1 00:00:00,240 --> 00:00:06,320 Speaker 1: Drew Harwell is an actual recognized authority on various things technologically, 2 00:00:06,480 --> 00:00:09,719 Speaker 1: covers technology and artificial intelligence for The Washington Post and 3 00:00:09,800 --> 00:00:14,000 Speaker 1: joins US Now to talk about the coming facial recognition 4 00:00:14,560 --> 00:00:17,520 Speaker 1: wave across America. Hey, Drew, how are you? Hey? Guy's good? 5 00:00:17,560 --> 00:00:19,920 Speaker 1: How are you good? That was an extremely worthy introduction, 6 00:00:19,960 --> 00:00:23,840 Speaker 1: but I had pulled it off. So listen. I think 7 00:00:23,880 --> 00:00:25,680 Speaker 1: a lot of us have followed some of the documentaries 8 00:00:25,720 --> 00:00:29,960 Speaker 1: about facial recognition in China and it's it's pretty cool 9 00:00:30,040 --> 00:00:33,280 Speaker 1: in a weird way, but pretty scary. What's the newest 10 00:00:33,479 --> 00:00:36,879 Speaker 1: on facial recognition in the US? Yeah, I mean you're 11 00:00:36,920 --> 00:00:40,839 Speaker 1: seeing it everywhere. You're seeing it in um schools and companies, 12 00:00:40,880 --> 00:00:42,760 Speaker 1: and you know, it's still sort of rare, but it's 13 00:00:42,840 --> 00:00:46,000 Speaker 1: it's increasing pretty quickly. And the big sort of new 14 00:00:46,040 --> 00:00:50,240 Speaker 1: front is for police departments in sheriff's office, where they 15 00:00:50,320 --> 00:00:53,479 Speaker 1: are allowing sort of deputies and investigators to use it 16 00:00:53,520 --> 00:00:57,200 Speaker 1: to identify people that they meet out on on the 17 00:00:57,240 --> 00:01:00,240 Speaker 1: streets and in the field and in their way maybe 18 00:01:00,320 --> 00:01:03,640 Speaker 1: lead to new arrests and new investigations. How would that 19 00:01:03,680 --> 00:01:08,199 Speaker 1: work exactly in practicality, So you know, if somebody shoplifts 20 00:01:08,200 --> 00:01:11,000 Speaker 1: out of the store. They're often caught on security or 21 00:01:11,040 --> 00:01:15,000 Speaker 1: surveillance cameras. The people who called the police, they sort 22 00:01:15,000 --> 00:01:16,959 Speaker 1: of have the footage, but they don't have any quick 23 00:01:17,000 --> 00:01:20,920 Speaker 1: way to identify the people. So now these deputies can 24 00:01:20,959 --> 00:01:23,280 Speaker 1: just sort of take that image, run it through their 25 00:01:23,280 --> 00:01:26,880 Speaker 1: facial recognition which scans all of their sort of jail 26 00:01:26,959 --> 00:01:31,560 Speaker 1: mug shots, and you know, okay, instead of saying that 27 00:01:31,640 --> 00:01:33,120 Speaker 1: he was a guy, he's I don't know, he's about 28 00:01:33,160 --> 00:01:35,399 Speaker 1: six ft tall, probably way too und nay bonds. Well, 29 00:01:35,400 --> 00:01:38,560 Speaker 1: they just they just do the facial facial recognition thing 30 00:01:38,560 --> 00:01:40,319 Speaker 1: and say it's Jimmy Smith, he lives over here, and 31 00:01:40,360 --> 00:01:42,679 Speaker 1: you go get him. Well, does it have to be 32 00:01:42,760 --> 00:01:45,840 Speaker 1: somebody who's been in the system. Yeah, that's right. So 33 00:01:45,959 --> 00:01:48,880 Speaker 1: it's photos right now, and it's everybody who's been sort 34 00:01:48,880 --> 00:01:51,120 Speaker 1: of arrested. Will all be in the system before you know, 35 00:01:51,240 --> 00:01:53,440 Speaker 1: it will all be in the system before you know, 36 00:01:53,600 --> 00:01:56,280 Speaker 1: if we aren't already. Yeah, that's right. I mean, you 37 00:01:56,320 --> 00:01:58,240 Speaker 1: know we have to think that there are you know, 38 00:01:58,280 --> 00:01:59,960 Speaker 1: the d m V has photos on all of us 39 00:02:00,040 --> 00:02:02,400 Speaker 1: or passport photos of the databases are out there. They're 40 00:02:02,440 --> 00:02:04,840 Speaker 1: just not yet connected to those systems. But you know, 41 00:02:04,880 --> 00:02:07,040 Speaker 1: the question is could they ever be and could we 42 00:02:07,080 --> 00:02:11,680 Speaker 1: ever be identified like that? Yes? And yes, yeah, you know, 43 00:02:12,760 --> 00:02:18,200 Speaker 1: there's a fine line between uh aware and paranoid, but 44 00:02:19,040 --> 00:02:21,239 Speaker 1: you know there and we reference this on the show. 45 00:02:21,280 --> 00:02:24,079 Speaker 1: Semi regularly drew that there are so many federal crimes 46 00:02:24,080 --> 00:02:27,480 Speaker 1: nobody even knows how to count them, including the regulations 47 00:02:27,520 --> 00:02:29,200 Speaker 1: you can violate which end you up in jail, and 48 00:02:29,200 --> 00:02:31,480 Speaker 1: the rest of it. And I could see a push 49 00:02:31,560 --> 00:02:33,800 Speaker 1: made that if you get a parking ticket, if you 50 00:02:33,840 --> 00:02:37,880 Speaker 1: have an overdue library book, if you do anything, you 51 00:02:37,919 --> 00:02:41,360 Speaker 1: step out a line, we just we're gonna take your picture. 52 00:02:42,000 --> 00:02:44,880 Speaker 1: That's it, and you get entered into the system, and 53 00:02:44,919 --> 00:02:49,079 Speaker 1: then everybody's in the system. Yeah, and that's not really 54 00:02:49,160 --> 00:02:51,639 Speaker 1: science fiction anymore. I mean that a way. A part 55 00:02:51,639 --> 00:02:53,560 Speaker 1: of that happens in China right now, where they have 56 00:02:53,840 --> 00:02:56,960 Speaker 1: sort of facial recognition camera set up at intersections. If 57 00:02:57,000 --> 00:02:59,320 Speaker 1: people jaywalk, it sort of takes a picture of on 58 00:02:59,400 --> 00:03:03,120 Speaker 1: it has their identity, and then they're they're named and shamed. Effectively, 59 00:03:03,440 --> 00:03:05,760 Speaker 1: their their pictures and identities are put up in these 60 00:03:05,800 --> 00:03:08,600 Speaker 1: like public squares. Is sort of like you know, moments 61 00:03:08,600 --> 00:03:11,480 Speaker 1: of embarrassment. So that's not happening here obviously, But that's 62 00:03:11,520 --> 00:03:14,160 Speaker 1: just a sign of you know, how this technology could 63 00:03:14,160 --> 00:03:15,919 Speaker 1: be used. I mean, it's it's it's a way that 64 00:03:16,120 --> 00:03:19,560 Speaker 1: deputies and police could identify any of us, um without 65 00:03:19,600 --> 00:03:22,280 Speaker 1: us really knowing or consenting. And so there's all sorts 66 00:03:22,320 --> 00:03:25,320 Speaker 1: of questions of privacy and whether we really want that 67 00:03:25,320 --> 00:03:27,720 Speaker 1: that future ahead of us. How good is it right now? 68 00:03:27,800 --> 00:03:31,560 Speaker 1: The technology on facial recognition? Is it pretty accurate most 69 00:03:31,600 --> 00:03:35,000 Speaker 1: of the time. It all depends on the quality of 70 00:03:35,000 --> 00:03:38,280 Speaker 1: the image and the images they're looking at, like their 71 00:03:38,360 --> 00:03:42,080 Speaker 1: database of facial images. Right, So if all of a 72 00:03:42,120 --> 00:03:45,280 Speaker 1: sudden does it not work, yeah, I mean that's a 73 00:03:45,280 --> 00:03:48,119 Speaker 1: good quay it really, I mean, yes, it can still 74 00:03:48,160 --> 00:03:49,920 Speaker 1: work in that and a lot of the deputies will 75 00:03:49,960 --> 00:03:51,920 Speaker 1: say like, oh, you know if even if somebody puts 76 00:03:51,920 --> 00:03:54,600 Speaker 1: on glasses or they grow out a beard, you know, 77 00:03:54,680 --> 00:03:57,360 Speaker 1: this can still tell. I mean, it really depends on 78 00:03:57,520 --> 00:03:59,520 Speaker 1: how good the images. And you know a lot of 79 00:03:59,560 --> 00:04:02,200 Speaker 1: these survey and lets photos are taken from in the 80 00:04:02,240 --> 00:04:06,720 Speaker 1: ceilings at weird angles, so those tend to be less accurate, um, 81 00:04:06,840 --> 00:04:09,040 Speaker 1: and you know they're they're taken in the darker by 82 00:04:09,240 --> 00:04:12,520 Speaker 1: you know, crappy smartphones so it all depends. But you know, 83 00:04:12,840 --> 00:04:16,599 Speaker 1: in this scenario, anything less than perfect accuracy is going 84 00:04:16,640 --> 00:04:21,760 Speaker 1: to lead to deputies potentially arresting the wrong right. Horrified 85 00:04:21,800 --> 00:04:24,240 Speaker 1: by all this, but the upsides, you know, for the 86 00:04:24,279 --> 00:04:26,640 Speaker 1: things for good. If we could do away with keys 87 00:04:26,720 --> 00:04:28,880 Speaker 1: more or less, I'd be happy that you could walk 88 00:04:28,920 --> 00:04:31,080 Speaker 1: into your building where you work with your face, or 89 00:04:31,080 --> 00:04:33,159 Speaker 1: your house, or start your car with your face. You 90 00:04:33,240 --> 00:04:35,600 Speaker 1: just don't need keys. That that'd be awesome. Drew Harwell 91 00:04:35,680 --> 00:04:38,760 Speaker 1: covers technology for the Washington Post and is on the line, Yeah, 92 00:04:38,800 --> 00:04:40,400 Speaker 1: that that would be kind of cool. And I think 93 00:04:40,400 --> 00:04:43,440 Speaker 1: we're moving in that direction. Hey, one thing you mentioned 94 00:04:43,440 --> 00:04:45,760 Speaker 1: in your piece, which is quite be you. You were 95 00:04:45,800 --> 00:04:49,320 Speaker 1: too fat, You didn't used to be somebody differently, let 96 00:04:49,320 --> 00:04:52,480 Speaker 1: myself go. One thing in your piece that will have 97 00:04:52,520 --> 00:04:54,800 Speaker 1: a link to so folks can find it easily is 98 00:04:54,880 --> 00:05:00,760 Speaker 1: that they've actually um run artists sketches through a software. 99 00:05:01,440 --> 00:05:04,599 Speaker 1: Mm hmm. Yeah. And that's not a way to see 100 00:05:04,600 --> 00:05:07,359 Speaker 1: if that looks like anybody. Yeah, And I mean you 101 00:05:07,400 --> 00:05:09,080 Speaker 1: have to wonder is that really going to give you 102 00:05:09,160 --> 00:05:11,560 Speaker 1: good results? Like you know, the system is designed to 103 00:05:11,600 --> 00:05:14,559 Speaker 1: compare one photo to the other. When you start getting 104 00:05:14,560 --> 00:05:17,720 Speaker 1: into artist sketches it. It all depends on how is 105 00:05:17,800 --> 00:05:19,719 Speaker 1: the how is the artist feeling that day, and how 106 00:05:19,800 --> 00:05:22,479 Speaker 1: good was the drawing right, and and so you know 107 00:05:22,640 --> 00:05:25,760 Speaker 1: that's gonna up the the danger of it being a 108 00:05:25,800 --> 00:05:29,080 Speaker 1: misidentification to all these people are saying we really shouldn't 109 00:05:29,120 --> 00:05:31,839 Speaker 1: be using them that in that way. But you know, Amazon, 110 00:05:31,880 --> 00:05:34,000 Speaker 1: for their point says whatever, I mean, this is a 111 00:05:34,000 --> 00:05:36,280 Speaker 1: tool for the deputies. They get to decide. It's it's 112 00:05:36,400 --> 00:05:38,200 Speaker 1: it's all human judgment at the end of the day 113 00:05:38,200 --> 00:05:40,200 Speaker 1: because they get to look at the photos and the matches, 114 00:05:40,240 --> 00:05:42,520 Speaker 1: at the system shows up and get to choose. But 115 00:05:42,920 --> 00:05:44,880 Speaker 1: you know, I think there's a question of whether, hey, 116 00:05:44,960 --> 00:05:47,080 Speaker 1: is this system being used for for too much? Is 117 00:05:47,080 --> 00:05:49,480 Speaker 1: there is their mission creep here where we're now just 118 00:05:49,520 --> 00:05:52,760 Speaker 1: sort of throwing photos into the certain because it's easy. 119 00:05:53,279 --> 00:05:55,760 Speaker 1: Are well cover tech for the Washington Post? True? Really 120 00:05:55,760 --> 00:05:57,720 Speaker 1: interesting stuff. Again, we'll have a link to your piece. 121 00:05:57,760 --> 00:06:01,440 Speaker 1: Thanks very much for the conversation. Thank you. There's no 122 00:06:01,520 --> 00:06:05,320 Speaker 1: stopping any of this, all our information unless we fight 123 00:06:04,880 --> 00:06:07,520 Speaker 1: for ZENA or faces. Everything is going to be everywhere, 124 00:06:07,520 --> 00:06:09,480 Speaker 1: all the time. The government will track everything we do 125 00:06:09,600 --> 00:06:13,239 Speaker 1: all the time. That is absolutely gonna happen in my lifetime? Chilling? 126 00:06:13,440 --> 00:06:15,840 Speaker 1: What's coming up in your news? Marshall Philips, President Trump's 127 00:06:15,839 --> 00:06:19,679 Speaker 1: former lawyer Michael Cohen one last verbal blast before checking 128 00:06:19,760 --> 00:06:24,000 Speaker 1: into jail, got a startling admission from Boeing about its 129 00:06:24,080 --> 00:06:30,640 Speaker 1: Max Jets, And yet another organ gets drowned. Another Oregon oregan, 130 00:06:32,960 --> 00:06:36,680 Speaker 1: What is what the fascination with drones? I don't know. 131 00:06:37,360 --> 00:06:39,120 Speaker 1: I don't know if you can work a drone into 132 00:06:39,160 --> 00:06:40,839 Speaker 1: what it makes the new wake me when the novelty 133 00:06:40,920 --> 00:06:41,480 Speaker 1: is worn off