we will 448 00:28:56,960 --> 00:28:58,880 Speaker 1: send you one, or will send you too, or we'll 449 00:28:58,880 --> 00:29:02,160 Speaker 1: send you up to eighty results. And then human beings 450 00:29:02,160 --> 00:29:05,440 Speaker 1: are supposed to call through these and decide which ones 451 00:29:05,480 --> 00:29:10,200 Speaker 1: are actually the more uh likely matches to the one 452 00:29:10,240 --> 00:29:12,640 Speaker 1: that the computer found. You're ready to have your mind 453 00:29:12,680 --> 00:29:18,240 Speaker 1: blown here, Ben, Let's say, okay, all right, Let's say 454 00:29:18,840 --> 00:29:23,920 Speaker 1: that your picture has come up in one of these investigations. 455 00:29:24,760 --> 00:29:28,080 Speaker 1: Let's say that the agency that did it does have 456 00:29:28,200 --> 00:29:31,160 Speaker 1: this policy that it must be reviewed by a human 457 00:29:31,200 --> 00:29:35,400 Speaker 1: being before it goes a knee further, fifty of the 458 00:29:35,400 --> 00:29:38,920 Speaker 1: time they're going to make the wrong call the human 459 00:29:38,960 --> 00:29:44,520 Speaker 1: being because this is hard. It's not just hard for computers. 460 00:29:44,560 --> 00:29:47,320 Speaker 1: It's hard for people to take. If you're not looking 461 00:29:47,360 --> 00:29:50,840 Speaker 1: at the exact same photos side by side, if you're 462 00:29:50,840 --> 00:29:52,760 Speaker 1: looking at a person on a different day, from a 463 00:29:52,800 --> 00:29:55,600 Speaker 1: different angle, from a different distance, under different lighting conditions, 464 00:29:55,600 --> 00:30:00,360 Speaker 1: wearing different clothing, wearing a different hairstyle, wearing whatever. It's hard, 465 00:30:00,960 --> 00:30:04,120 Speaker 1: like unless you are really familiar with that person. I mean, 466 00:30:04,160 --> 00:30:06,880 Speaker 1: I've seen pictures of people who look like me where 467 00:30:06,920 --> 00:30:09,600 Speaker 1: I looked and I thought, I don't remember ever being there, 468 00:30:09,680 --> 00:30:11,560 Speaker 1: and then as I looked, like, oh wait, that's not me. 469 00:30:13,000 --> 00:30:16,400 Speaker 1: I that has happened just to me. Don't beat yourself up. 470 00:30:16,480 --> 00:30:18,200 Speaker 1: I went home with the wrong girl. Like yeah, I 471 00:30:18,200 --> 00:30:21,680 Speaker 1: mean I've definitely walked into the wrong house. But that's 472 00:30:21,680 --> 00:30:24,000 Speaker 1: a totally different issue. That has nothing to do with 473 00:30:24,000 --> 00:30:25,640 Speaker 1: my recognition. That just has to do with me not 474 00:30:25,680 --> 00:30:28,200 Speaker 1: paying attention. That was back before you realize that Steely 475 00:30:28,280 --> 00:30:30,520 Speaker 1: was against the law. Yeah, you know, people need to 476 00:30:30,520 --> 00:30:33,360 Speaker 1: make these things a little more clear to me. Challenging stuff, 477 00:30:33,520 --> 00:30:37,920 Speaker 1: so it turns out that you people really need to 478 00:30:37,960 --> 00:30:43,840 Speaker 1: go through a very thorough, challenging, difficult training process to 479 00:30:43,920 --> 00:30:49,479 Speaker 1: become experts in matching photos. If you have not done that, 480 00:30:49,600 --> 00:30:53,040 Speaker 1: and a lot of agencies don't require that level of training, 481 00:30:54,280 --> 00:30:58,640 Speaker 1: there's a fifty error rate. So not only could the 482 00:30:58,720 --> 00:31:04,120 Speaker 1: machines misidentify somebody, the human beings who are meant to 483 00:31:04,200 --> 00:31:09,160 Speaker 1: be the the checks and balances against such things could 484 00:31:09,240 --> 00:31:12,160 Speaker 1: make the same mistake. So too bad. Coin flips, you're 485 00:31:12,200 --> 00:31:16,360 Speaker 1: in a lineup, you might be matched as someone of interest. 486 00:31:16,640 --> 00:31:20,440 Speaker 1: Now you might say, let's say, let's say that you're 487 00:31:20,480 --> 00:31:22,719 Speaker 1: the kind of person who says, I didn't do anything 488 00:31:22,720 --> 00:31:27,240 Speaker 1: wrong to worry. What do I have to worry about? Well, 489 00:31:27,440 --> 00:31:30,720 Speaker 1: I mean, you're talking about a violation of a basic 490 00:31:30,760 --> 00:31:35,320 Speaker 1: amendment here, potentially the protection against unreasonable search and seizure. 491 00:31:35,360 --> 00:31:39,640 Speaker 1: You're being searched with no justification. There's no reason for 492 00:31:39,680 --> 00:31:42,080 Speaker 1: them to search you, but they're going to be looking 493 00:31:42,120 --> 00:31:45,080 Speaker 1: into you and your background. Let's say that you did 494 00:31:45,120 --> 00:31:50,120 Speaker 1: do something that was of minor significance in the past 495 00:31:50,200 --> 00:31:53,560 Speaker 1: that maybe you know you're sorry for. You did something dumb, 496 00:31:54,160 --> 00:31:56,720 Speaker 1: you you are sorry for it. You have reformed you're not. 497 00:31:57,000 --> 00:32:00,040 Speaker 1: You know you you luckily weren't taken to task for it. 498 00:32:00,720 --> 00:32:03,240 Speaker 1: I guarantee you if there's a federal investigation, that's gonna 499 00:32:03,280 --> 00:32:05,200 Speaker 1: be one of the things that pops up. I mean, 500 00:32:05,280 --> 00:32:08,520 Speaker 1: it's it's things like your your life could be ruined 501 00:32:09,520 --> 00:32:12,800 Speaker 1: or at least significantly impacted in a negative way for 502 00:32:12,960 --> 00:32:15,600 Speaker 1: something of minor importance that may have happened in the 503 00:32:15,640 --> 00:32:18,720 Speaker 1: past that has nothing at all to do with whatever 504 00:32:18,760 --> 00:32:23,120 Speaker 1: the investigation is. And also, let's let's go a step 505 00:32:23,200 --> 00:32:32,280 Speaker 1: further here. Sure, so the practice of expunging a criminal conviction, right, 506 00:32:32,400 --> 00:32:36,360 Speaker 1: that happens a lot with especially with people who commit 507 00:32:36,440 --> 00:32:42,040 Speaker 1: a crime when they're teenagers. Right. Sure that whether whether 508 00:32:42,160 --> 00:32:48,640 Speaker 1: or not that's expunged doesn't really matter in today's investigatory climate. 509 00:32:49,040 --> 00:32:51,440 Speaker 1: It's kind of like that game where people pretend the 510 00:32:51,480 --> 00:32:54,000 Speaker 1: floor is lava and no one steps on the floor. 511 00:32:56,480 --> 00:32:58,120 Speaker 1: I'm sorry, you have to learn about it. This like 512 00:32:58,200 --> 00:33:00,960 Speaker 1: the editorial board, the floor is lava over at the 513 00:33:01,040 --> 00:33:05,280 Speaker 1: editorial department. We Um, we started that joke. Um, that's 514 00:33:05,320 --> 00:33:07,680 Speaker 1: why we have the carpet tiles. Ben about two weeks 515 00:33:07,720 --> 00:33:12,200 Speaker 1: before you got here, just kept going, you don't have 516 00:33:12,320 --> 00:33:16,640 Speaker 1: they're not special shoes. Man. On the flip side, my 517 00:33:16,960 --> 00:33:22,600 Speaker 1: hamstrings are amazing. That's true. That's true. Alright, so, um, 518 00:33:22,760 --> 00:33:27,360 Speaker 1: we're adding some levity, but we're adding levity. Yeah, you 519 00:33:27,400 --> 00:33:32,120 Speaker 1: have to because and I am not you know me, Ben, 520 00:33:32,240 --> 00:33:34,520 Speaker 1: I am not a They're all out to get you 521 00:33:34,640 --> 00:33:39,520 Speaker 1: kind of guy. No, no, you You endeavor to be 522 00:33:39,600 --> 00:33:43,960 Speaker 1: as objective as possible when you're discussing anything tech related. 523 00:33:44,200 --> 00:33:49,400 Speaker 1: I just feel very strongly that this particular practice is 524 00:33:50,880 --> 00:33:54,360 Speaker 1: dangerous and irresponsible on multiple levels. And I'm not the 525 00:33:54,400 --> 00:33:58,880 Speaker 1: only one. Congress called the FBI in front of the 526 00:33:58,880 --> 00:34:02,880 Speaker 1: principal's office back in March seventeen and essentially was saying 527 00:34:02,880 --> 00:34:06,760 Speaker 1: the same thing that we're saying. There's no accountability here. Uh. 528 00:34:06,800 --> 00:34:13,719 Speaker 1: This is horrifying. It is a gross uh over overstepping 529 00:34:13,719 --> 00:34:17,680 Speaker 1: of your authority. It uh. It is violating people's privacy. 530 00:34:18,960 --> 00:34:23,439 Speaker 1: People should have an expectation to not be called into 531 00:34:23,440 --> 00:34:25,719 Speaker 1: these virtual lineups, even if you never know about it. 532 00:34:25,760 --> 00:34:29,200 Speaker 1: The fact that it's happening is disturbing, right. I wonder 533 00:34:29,239 --> 00:34:33,640 Speaker 1: if it qualifies on your unreasonable search that and that's 534 00:34:33,680 --> 00:34:36,960 Speaker 1: one of the arguments. That's Georgetown University has has a 535 00:34:37,040 --> 00:34:40,239 Speaker 1: great report on this that is very easy to read. Uh. 536 00:34:40,280 --> 00:34:43,800 Speaker 1: There's also the Government Accountability Office has an amazing report 537 00:34:43,840 --> 00:34:47,400 Speaker 1: specifically about the FBI. Georgetown looks at the broader picture. 538 00:34:47,840 --> 00:34:51,640 Speaker 1: Government Accountability Office looks specifically at the FBI's use because 539 00:34:51,680 --> 00:34:54,440 Speaker 1: it's a federal agency. In the Government Accountability Offices is 540 00:34:54,440 --> 00:34:57,160 Speaker 1: also a federal agency. The Georgetown one looks at the 541 00:34:57,200 --> 00:35:01,080 Speaker 1: broader use of facial recognition in law and enforcement in general, 542 00:35:01,600 --> 00:35:05,120 Speaker 1: and they there are some very strong points saying this 543 00:35:05,239 --> 00:35:08,520 Speaker 1: seems like it violates our protection against unreasonable search and 544 00:35:08,560 --> 00:35:11,919 Speaker 1: seizure and that you know that that's a huge deal. 545 00:35:12,000 --> 00:35:15,160 Speaker 1: That's that that's a protection granted to us by the 546 00:35:15,200 --> 00:35:19,920 Speaker 1: Constitution of the United States. So there's definitely a movement 547 00:35:20,080 --> 00:35:26,120 Speaker 1: in government to push these agencies to develop regulations. And 548 00:35:26,160 --> 00:35:30,759 Speaker 1: I suspect that if the agencies failed to do so 549 00:35:30,840 --> 00:35:34,440 Speaker 1: in a timely manner, we will see actual legislative movement 550 00:35:35,000 --> 00:35:40,799 Speaker 1: in various state and federal governing bodies. Two codify it 551 00:35:41,280 --> 00:35:45,440 Speaker 1: at that level, if not at the actual agency level. Uh. 552 00:35:45,480 --> 00:35:47,960 Speaker 1: And I would prefer it to be codified at the 553 00:35:48,000 --> 00:35:51,640 Speaker 1: state and federal level because I always worry about any 554 00:35:51,680 --> 00:35:54,600 Speaker 1: group that's allowed to make up its own rules. Yeah, 555 00:35:54,719 --> 00:35:58,960 Speaker 1: And and I find it optimistic to to say that 556 00:35:59,000 --> 00:36:04,040 Speaker 1: someone would limit this ability because we know, for instance, 557 00:36:04,160 --> 00:36:13,920 Speaker 1: that historically, when aggregation of powerful data bases occurs, it 558 00:36:14,320 --> 00:36:18,160 Speaker 1: almost never just due to the different paces of technological 559 00:36:18,200 --> 00:36:23,760 Speaker 1: evolution and legislative evolution, the it almost never is reigned 560 00:36:23,800 --> 00:36:29,240 Speaker 1: back efficiently. I mean, like the like the right now, if, 561 00:36:29,400 --> 00:36:34,279 Speaker 1: for instance, I was that I decided to pull it 562 00:36:34,480 --> 00:36:37,359 Speaker 1: Edwards Snowden or Julian Assang or like what's a good 563 00:36:37,400 --> 00:36:41,000 Speaker 1: bond villain the old doctor No, okay, doctor No. I 564 00:36:41,040 --> 00:36:44,640 Speaker 1: was like, I'm gonna do a doctor no thing or 565 00:36:44,760 --> 00:36:47,560 Speaker 1: rs Goldfinger, Yes, yes, I'm gonna do a doctor no 566 00:36:47,719 --> 00:36:52,440 Speaker 1: Goldfinger thing. And I'm like, Matt Jonathan and I are 567 00:36:52,520 --> 00:36:56,960 Speaker 1: going to do some some terrible attack on the US 568 00:36:57,160 --> 00:37:01,200 Speaker 1: right from our secret underwater base. Of course, of course 569 00:37:01,200 --> 00:37:04,880 Speaker 1: that's where we would do that. The volcano base is nice, 570 00:37:05,560 --> 00:37:08,080 Speaker 1: But if you're doing like a world level attack where 571 00:37:08,080 --> 00:37:11,120 Speaker 1: you're you're legitimately causing vast destruction, you want to be 572 00:37:11,160 --> 00:37:13,760 Speaker 1: in that underwater base. You genuinely have to be afraid 573 00:37:13,760 --> 00:37:16,600 Speaker 1: of floor lava in the volcano base, just you really do. 574 00:37:17,000 --> 00:37:22,080 Speaker 1: Those carpet tiles are a lifesaver. Your strings do look great? 575 00:37:22,680 --> 00:37:27,600 Speaker 1: So what what What happened though, is uh, I would 576 00:37:27,680 --> 00:37:35,560 Speaker 1: quickly learn that whatever legislation exists will not in practice 577 00:37:36,040 --> 00:37:41,359 Speaker 1: stop uh stop the n essay from knowing everything I've 578 00:37:41,360 --> 00:37:46,240 Speaker 1: done online and from extrapolating, in the interests of national 579 00:37:46,320 --> 00:37:53,160 Speaker 1: security any other information. The FISA courts are for the birds. 580 00:37:53,480 --> 00:37:56,120 Speaker 1: Yeah let me, let me, let me throw one more. 581 00:37:56,280 --> 00:37:58,680 Speaker 1: I would make it worse. Yeah, okay, you wanted to 582 00:37:58,680 --> 00:38:03,000 Speaker 1: be worse. Okay, So you know I mentioned earlier that 583 00:38:03,000 --> 00:38:05,239 Speaker 1: that idea that if I haven't done anything wrong, I'm 584 00:38:05,280 --> 00:38:08,400 Speaker 1: all right. Here in the United States, one of the 585 00:38:08,400 --> 00:38:12,359 Speaker 1: things that we are allowed to do is protected by 586 00:38:12,400 --> 00:38:17,920 Speaker 1: the Constitution, is to assemble peacefully in public and express 587 00:38:18,160 --> 00:38:22,880 Speaker 1: free speech. So there has been a fear, justifiably in 588 00:38:22,960 --> 00:38:28,040 Speaker 1: my mind, that an agency that perhaps has a specific 589 00:38:28,160 --> 00:38:34,400 Speaker 1: political inclination, could use such facial recognition software to identify 590 00:38:34,640 --> 00:38:43,480 Speaker 1: participants in a peaceful protest for later let us say discussions. Right, 591 00:38:44,360 --> 00:38:47,640 Speaker 1: you don't think that's already been done. What I'm here 592 00:38:47,680 --> 00:38:51,240 Speaker 1: to say is that Georgetown, when they did their report, 593 00:38:51,960 --> 00:38:55,680 Speaker 1: they asked various agencies about their policies as to the 594 00:38:55,760 --> 00:38:58,480 Speaker 1: use of facial recognition software they found. Out of the 595 00:38:58,560 --> 00:39:02,840 Speaker 1: fifty two agency ease that they were able to contact 596 00:39:02,840 --> 00:39:07,720 Speaker 1: and get a response from, only one has a specific 597 00:39:07,800 --> 00:39:11,560 Speaker 1: rule against using facial recognition to identify people participating in 598 00:39:11,600 --> 00:39:15,799 Speaker 1: public demonstrations or free speech in general. Only one out 599 00:39:15,800 --> 00:39:18,719 Speaker 1: of fifty two that they asked specifically has a rule 600 00:39:18,800 --> 00:39:22,719 Speaker 1: against that. So, in other words, fifty one out of 601 00:39:22,760 --> 00:39:25,880 Speaker 1: fifty two have no rule. It means that if the 602 00:39:25,920 --> 00:39:28,840 Speaker 1: police were to use it, or whatever other law enforcement 603 00:39:28,880 --> 00:39:32,400 Speaker 1: agency were to use facial recognition, perhaps even in a 604 00:39:32,400 --> 00:39:34,200 Speaker 1: live feed, because you can do it in video just 605 00:39:34,280 --> 00:39:36,680 Speaker 1: as well as you can do it in photographs, to 606 00:39:37,000 --> 00:39:41,000 Speaker 1: start identifying to populate a crowd with names and identities 607 00:39:41,360 --> 00:39:45,000 Speaker 1: who are protesting a particular thing. Let's say it's police brutality, 608 00:39:45,320 --> 00:39:49,520 Speaker 1: which would be very relevant, especially in recent years. That 609 00:39:49,760 --> 00:39:53,080 Speaker 1: is a legitimate concern that that people's free speech could be, 610 00:39:53,760 --> 00:39:57,440 Speaker 1: uh could be could be violated. If you feel like 611 00:39:57,520 --> 00:40:01,560 Speaker 1: you can't go out there and express your thoughts because 612 00:40:01,680 --> 00:40:04,960 Speaker 1: you might be unfairly targeted as a result of that, 613 00:40:04,960 --> 00:40:07,640 Speaker 1: that is a violation of your free speech, not to mention, 614 00:40:07,760 --> 00:40:11,000 Speaker 1: you know, your personal safety. Depending upon how legit those 615 00:40:11,040 --> 00:40:16,520 Speaker 1: fears are, so that is incredibly troubling. I don't know 616 00:40:16,560 --> 00:40:19,759 Speaker 1: if you noticed this, Ben Matt. Maybe you guys don't 617 00:40:19,760 --> 00:40:22,800 Speaker 1: notice it. I've noticed that there have been a couple 618 00:40:22,880 --> 00:40:28,640 Speaker 1: of protests recently in the last twelve months or so. 619 00:40:28,719 --> 00:40:31,439 Speaker 1: I've seen a few, like some of some of which 620 00:40:31,440 --> 00:40:35,440 Speaker 1: have had a significant number of participants, both on both 621 00:40:35,480 --> 00:40:39,160 Speaker 1: on different sides of political issues, right, not just I'm 622 00:40:39,160 --> 00:40:42,879 Speaker 1: not saying that this is one group targeting one other group. 623 00:40:42,920 --> 00:40:45,160 Speaker 1: I'm saying there are a lot of different parties involved here, 624 00:40:45,440 --> 00:40:49,480 Speaker 1: and no matter how this technology is used against whichever 625 00:40:49,600 --> 00:40:52,080 Speaker 1: group it is used, the fact that there are no 626 00:40:52,200 --> 00:40:55,799 Speaker 1: rules about how to use it is deeply troubling, and 627 00:40:56,080 --> 00:41:00,799 Speaker 1: I would argue fundamentally un American, I would agree, what 628 00:41:00,840 --> 00:41:05,280 Speaker 1: do you think that? Yeah, definitely, I've seen the photographs 629 00:41:05,280 --> 00:41:09,680 Speaker 1: and videos of police filming using video cameras to film 630 00:41:09,800 --> 00:41:15,920 Speaker 1: protests and protesters, and that makes me very nervous. And honestly, 631 00:41:16,040 --> 00:41:19,200 Speaker 1: if you're able to get at those images, then you 632 00:41:19,200 --> 00:41:22,680 Speaker 1: can run that search, even if you weren't the one 633 00:41:22,719 --> 00:41:26,160 Speaker 1: filming it. Right. Like, one of the elements we didn't 634 00:41:26,200 --> 00:41:29,000 Speaker 1: really touch on in this in this episode, but that 635 00:41:29,320 --> 00:41:31,600 Speaker 1: is related, is the fact that we have lots of 636 00:41:31,640 --> 00:41:36,000 Speaker 1: private companies out there that incorporate facial recognition technology as 637 00:41:36,040 --> 00:41:39,480 Speaker 1: a feature for you, for consumers. Right. So Facebook is 638 00:41:39,480 --> 00:41:41,480 Speaker 1: a great example. You take a picture of someone on 639 00:41:41,520 --> 00:41:44,840 Speaker 1: Facebook that you're friends with. Facebook will often say, hey, 640 00:41:45,000 --> 00:41:46,879 Speaker 1: do you want to tag this photo? And they'll even 641 00:41:46,920 --> 00:41:48,920 Speaker 1: give you the name of the person because their facial 642 00:41:48,960 --> 00:41:51,839 Speaker 1: recognition software is good enough to let you know, like, oh, well, 643 00:41:51,920 --> 00:41:53,960 Speaker 1: we're pretty sure this is your buddy, you know, Bob, 644 00:41:54,280 --> 00:41:55,759 Speaker 1: So you want us to go ahead and tag Bob 645 00:41:55,840 --> 00:41:59,480 Speaker 1: for you? Well, you know that on its own as 646 00:41:59,520 --> 00:42:02,600 Speaker 1: a feature, there's nothing necessarily wrong with that. But you 647 00:42:02,719 --> 00:42:08,520 Speaker 1: could imagine that same sort of of deep, deep wealth 648 00:42:08,719 --> 00:42:12,960 Speaker 1: of data being put to nefarious use under the right circumstances, 649 00:42:13,000 --> 00:42:16,160 Speaker 1: maybe not by Facebook itself, but perhaps by an entity 650 00:42:16,200 --> 00:42:19,080 Speaker 1: that forces Facebook to hand that information over exactly. And 651 00:42:19,120 --> 00:42:24,040 Speaker 1: I'm glad you said that, because more more frightening than 652 00:42:25,040 --> 00:42:32,000 Speaker 1: law enforcement unfairly targeting people is the idea of the 653 00:42:32,080 --> 00:42:37,840 Speaker 1: increasingly gray area and the increasingly black box interactions between 654 00:42:38,080 --> 00:42:42,279 Speaker 1: large data aggregators like Facebook, Google, your local I s 655 00:42:42,320 --> 00:42:48,000 Speaker 1: P and Internet service provider, and the rest of the 656 00:42:48,000 --> 00:42:51,279 Speaker 1: alphabet soup of the federal government, you know, and it 657 00:42:51,320 --> 00:42:55,920 Speaker 1: could be anything from the FBI to the n s 658 00:42:56,000 --> 00:43:00,560 Speaker 1: A to local police department rights in and at Facebook 659 00:43:00,560 --> 00:43:03,279 Speaker 1: and says, hey, we need to We think there are 660 00:43:03,320 --> 00:43:06,400 Speaker 1: these three guys who went to this underwater base off 661 00:43:06,440 --> 00:43:10,520 Speaker 1: the coast of redacted, and they've been posting photos in 662 00:43:10,560 --> 00:43:14,200 Speaker 1: their timeline and we just want to make sure that 663 00:43:14,320 --> 00:43:16,719 Speaker 1: these three guys are the same three guys who did 664 00:43:16,800 --> 00:43:20,759 Speaker 1: that amazing diamond heist a few years ago and for 665 00:43:20,880 --> 00:43:24,880 Speaker 1: their for their freeze ray, yes, yes, yeah, Hey do 666 00:43:24,880 --> 00:43:29,520 Speaker 1: you guys remember b O b Y Yeah yeah, yeah 667 00:43:29,640 --> 00:43:33,799 Speaker 1: yeah Bob uh he he came at us a little 668 00:43:33,800 --> 00:43:37,480 Speaker 1: while ago, us being the world through social social media 669 00:43:37,520 --> 00:43:40,040 Speaker 1: and talked about the Earth being flat and we talked 670 00:43:40,040 --> 00:43:44,200 Speaker 1: about that. Well, he thinks that Snapchat is really just 671 00:43:44,800 --> 00:43:49,520 Speaker 1: collecting blackmail photos of our silly faces doing things. I 672 00:43:49,520 --> 00:43:53,240 Speaker 1: will tell you this, Uh, if you think that the 673 00:43:53,280 --> 00:43:56,960 Speaker 1: images and video that you take on Snapchat that disappear 674 00:43:57,040 --> 00:44:02,279 Speaker 1: after twenty four hours has truly disappeared, absolutely wrong. Yeah. No, 675 00:44:02,400 --> 00:44:05,400 Speaker 1: that exists on a server somewhere. It has to. And 676 00:44:05,400 --> 00:44:08,319 Speaker 1: then furthermore, and I love that we're exploring this part too, 677 00:44:08,360 --> 00:44:13,200 Speaker 1: because Okay, Facebook, people pile onto Facebook often because it 678 00:44:13,360 --> 00:44:17,799 Speaker 1: is you know, diabolical. It's it's an enormous corporation that 679 00:44:18,360 --> 00:44:24,120 Speaker 1: is completely dependent upon users surrendering information about themselves. So 680 00:44:25,000 --> 00:44:27,200 Speaker 1: due to its very nature as to what it is 681 00:44:27,239 --> 00:44:29,319 Speaker 1: and how it makes money. It makes money through the 682 00:44:29,360 --> 00:44:32,799 Speaker 1: fact that people use it and reveal their likes their 683 00:44:32,840 --> 00:44:35,839 Speaker 1: dislikes there you know, the things that they want. Thus 684 00:44:35,880 --> 00:44:39,120 Speaker 1: you can serve ads against that. It's very easy for 685 00:44:39,239 --> 00:44:43,400 Speaker 1: that business model to take a very dark turn without 686 00:44:43,440 --> 00:44:48,120 Speaker 1: without a lot of discipline and policies and and the 687 00:44:48,160 --> 00:44:53,440 Speaker 1: will to actually uh perform business in an ethical manner. 688 00:44:53,880 --> 00:44:56,360 Speaker 1: And to be completely fair, I don't think Facebook is 689 00:44:56,440 --> 00:44:59,279 Speaker 1: run by a bunch of vampire demons, but I do 690 00:44:59,480 --> 00:45:03,680 Speaker 1: think that it can be very easy to make unethical 691 00:45:03,760 --> 00:45:06,839 Speaker 1: decisions even without being aware of it, when you have 692 00:45:07,040 --> 00:45:10,360 Speaker 1: that massive amount of data. Absolutely, and it also extends 693 00:45:10,480 --> 00:45:14,880 Speaker 1: to as you said, snapchat. And I had an issue 694 00:45:15,040 --> 00:45:18,239 Speaker 1: when um one of one of our co host here 695 00:45:18,520 --> 00:45:22,400 Speaker 1: knowl was going through a Pokemon Go phase and it 696 00:45:22,680 --> 00:45:28,600 Speaker 1: don't put put Pokemon Go camera on me. We're hanging out, 697 00:45:28,920 --> 00:45:32,600 Speaker 1: so there's always you know, a different Pokemon that's on you. 698 00:45:32,840 --> 00:45:36,280 Speaker 1: And of course being a little bit on the paranoid side. 699 00:45:36,600 --> 00:45:40,080 Speaker 1: I'm kidding. I'm way deep into that. Well, Ben, Ben, 700 00:45:40,200 --> 00:45:42,200 Speaker 1: I mean, you're I hate to tell you this, but 701 00:45:42,280 --> 00:45:46,080 Speaker 1: you are a pig post. You're just got piggies all 702 00:45:46,160 --> 00:45:48,160 Speaker 1: over you. What do you think they mean when they 703 00:45:48,160 --> 00:45:54,000 Speaker 1: say catch them all talking about all the faces? Yeah. 704 00:45:54,000 --> 00:45:56,040 Speaker 1: One of the first things I turned off on Pokemon Go, 705 00:45:56,200 --> 00:45:58,600 Speaker 1: although it was not for fear of Well, there are 706 00:45:58,640 --> 00:46:01,840 Speaker 1: some concern for privacy because you know, you're using it 707 00:46:01,840 --> 00:46:03,879 Speaker 1: in public and there are people all around you. And 708 00:46:03,880 --> 00:46:05,960 Speaker 1: while you can take photos of people in public, there's 709 00:46:06,000 --> 00:46:09,120 Speaker 1: nothing illegal against that. I just think it's, you know, rude. 710 00:46:09,520 --> 00:46:14,080 Speaker 1: So I turned off the camera slash augmented reality part, 711 00:46:14,160 --> 00:46:16,759 Speaker 1: so it just has the animated background as opposed to 712 00:46:16,800 --> 00:46:21,040 Speaker 1: an actual camera view. Um and uh. But again I 713 00:46:21,080 --> 00:46:24,120 Speaker 1: did that not really out of concern of other people, 714 00:46:24,200 --> 00:46:27,400 Speaker 1: but rather because it doesn't drain. My battery is fast, 715 00:46:27,440 --> 00:46:29,520 Speaker 1: So it's a selfish reason. I wish I could say 716 00:46:29,560 --> 00:46:31,640 Speaker 1: I was being a really decent human being, but really 717 00:46:31,680 --> 00:46:33,640 Speaker 1: I just didn't want my phone to die. But is 718 00:46:33,680 --> 00:46:36,359 Speaker 1: it really turning your camera off? Well, I mean, it's 719 00:46:36,400 --> 00:46:38,759 Speaker 1: not showing it on my screen, whether it's showing it 720 00:46:38,760 --> 00:46:41,360 Speaker 1: on someone screen. Oh gosh, I guess, I guess what 721 00:46:41,400 --> 00:46:45,719 Speaker 1: I am doing is consistently giving Nintendo and the Pokemon 722 00:46:45,800 --> 00:46:48,719 Speaker 1: Corporation up to date information about who is on the 723 00:46:48,719 --> 00:46:52,680 Speaker 1: belt line. That's good, that's what they need, and it's 724 00:46:52,680 --> 00:46:54,799 Speaker 1: a shame that they don't pay you. So there there 725 00:46:54,800 --> 00:46:57,000 Speaker 1: are a couple of things here that we should we 726 00:46:57,040 --> 00:47:01,680 Speaker 1: should explore, so sure another they're Another fact that we 727 00:47:01,760 --> 00:47:05,799 Speaker 1: must add is yes, your face is out there. We 728 00:47:05,800 --> 00:47:08,280 Speaker 1: were increasingly, you know, I think it was Andy Warhol 729 00:47:08,280 --> 00:47:11,440 Speaker 1: who said, now everybody will have fifteen minutes of fame. 730 00:47:11,719 --> 00:47:16,839 Speaker 1: We're increasingly arriving at a world where everyone in some 731 00:47:16,960 --> 00:47:21,120 Speaker 1: way is going to be recognizable in one of these databases. 732 00:47:21,160 --> 00:47:25,359 Speaker 1: But living in an anonymous life is almost impossible if 733 00:47:25,400 --> 00:47:30,080 Speaker 1: you are living in modern society, right, And to me 734 00:47:30,480 --> 00:47:33,280 Speaker 1: a little twisting the knife with this, this is being 735 00:47:33,400 --> 00:47:37,120 Speaker 1: sold at a profit and none of the no one 736 00:47:37,280 --> 00:47:42,200 Speaker 1: is being compensated. So, for instance, every time Matt's UH 737 00:47:42,360 --> 00:47:46,200 Speaker 1: image shows up, it's a false positive. Every time that 738 00:47:46,200 --> 00:47:50,400 Speaker 1: that one resource that is entirely created by him shows 739 00:47:50,520 --> 00:47:54,600 Speaker 1: up in you know, a P D, FBI, etcetera, he 740 00:47:55,000 --> 00:47:59,239 Speaker 1: doesn't get any royalty from that. And I don't I 741 00:48:00,360 --> 00:48:04,160 Speaker 1: for the record, I completely think that will never actually happen. Now, 742 00:48:04,400 --> 00:48:07,400 Speaker 1: if we ever see a time where the FBI opens 743 00:48:07,480 --> 00:48:10,919 Speaker 1: up a fun and zany FBI T shirts store where 744 00:48:10,960 --> 00:48:14,759 Speaker 1: you can get your photos stored that was stored on 745 00:48:14,800 --> 00:48:17,879 Speaker 1: the law enforcement database onto a T shirt and maybe 746 00:48:17,920 --> 00:48:20,120 Speaker 1: as a mug shot or something, that's a great idea, 747 00:48:20,520 --> 00:48:24,080 Speaker 1: oh on a coffee mic mug shot. Coffee mug shot. 748 00:48:24,200 --> 00:48:28,879 Speaker 1: So let's uh, well, let's as as they like to say, 749 00:48:28,960 --> 00:48:32,200 Speaker 1: let's in big in this, all right. I think that's 750 00:48:32,200 --> 00:48:37,360 Speaker 1: a cromulent decision. Thank you. Uh, I agree. So we're 751 00:48:37,920 --> 00:48:42,400 Speaker 1: we're talking right now about the United States, which had 752 00:48:42,440 --> 00:48:46,120 Speaker 1: does have an extensive network, as you said, an extensive 753 00:48:46,239 --> 00:48:51,360 Speaker 1: array of networks. But my question is when it runs 754 00:48:51,360 --> 00:48:56,359 Speaker 1: into like multinational private industry, does it also run into 755 00:48:56,520 --> 00:49:02,680 Speaker 1: international law enforcement like interpool. I mean, really, if you 756 00:49:02,800 --> 00:49:06,120 Speaker 1: don't think agencies like the Central Intelligence Agency have their 757 00:49:06,160 --> 00:49:09,439 Speaker 1: own versions of the database that incorporate people from all 758 00:49:09,440 --> 00:49:12,080 Speaker 1: over the world, you're absolutely wrong. I mean it has 759 00:49:12,120 --> 00:49:16,040 Speaker 1: to write like and at some point in some aspects 760 00:49:16,120 --> 00:49:21,000 Speaker 1: you understand, right, you're talking about trying to protect national 761 00:49:21,120 --> 00:49:29,160 Speaker 1: interests from very flexible, very mobile, very agile enemies essentially, 762 00:49:29,200 --> 00:49:31,440 Speaker 1: or or agents if you want to call them that, 763 00:49:31,600 --> 00:49:36,200 Speaker 1: whether they're state agents or otherwise. So you understand, you 764 00:49:36,280 --> 00:49:40,160 Speaker 1: understand the necessity. The problem is again, unless you have 765 00:49:40,800 --> 00:49:43,799 Speaker 1: a clear set of rules, you know you have some 766 00:49:43,840 --> 00:49:46,880 Speaker 1: transparency there on how it operates, and you have the 767 00:49:46,920 --> 00:49:50,360 Speaker 1: ability to actually put your technology to the test to 768 00:49:50,440 --> 00:49:55,040 Speaker 1: show that it is in fact accurate. Ultimately, you're left 769 00:49:55,040 --> 00:49:56,759 Speaker 1: with a question of do we did we get the 770 00:49:56,840 --> 00:49:59,719 Speaker 1: right person? This leads to deaths if we If we 771 00:49:59,760 --> 00:50:02,799 Speaker 1: get the wrong person, two things happen. The bad guy 772 00:50:02,840 --> 00:50:05,360 Speaker 1: got away, right because you didn't get the person you 773 00:50:05,400 --> 00:50:08,080 Speaker 1: wanted to get, and you've given the bad guys more 774 00:50:08,120 --> 00:50:11,000 Speaker 1: reason to hate you. Because you are targeting people who 775 00:50:11,040 --> 00:50:14,759 Speaker 1: are innocent, you are creating more bad guys. Right. If 776 00:50:14,760 --> 00:50:16,799 Speaker 1: you go out there and you are targeting people who 777 00:50:16,800 --> 00:50:19,879 Speaker 1: are are innocent of the crimes that you think they 778 00:50:19,880 --> 00:50:23,400 Speaker 1: committed because you're following a wrong lead due to a 779 00:50:23,440 --> 00:50:30,760 Speaker 1: technological glitch, then you are justifying that anti sentiment because 780 00:50:31,120 --> 00:50:33,279 Speaker 1: you are doing what you have been accused of doing, 781 00:50:33,320 --> 00:50:37,560 Speaker 1: which is coming into another culture and disrupting it. So 782 00:50:37,880 --> 00:50:41,000 Speaker 1: this is true whether it's domestically and we're talking about 783 00:50:41,080 --> 00:50:45,520 Speaker 1: people who are disproportionately targeted by law enforcement because of 784 00:50:45,560 --> 00:50:47,799 Speaker 1: this sort of same sort of issue, or on a 785 00:50:47,800 --> 00:50:52,040 Speaker 1: global scale. Moreover, there are other nations that have even 786 00:50:52,160 --> 00:50:55,600 Speaker 1: more pervasive camera technologies built around them than the United 787 00:50:55,640 --> 00:50:58,920 Speaker 1: States does. Hello England, I love you, but I can't 788 00:50:58,920 --> 00:51:01,759 Speaker 1: go anywhere without being on a camera in England, right, 789 00:51:01,800 --> 00:51:06,440 Speaker 1: don't they have the highest density of CCTV. They certainly did, 790 00:51:06,520 --> 00:51:08,200 Speaker 1: at least for a long time. I don't know if 791 00:51:08,200 --> 00:51:10,480 Speaker 1: they currently hold that record, but they for for a 792 00:51:10,480 --> 00:51:13,880 Speaker 1: long time, like especially in London, because they had so 793 00:51:13,920 --> 00:51:17,560 Speaker 1: many different CCTV systems that were most of them were 794 00:51:17,600 --> 00:51:23,280 Speaker 1: self contained. Right, so a shop owner has a security camera. Yeah, essentially, 795 00:51:23,280 --> 00:51:25,520 Speaker 1: that's what it was. So it wasn't like it was 796 00:51:25,560 --> 00:51:27,880 Speaker 1: just this vast network apart from what you know you 797 00:51:27,880 --> 00:51:31,360 Speaker 1: would see like in in um Sherlock, where microft is 798 00:51:31,400 --> 00:51:34,120 Speaker 1: able to zoom in on Watson wherever he goes because 799 00:51:34,120 --> 00:51:37,160 Speaker 1: he can tap into every camera that's on every street corner. Well, 800 00:51:37,160 --> 00:51:39,960 Speaker 1: that's one thing, because those were those were streets, so 801 00:51:40,080 --> 00:51:44,320 Speaker 1: those would presumably be operated by by the government itself 802 00:51:44,920 --> 00:51:46,440 Speaker 1: or at least some branch of the government, but you 803 00:51:46,440 --> 00:51:49,640 Speaker 1: wouldn't be able to necessarily tap into like every every 804 00:51:49,680 --> 00:51:52,520 Speaker 1: stores feed because it's a self contained system. It's not 805 00:51:52,600 --> 00:51:55,759 Speaker 1: connected to a network for now, which brings us to, 806 00:51:56,400 --> 00:52:00,640 Speaker 1: of course the future. Yes, okay, very last part, uh, 807 00:52:00,719 --> 00:52:08,520 Speaker 1: speculation on speculation, educated guesses where is this going? So 808 00:52:09,840 --> 00:52:12,400 Speaker 1: On the one hand, I am somewhat encouraged in the 809 00:52:12,440 --> 00:52:15,240 Speaker 1: United States that a lot of focus has been brought 810 00:52:15,280 --> 00:52:19,279 Speaker 1: to this matter along with the Government Accountability Office and 811 00:52:19,320 --> 00:52:23,239 Speaker 1: Georgetown University. You have Congress actually taking the FBI to 812 00:52:23,320 --> 00:52:27,160 Speaker 1: task about this. Obviously, there would need to be a 813 00:52:27,200 --> 00:52:30,080 Speaker 1: lot more of those sort of discussions at all levels 814 00:52:30,120 --> 00:52:35,360 Speaker 1: of government for that to actually lead to regulations and transparency. 815 00:52:36,200 --> 00:52:38,279 Speaker 1: I think it's a good start. I do not know 816 00:52:38,320 --> 00:52:40,200 Speaker 1: if it's going to have enough momentum to carry it 817 00:52:40,239 --> 00:52:46,280 Speaker 1: forward to prevent massive abuse of this kind of technology 818 00:52:46,360 --> 00:52:48,920 Speaker 1: on multiple levels, because I mean that's happening right now. 819 00:52:48,920 --> 00:52:51,799 Speaker 1: I don't know if it'll curtail it. Um. I hope 820 00:52:51,840 --> 00:52:55,760 Speaker 1: it will. That's one possible future is that we'll see 821 00:52:55,800 --> 00:52:59,120 Speaker 1: over time the development of these rules and policies that 822 00:52:59,200 --> 00:53:02,560 Speaker 1: various agencies have to follow so that the use of 823 00:53:02,600 --> 00:53:06,919 Speaker 1: this technology is done in an accountable and responsible way, 824 00:53:07,480 --> 00:53:11,000 Speaker 1: and that uh, you know the FBI. They state that 825 00:53:11,040 --> 00:53:14,440 Speaker 1: when they're running these searches, any search result they get 826 00:53:14,480 --> 00:53:18,360 Speaker 1: back that does not mean the person that they get 827 00:53:18,640 --> 00:53:21,319 Speaker 1: has become a suspect. It means that they have a 828 00:53:21,400 --> 00:53:24,280 Speaker 1: match that a potential lead to follow. They're very careful 829 00:53:24,280 --> 00:53:27,760 Speaker 1: in their wording to say that they haven't identified to suspect. 830 00:53:27,760 --> 00:53:32,280 Speaker 1: That's why they use probe photo rather than suspect photo. However, 831 00:53:32,360 --> 00:53:35,279 Speaker 1: words only go so far. How where do the actions go? 832 00:53:35,360 --> 00:53:37,200 Speaker 1: And that's the big fear is that we don't really 833 00:53:37,239 --> 00:53:44,840 Speaker 1: know yet. So optimist Jonathan would say, over time, we 834 00:53:44,920 --> 00:53:48,600 Speaker 1: developed these rules, we hold these agencies accountable for them. 835 00:53:48,760 --> 00:53:51,840 Speaker 1: We make certain that no one is relying too heavily 836 00:53:51,880 --> 00:53:56,560 Speaker 1: on a technology that is not perfect, and it just 837 00:53:56,680 --> 00:54:01,360 Speaker 1: becomes another another tool of agencies to use for specific 838 00:54:01,400 --> 00:54:04,160 Speaker 1: situations in order to investigate leads. And that is as 839 00:54:04,160 --> 00:54:10,000 Speaker 1: far as it goes. Cynical Jonathan, I'm more familiar with 840 00:54:10,040 --> 00:54:14,400 Speaker 1: this one. Cynical Jonathan thinks this is going to continue 841 00:54:15,080 --> 00:54:23,960 Speaker 1: largely unchecked at various levels of law enforcement agencies, including 842 00:54:23,960 --> 00:54:28,680 Speaker 1: the federal level. Largely used in in agencies that have 843 00:54:28,880 --> 00:54:31,239 Speaker 1: no transparency because of the nature of what they do. 844 00:54:31,320 --> 00:54:36,040 Speaker 1: Things like the n s A, and that there will 845 00:54:36,080 --> 00:54:39,839 Speaker 1: be a growing number of people very much interested in 846 00:54:40,000 --> 00:54:43,400 Speaker 1: using it for purposes that it was not intended, in 847 00:54:43,400 --> 00:54:47,480 Speaker 1: other words, not to investigate crimes alone. I mean they 848 00:54:47,520 --> 00:54:50,239 Speaker 1: would still want to use it for that obviously, but 849 00:54:50,360 --> 00:54:55,960 Speaker 1: also to perhaps push specific political agendas by suppressing opposing 850 00:54:56,040 --> 00:55:01,680 Speaker 1: political agendas. And this sounds very or Willian and very 851 00:55:01,800 --> 00:55:04,200 Speaker 1: like I'm not again, I'm not a Norwellian type of 852 00:55:04,280 --> 00:55:08,280 Speaker 1: dystopia person, but it makes it so easy to do 853 00:55:08,880 --> 00:55:12,960 Speaker 1: that even if you weren't consciously setting out on that path, 854 00:55:13,400 --> 00:55:16,560 Speaker 1: you could do it just by happenstance of your other 855 00:55:16,680 --> 00:55:21,160 Speaker 1: methods of going about your business. And because we have 856 00:55:21,200 --> 00:55:24,640 Speaker 1: no rules or regulations for it, there's no protection against it. 857 00:55:25,000 --> 00:55:28,120 Speaker 1: So I find it very concerning. I think it needs 858 00:55:28,560 --> 00:55:34,000 Speaker 1: to be addressed and and it needs to be consistently 859 00:55:34,840 --> 00:55:38,840 Speaker 1: looked at as something that we have to fix, because 860 00:55:38,880 --> 00:55:41,960 Speaker 1: if we don't, the abuse of it is going to 861 00:55:42,000 --> 00:55:45,600 Speaker 1: be rampant. It's going to be disruptive. And I mean, 862 00:55:45,640 --> 00:55:49,520 Speaker 1: if you are not a member of whichever privileged group 863 00:55:49,760 --> 00:55:53,239 Speaker 1: is not consistently targeted by this, you might not see 864 00:55:53,239 --> 00:55:56,319 Speaker 1: a problem with it. But for everybody else it's it's 865 00:55:56,360 --> 00:56:00,440 Speaker 1: a huge it's unfair, it's a threat, it's not cool. 866 00:56:01,239 --> 00:56:05,759 Speaker 1: Uh So yeah, I'm I'm afraid we're going to a 867 00:56:05,800 --> 00:56:08,919 Speaker 1: real dark place and it's literally stuff they don't want 868 00:56:08,960 --> 00:56:11,520 Speaker 1: you to know. When we talk about where this data goes, 869 00:56:11,640 --> 00:56:16,520 Speaker 1: where it's aggregated, who's talking to whom? Right? This is? 870 00:56:17,160 --> 00:56:20,520 Speaker 1: This is not only I don't know. I think you 871 00:56:20,600 --> 00:56:26,320 Speaker 1: make an important point when you say it's not illegal, 872 00:56:26,840 --> 00:56:33,759 Speaker 1: because there's no measure of legality yet. But legal and 873 00:56:34,719 --> 00:56:37,799 Speaker 1: ethical are not always the same thing. No, I mean, 874 00:56:37,800 --> 00:56:41,359 Speaker 1: there are plenty of things that I think you could 875 00:56:41,440 --> 00:56:44,960 Speaker 1: argue are unethical that are perfectly legal, and vice versa. 876 00:56:45,000 --> 00:56:47,280 Speaker 1: There are some things that you might argue are illegal 877 00:56:47,320 --> 00:56:51,560 Speaker 1: but are perfectly ethical. Right, Like it's legal to uh 878 00:56:51,920 --> 00:56:54,760 Speaker 1: seeing that Journey song and get karaoke, But is it ethical? 879 00:56:54,880 --> 00:56:56,919 Speaker 1: I would think not that, I would think at this point, 880 00:56:57,080 --> 00:57:02,800 Speaker 1: don't stop believing has been overdone. Go with a different 881 00:57:02,880 --> 00:57:07,160 Speaker 1: Journey song. So at this point we've talked about the past, 882 00:57:07,600 --> 00:57:13,800 Speaker 1: the present, and the possible future of facial recognition, and Jonathan, 883 00:57:13,840 --> 00:57:16,080 Speaker 1: I want to thank you so much for coming on 884 00:57:16,120 --> 00:57:21,760 Speaker 1: the show and illuminating us. Choosing that word very carefully, uh, 885 00:57:21,920 --> 00:57:26,200 Speaker 1: with with the facts behind something that people hear about, 886 00:57:26,240 --> 00:57:29,400 Speaker 1: and this is a morphous boogeyman sort of way. But 887 00:57:29,560 --> 00:57:31,800 Speaker 1: it turns out that even when you shed this light 888 00:57:31,840 --> 00:57:35,000 Speaker 1: on it, for us, it's still a boogeyman. Oh yeah, 889 00:57:35,040 --> 00:57:39,160 Speaker 1: Now this is this is something that is happening. It's 890 00:57:39,200 --> 00:57:46,080 Speaker 1: happening without any real oversight it. It is certainly incorporating 891 00:57:46,800 --> 00:57:51,040 Speaker 1: uh data from people who have no criminal background whatsoever, 892 00:57:51,840 --> 00:57:56,640 Speaker 1: and it is it's very disturbing, like it's It's one 893 00:57:56,680 --> 00:57:59,320 Speaker 1: thing if everyone were to go with the rules that 894 00:57:59,360 --> 00:58:02,680 Speaker 1: the FBI originally set, which is that we're only using 895 00:58:02,720 --> 00:58:06,240 Speaker 1: the data that we've collected from criminal cases. Even then 896 00:58:06,280 --> 00:58:09,840 Speaker 1: you could argue, well, we don't necessarily know that everybody 897 00:58:09,840 --> 00:58:13,440 Speaker 1: who's in that criminal database actually did something criminal. There 898 00:58:13,480 --> 00:58:15,720 Speaker 1: might be some innocent people in there, but there probably 899 00:58:15,760 --> 00:58:17,960 Speaker 1: are some innocent people in there, And you're also missing 900 00:58:17,960 --> 00:58:20,320 Speaker 1: out on everybody who has yet to commit a crime. 901 00:58:20,600 --> 00:58:24,680 Speaker 1: But that that part would be less concerning from the 902 00:58:24,760 --> 00:58:28,000 Speaker 1: average citizen, right, Matt. I mean, if you're FBI, you 903 00:58:28,080 --> 00:58:30,200 Speaker 1: hate it because it makes it harder for you to 904 00:58:30,360 --> 00:58:32,840 Speaker 1: find a lead. But if you're an average citizen, you're 905 00:58:32,840 --> 00:58:38,040 Speaker 1: relieved because you know you're not gonna get targeted. But 906 00:58:38,080 --> 00:58:40,840 Speaker 1: the problem is that no those rules, that's not what 907 00:58:40,920 --> 00:58:43,760 Speaker 1: everyone's following people are. In fact, we don't know what 908 00:58:43,800 --> 00:58:49,360 Speaker 1: everyone is doing. And it's terrifying because you are. There's 909 00:58:49,360 --> 00:58:52,280 Speaker 1: no way of knowing how frequently your face has already 910 00:58:52,280 --> 00:58:55,440 Speaker 1: been in virtual lineups. It probably has been at some point. 911 00:58:55,640 --> 00:58:58,040 Speaker 1: It may have been that it reached a very early 912 00:58:58,120 --> 00:59:01,440 Speaker 1: stage before it was dismissed. It never went further than that. 913 00:59:02,560 --> 00:59:06,840 Speaker 1: But right now, as we're talking, as your listeners are 914 00:59:06,840 --> 00:59:11,640 Speaker 1: listening to this, there are cold algorithms looking for that 915 00:59:11,760 --> 00:59:14,880 Speaker 1: numeric match and your face might be it. And that 916 00:59:14,960 --> 00:59:17,320 Speaker 1: reminds me, Uh, do you guys want to take a 917 00:59:17,360 --> 00:59:20,360 Speaker 1: picture for Instagram after we after we closed the show 918 00:59:21,840 --> 00:59:25,919 Speaker 1: doesn't have to be of us? All right, we'll break 919 00:59:25,920 --> 00:59:30,000 Speaker 1: out I patches or something. Maybe this is all true. 920 00:59:30,160 --> 00:59:33,760 Speaker 1: This is not a conspiracy theory. This is there is 921 00:59:33,800 --> 00:59:40,360 Speaker 1: an active collection of groups that are cooperating using your 922 00:59:40,640 --> 00:59:43,360 Speaker 1: data in what could be and what have been very 923 00:59:43,440 --> 00:59:48,160 Speaker 1: dangerous ways. And Jonathan, I think you've ended it for 924 00:59:48,240 --> 00:59:53,760 Speaker 1: us on the perfect note, because this is only weirdly 925 00:59:53,880 --> 00:59:58,120 Speaker 1: enough the beginning. So if people want to learn more 926 00:59:58,160 --> 01:00:01,440 Speaker 1: about not just this, but all things tech related? Could you? 927 01:00:01,560 --> 01:00:04,320 Speaker 1: Could you tell us where to check you out? Absolutely? 928 01:00:04,520 --> 01:00:09,040 Speaker 1: I host the podcast tech Stuff uh we uh we, 929 01:00:09,160 --> 01:00:12,560 Speaker 1: published twice a week Wednesdays and Fridays. We cover all 930 01:00:12,600 --> 01:00:16,200 Speaker 1: sorts of technology topics, from things that are scary, like 931 01:00:16,280 --> 01:00:19,840 Speaker 1: I did a recent episode about near misses with nuclear war, 932 01:00:20,600 --> 01:00:22,600 Speaker 1: the times when we were the world was on the 933 01:00:22,600 --> 01:00:27,680 Speaker 1: brink of nuclear devastation and cooler heads prevailed because clearly 934 01:00:27,720 --> 01:00:30,560 Speaker 1: that did not happen. But I also do fun, silly stuff. 935 01:00:30,600 --> 01:00:34,120 Speaker 1: I did one that was all about the top memes 936 01:00:34,320 --> 01:00:36,640 Speaker 1: of the last few years, so talking about where those 937 01:00:36,680 --> 01:00:40,160 Speaker 1: came from and and uh and and how they evolved 938 01:00:40,200 --> 01:00:44,320 Speaker 1: over time. So it's the whole gambuit of technology stories. 939 01:00:44,440 --> 01:00:46,560 Speaker 1: So if you want to check that out, go to 940 01:00:46,840 --> 01:00:51,200 Speaker 1: your favorite podcatching service such as iTunes podcasts and listen 941 01:00:51,280 --> 01:00:53,560 Speaker 1: to tech Stuff. If you like it, subscribe if you 942 01:00:53,680 --> 01:00:56,520 Speaker 1: really like it, you can watch me record it live 943 01:00:56,720 --> 01:01:00,880 Speaker 1: because I'm doing something crazy on Wednesdays and Fridays over 944 01:01:00,920 --> 01:01:05,440 Speaker 1: at twitch dot tv slash tech Stuff. I live stream 945 01:01:05,840 --> 01:01:09,240 Speaker 1: the recording sessions of my show, so you can go 946 01:01:09,320 --> 01:01:11,600 Speaker 1: and watch me record live. And here's the cool thing 947 01:01:12,560 --> 01:01:15,600 Speaker 1: or lame thing, depending upon your point of view, you 948 01:01:15,640 --> 01:01:17,960 Speaker 1: can witness all the times I mess up or have 949 01:01:18,000 --> 01:01:20,000 Speaker 1: to take a break, or have to cough, or have 950 01:01:20,040 --> 01:01:23,000 Speaker 1: to drink some water or whatever it may be. Because 951 01:01:23,040 --> 01:01:25,320 Speaker 1: it's live, it's all the stuff that we cut out 952 01:01:25,360 --> 01:01:28,040 Speaker 1: before we published the show. You can witness it. And 953 01:01:28,280 --> 01:01:30,880 Speaker 1: you can say I heard that show a month before 954 01:01:30,880 --> 01:01:35,040 Speaker 1: it published, because that's how far out I am. That's fantastic, 955 01:01:35,080 --> 01:01:37,640 Speaker 1: And I think Matt sometimes helps you produce that is 956 01:01:37,640 --> 01:01:39,720 Speaker 1: that he does. Matt has I have pulled Matt in 957 01:01:39,800 --> 01:01:42,600 Speaker 1: on multiple occasions to help set up the both the 958 01:01:42,680 --> 01:01:46,440 Speaker 1: recording and the Twitch streaming side of that. Matt has 959 01:01:46,480 --> 01:01:50,480 Speaker 1: appeared on camera briefly as part of that. I don't 960 01:01:50,520 --> 01:01:52,480 Speaker 1: make him stay for the whole thing because he's cut 961 01:01:52,520 --> 01:01:55,680 Speaker 1: It turns out he's got stuff to do sometimes. Yeah, 962 01:01:56,160 --> 01:01:59,120 Speaker 1: so uh, do check out Jonathan and that if you 963 01:01:59,200 --> 01:02:02,440 Speaker 1: are interest in the future of technology and want to 964 01:02:02,440 --> 01:02:06,080 Speaker 1: feel a little bit better about the state of humanity 965 01:02:06,200 --> 01:02:09,520 Speaker 1: than check out Jonathan's other show, Forward Thinking, which is 966 01:02:09,600 --> 01:02:14,360 Speaker 1: available on YouTube and the Facebook's Yeah Yeah if you wanna. 967 01:02:14,520 --> 01:02:17,400 Speaker 1: If you wanna see my face getting identified all over 968 01:02:17,400 --> 01:02:20,480 Speaker 1: the place, check out Forward Thinking. That one's that was 969 01:02:20,560 --> 01:02:23,959 Speaker 1: kind of like the the shiny happy cousin to stuff 970 01:02:24,000 --> 01:02:28,000 Speaker 1: they don't want you to know's dark, dark emo cousin. 971 01:02:28,080 --> 01:02:32,240 Speaker 1: I think we prefer to think of it as conspiracy realism. 972 01:02:32,360 --> 01:02:35,360 Speaker 1: I look, I'm not making any kind of judgments here, 973 01:02:35,400 --> 01:02:38,720 Speaker 1: I'm merely making an observation. I love all the children 974 01:02:38,760 --> 01:02:42,400 Speaker 1: of How Stuff Works equally. All right, Well, now we're 975 01:02:42,400 --> 01:02:45,320 Speaker 1: definitely putting a picture of you on Instagram, and you 976 01:02:45,360 --> 01:02:49,040 Speaker 1: may be asking yourselves, ladies and gentlemen, Wait, the guys 977 01:02:49,080 --> 01:02:51,840 Speaker 1: have an Instagram. Oh my gosh, o MG, where can 978 01:02:51,880 --> 01:02:55,120 Speaker 1: I find it? Well, the answer is Conspiracy Stuff Show. 979 01:02:55,280 --> 01:02:57,919 Speaker 1: You can also find us on Facebook and Twitter, where 980 01:02:57,960 --> 01:03:03,640 Speaker 1: we are Conspiracy Stuff. And if you're if you're sufficiently 981 01:03:03,800 --> 01:03:08,040 Speaker 1: freaked out enough by social media where you're seeing Ben Matt, 982 01:03:08,320 --> 01:03:11,800 Speaker 1: Jonathan Noel love the show, I want to talk to you. 983 01:03:11,840 --> 01:03:14,400 Speaker 1: Guys have some stuff that my fellow listeners should know, 984 01:03:14,520 --> 01:03:18,160 Speaker 1: but I'm I'm scared shirtless of social media, then you 985 01:03:18,200 --> 01:03:21,240 Speaker 1: can write to us directly. We are conspiracy at how 986 01:03:21,280 --> 01:03:23,840 Speaker 1: Stuff Works dot com and we especially want to hear 987 01:03:23,880 --> 01:03:29,600 Speaker 1: from you if you live in Alabama, Arizona, Arkansas, Delaware, Illinois, Iowa, Kentucky, Maryland, Michigan, Nebraska, 988 01:03:29,640 --> 01:03:33,560 Speaker 1: New Mexico, North Carolina, North Dakota, South Carolina, Tennessee, Texas, Utah, 989 01:03:33,560 --> 01:03:35,959 Speaker 1: and Vermont. Because those are the states that are working 990 01:03:36,000 --> 01:03:39,400 Speaker 1: directly with the FBI, why I counted eighteen of them, Matt, 991 01:03:39,760 --> 01:03:41,960 Speaker 1: that's right, Guys. We end when we say the email 992 01:03:42,080 --> 01:04:05,520 Speaker 1: conspiracy at how stuff works dot com.