1 00:00:01,720 --> 00:00:02,880 Speaker 1: Al Zone Media. 2 00:00:05,200 --> 00:00:09,680 Speaker 2: Welcome dick it app and here a podcast sometimes occasion, 3 00:00:09,720 --> 00:00:13,960 Speaker 2: not even that occasionally, that's about a bunch of not 4 00:00:14,200 --> 00:00:17,240 Speaker 2: very functional mass surveillance technology that's being deployed against all 5 00:00:17,280 --> 00:00:20,040 Speaker 2: of us. I'm your host, Miah Wong, also with me 6 00:00:20,079 --> 00:00:21,080 Speaker 2: as Garrison Davis. 7 00:00:22,040 --> 00:00:23,360 Speaker 3: Yeah, and so today. 8 00:00:23,079 --> 00:00:25,720 Speaker 2: We're gonna be talking more about something we've talked about 9 00:00:25,760 --> 00:00:27,960 Speaker 2: on the show I think a couple of times, but 10 00:00:28,240 --> 00:00:32,040 Speaker 2: that is the shot Spotter program, and there was recently 11 00:00:32,080 --> 00:00:35,680 Speaker 2: a leak of the locations of all of shot spotters 12 00:00:35,840 --> 00:00:39,440 Speaker 2: like gunfire sensors, and with us to talk about it 13 00:00:39,479 --> 00:00:42,720 Speaker 2: are the people who got the leak and wrote the 14 00:00:42,840 --> 00:00:47,960 Speaker 2: article about about where the shotspatter sensors are, and that 15 00:00:48,120 --> 00:00:50,800 Speaker 2: is Drew Mei Rotra who's a staff writer at Wired, 16 00:00:50,880 --> 00:00:54,160 Speaker 2: and Joey Scott, who's a freelance investigative journalist and photographer. 17 00:00:54,720 --> 00:00:56,480 Speaker 3: And both of you two, welcome to the show. 18 00:00:57,160 --> 00:00:58,080 Speaker 1: Thanks for having us. 19 00:00:58,400 --> 00:01:00,000 Speaker 4: Yes, thank you. 20 00:01:00,920 --> 00:01:02,840 Speaker 2: Yeah, I'm glad to be talking to you about this. So, 21 00:01:03,600 --> 00:01:07,160 Speaker 2: I guess for the people who don't remember or like 22 00:01:07,440 --> 00:01:09,640 Speaker 2: have not listened to other episodes we've done about this, 23 00:01:09,760 --> 00:01:12,039 Speaker 2: or have read this article which you should go read 24 00:01:12,080 --> 00:01:16,959 Speaker 2: at Wired it's great. Can you describe what ShotSpotter is 25 00:01:17,120 --> 00:01:20,480 Speaker 2: and what it's supposed to do versus what it actually does? 26 00:01:21,600 --> 00:01:23,320 Speaker 1: Sure, Joey, you want me to take this or do 27 00:01:23,319 --> 00:01:23,800 Speaker 1: you want to do it? 28 00:01:23,880 --> 00:01:26,440 Speaker 3: Yeah? Go ahead, sure well. 29 00:01:26,760 --> 00:01:31,960 Speaker 1: ShotSpotter is a sort of controversial gunshot detection system built 30 00:01:32,000 --> 00:01:35,320 Speaker 1: by the company Sound Thinking. On the face of it, 31 00:01:35,360 --> 00:01:38,440 Speaker 1: the tech is sort of straightforward. The company will basically 32 00:01:38,480 --> 00:01:41,720 Speaker 1: install little sensors on street lights and traffic signs and 33 00:01:41,760 --> 00:01:46,960 Speaker 1: a jurisdiction, and these sensors are sort of like algorithmically 34 00:01:47,000 --> 00:01:51,080 Speaker 1: tuned to detect gunshots. So when one of these sensors 35 00:01:51,160 --> 00:01:54,760 Speaker 1: here's something, it basically will send an alert to an 36 00:01:54,760 --> 00:01:58,400 Speaker 1: incident review center will then like vet the sound make 37 00:01:58,440 --> 00:02:00,960 Speaker 1: sure it was actually a gunshot before we're then forwarding 38 00:02:01,040 --> 00:02:03,760 Speaker 1: it to dispatchers who send a cop to investigate the sound. 39 00:02:04,200 --> 00:02:08,040 Speaker 1: You know, activists and academics have been basically saying for 40 00:02:08,160 --> 00:02:12,440 Speaker 1: years that this tech is inaccurate and primes police basically 41 00:02:12,480 --> 00:02:15,840 Speaker 1: to go to low income community communities of color expecting 42 00:02:15,919 --> 00:02:17,960 Speaker 1: gunshots when likely they won't find any. 43 00:02:18,720 --> 00:02:21,880 Speaker 2: Yeah, and I think I think the specifically low income 44 00:02:21,880 --> 00:02:24,800 Speaker 2: communities of color thing is a big part of this 45 00:02:24,960 --> 00:02:28,760 Speaker 2: because so you all created a map of where the 46 00:02:28,760 --> 00:02:31,840 Speaker 2: shot spotter sensors are from the data you got, and 47 00:02:31,919 --> 00:02:34,080 Speaker 2: I looked at Chicago one and immediately I was like, 48 00:02:34,120 --> 00:02:36,239 Speaker 2: I recognized that map. That is the map of where 49 00:02:36,240 --> 00:02:41,239 Speaker 2: the not white people are in the City's. 50 00:02:41,040 --> 00:02:45,040 Speaker 1: Yeah, yeah, it's it's it's it's stark. I think a 51 00:02:45,040 --> 00:02:48,680 Speaker 1: lot a lot of the responses that I've seen on 52 00:02:48,760 --> 00:02:52,680 Speaker 1: Twitter and you know, in my email inboxer essentially that like, look, 53 00:02:52,760 --> 00:02:55,160 Speaker 1: this is just a map of where all the not 54 00:02:55,280 --> 00:02:58,960 Speaker 1: white people are and whatever city that it's deployed in. 55 00:03:00,080 --> 00:03:04,080 Speaker 2: Yeah, and y'all did some analysis of what you found 56 00:03:04,160 --> 00:03:07,799 Speaker 2: sort of statistically about where these centers ended up and 57 00:03:07,919 --> 00:03:10,760 Speaker 2: like the sort of the the I guess like class 58 00:03:10,760 --> 00:03:12,920 Speaker 2: and racial composition of those places. Can you talk about 59 00:03:12,919 --> 00:03:13,480 Speaker 2: that a little bit? 60 00:03:14,720 --> 00:03:17,600 Speaker 1: Sure, I'll take this one just because I worked on 61 00:03:17,639 --> 00:03:20,080 Speaker 1: the analysis. So yeah, I mean, what we found is 62 00:03:20,080 --> 00:03:23,799 Speaker 1: that more than twelve million Americans live in a neighborhood 63 00:03:23,800 --> 00:03:27,760 Speaker 1: with at least one shot Spotter sensor. We basically joined 64 00:03:28,600 --> 00:03:34,359 Speaker 1: census data onto the locations of every single SHOTSPO microphone 65 00:03:35,360 --> 00:03:38,160 Speaker 1: and looked at the demographic composition of those neighborhoods. And 66 00:03:38,320 --> 00:03:40,720 Speaker 1: you know what, we found is that an aggregate, nearly 67 00:03:40,760 --> 00:03:43,440 Speaker 1: seventy percent of the people who live in a neighborhood 68 00:03:43,440 --> 00:03:47,320 Speaker 1: with one set with at least one sensor identify as 69 00:03:47,360 --> 00:03:51,160 Speaker 1: either black or Latine. Nearly three quarters of those neighborhoods 70 00:03:51,200 --> 00:03:54,120 Speaker 1: are a majority not white, and the average household income 71 00:03:54,160 --> 00:03:56,640 Speaker 1: in a neighborhood with at least one sensor is fifty 72 00:03:57,000 --> 00:04:00,280 Speaker 1: dollars a year, So these are low income communities of color. 73 00:04:00,320 --> 00:04:02,240 Speaker 1: It's kind of hard to describe it in any other way. 74 00:04:03,640 --> 00:04:05,240 Speaker 2: Yeah, And you know, one of the things, and this 75 00:04:05,280 --> 00:04:08,200 Speaker 2: has been a thing for so I'm in Chicago. There's 76 00:04:08,200 --> 00:04:11,240 Speaker 2: been a huge series of fights over getting rid Ofshotspotter here. 77 00:04:11,280 --> 00:04:12,720 Speaker 2: And one of the things you hear all the time 78 00:04:13,560 --> 00:04:15,840 Speaker 2: that shot is the shots are People will go, no, well, 79 00:04:15,840 --> 00:04:19,960 Speaker 2: we don't use race as a factor for yeah, But 80 00:04:20,000 --> 00:04:23,640 Speaker 2: like ShotSpotter insists that they don't use race at all 81 00:04:24,600 --> 00:04:29,320 Speaker 2: in in determining where where they put these sensors. But kabba, 82 00:04:29,640 --> 00:04:33,960 Speaker 2: they've still managed to somehow create this map. And I 83 00:04:34,120 --> 00:04:39,440 Speaker 2: don't know, I I'm wondering what you think about like 84 00:04:39,600 --> 00:04:42,359 Speaker 2: their response and whether you and I guess this is 85 00:04:42,360 --> 00:04:44,680 Speaker 2: more of a subjective think, like how much do you 86 00:04:44,720 --> 00:04:47,480 Speaker 2: actually believe them when they when they say. 87 00:04:47,200 --> 00:04:53,240 Speaker 4: This, Well, I think when you know, we are investigating this, 88 00:04:53,400 --> 00:04:56,400 Speaker 4: we found that the police don't even know where these 89 00:04:56,440 --> 00:05:00,840 Speaker 4: locations are, and so they're just given ShotSpot data of 90 00:05:00,880 --> 00:05:03,320 Speaker 4: where to put this stuff. So the police can kind 91 00:05:03,320 --> 00:05:06,039 Speaker 4: of wipe their hands of like, oh, we insisted that 92 00:05:06,040 --> 00:05:08,480 Speaker 4: they put it in this place or anything like that. 93 00:05:08,920 --> 00:05:12,000 Speaker 4: And I think, you know, Drove can probably speak to this, 94 00:05:12,120 --> 00:05:15,520 Speaker 4: but you know, the argument is this is where all 95 00:05:15,600 --> 00:05:19,080 Speaker 4: the shootings are, and so that's where they are. But 96 00:05:19,600 --> 00:05:22,600 Speaker 4: you know, when you investigate that, it doesn't call into 97 00:05:22,600 --> 00:05:25,640 Speaker 4: effect like in other parts of the country outside of 98 00:05:25,680 --> 00:05:29,240 Speaker 4: like Chicago or something. You look at gun violence and 99 00:05:29,279 --> 00:05:31,800 Speaker 4: where these alerts are, you know, they aren't just where 100 00:05:31,800 --> 00:05:35,160 Speaker 4: the alerts are. And you know, Pasadena is an example. 101 00:05:35,760 --> 00:05:38,240 Speaker 4: You know, shootings happen outside of where the alerts are, 102 00:05:38,279 --> 00:05:42,440 Speaker 4: but they're specifically in a very specific part of Pasadena 103 00:05:42,480 --> 00:05:45,279 Speaker 4: that is poor and non white. 104 00:05:46,640 --> 00:05:49,440 Speaker 1: So yeah, yeah, and I think you know, when we 105 00:05:49,480 --> 00:05:52,840 Speaker 1: spoke to sound Thinking, you know, I think it's important 106 00:05:52,880 --> 00:05:54,680 Speaker 1: to point out here that they did not dispute or 107 00:05:54,720 --> 00:05:58,359 Speaker 1: findings or the sort of authenticity of the dock. But 108 00:05:59,160 --> 00:06:00,800 Speaker 1: you know, they said what you would expect that the 109 00:06:00,800 --> 00:06:04,359 Speaker 1: sensor deployment is not really informed by race, and you know, 110 00:06:04,440 --> 00:06:06,919 Speaker 1: the way it works, as Joey says, is that the 111 00:06:06,960 --> 00:06:09,839 Speaker 1: company basically asks police department who purchased the systems for 112 00:06:09,960 --> 00:06:14,200 Speaker 1: data about gun violence, which Sound Thinking says is objective. 113 00:06:14,240 --> 00:06:16,680 Speaker 1: But we have no idea what that data actually looks like, right. 114 00:06:16,720 --> 00:06:19,840 Speaker 1: We don't know if it's all crime data, which might 115 00:06:19,880 --> 00:06:23,080 Speaker 1: be you know, a subject to enforcement bias, right if 116 00:06:23,120 --> 00:06:26,120 Speaker 1: they include things like drug crimes on their drug arrests. 117 00:06:26,440 --> 00:06:31,440 Speaker 1: So we just don't really know why Sound Thinking, you know, 118 00:06:32,160 --> 00:06:37,159 Speaker 1: makes a recommended plan for their sensor deployment. The other 119 00:06:37,200 --> 00:06:39,760 Speaker 1: thing that Sound Thinking had told me is that, you know, 120 00:06:39,920 --> 00:06:42,200 Speaker 1: sometimes they'll ask for data and they'll do it this 121 00:06:42,360 --> 00:06:44,840 Speaker 1: sort of data informed way, but other times cops will 122 00:06:44,839 --> 00:06:46,960 Speaker 1: just say, like, look, we want the we want the 123 00:06:47,000 --> 00:06:49,600 Speaker 1: deployment in this area, and that might include like a 124 00:06:49,640 --> 00:06:52,280 Speaker 1: stadium or a school or places where people gather. 125 00:06:52,680 --> 00:06:55,960 Speaker 3: So you know, it's kind of we don't really know. 126 00:06:55,920 --> 00:06:59,360 Speaker 1: Why exactly Sound Thinking is deploying its censors in any 127 00:06:59,360 --> 00:06:59,960 Speaker 1: given location. 128 00:07:01,400 --> 00:07:03,719 Speaker 2: Yeah, and having them be deployed by cops is like 129 00:07:04,160 --> 00:07:09,920 Speaker 2: he is a spectacular way to have cop brain in 130 00:07:10,000 --> 00:07:14,000 Speaker 2: terms in terms of locations, which not not not not 131 00:07:14,160 --> 00:07:17,320 Speaker 2: I don't know, not an especially good way to get 132 00:07:17,320 --> 00:07:20,360 Speaker 2: a statistically unbiased sampling of where you would potentially want 133 00:07:20,400 --> 00:07:23,800 Speaker 2: these things. So I guess I guess the a thing 134 00:07:23,840 --> 00:07:26,560 Speaker 2: we should talk about in terms of what the issues 135 00:07:26,600 --> 00:07:30,720 Speaker 2: with the system is are. Okay, So shot spotter claims 136 00:07:30,760 --> 00:07:32,360 Speaker 2: that it and this is something I've seen over and 137 00:07:32,400 --> 00:07:34,200 Speaker 2: over and over again. It claims it is a ninety 138 00:07:34,240 --> 00:07:39,960 Speaker 2: seven percent accuracy rate of detecting gunshots. There's just I 139 00:07:39,960 --> 00:07:42,440 Speaker 2: I don't believe it. None of the research I've ever 140 00:07:42,480 --> 00:07:46,720 Speaker 2: seen backs that up. You talk a bit about a 141 00:07:46,760 --> 00:07:50,000 Speaker 2: bit about like what it what it's actually detecting, versus 142 00:07:50,920 --> 00:07:52,240 Speaker 2: what what they sort of claim it is. 143 00:07:53,680 --> 00:07:57,120 Speaker 4: Yeah, I think, well, I guess the overreaching kind of 144 00:07:57,200 --> 00:08:01,600 Speaker 4: theme here is we just don't know. ShotSpotter is very 145 00:08:02,320 --> 00:08:07,120 Speaker 4: not transparent about their data. There have been really no 146 00:08:07,280 --> 00:08:11,760 Speaker 4: peer reviewed, independent studies of the technology. So when we 147 00:08:11,800 --> 00:08:15,680 Speaker 4: make when we talk about, you know, how effective it is, 148 00:08:15,720 --> 00:08:18,400 Speaker 4: that that is a claim that ShotSpotter makes based off of, 149 00:08:19,040 --> 00:08:22,720 Speaker 4: you know, very little information given to the public about it, 150 00:08:23,160 --> 00:08:25,760 Speaker 4: you know, And that's kind of the big issue is 151 00:08:26,240 --> 00:08:29,680 Speaker 4: when you start getting down into the nitty gritty of 152 00:08:29,800 --> 00:08:33,840 Speaker 4: like what's actually going on. You notice that a lot 153 00:08:33,840 --> 00:08:37,839 Speaker 4: of the times what they consider a gunshot, police will 154 00:08:37,880 --> 00:08:41,000 Speaker 4: investigate and find out it was a firework, which if 155 00:08:41,000 --> 00:08:44,439 Speaker 4: you live in you know, I use Pasadena because it's 156 00:08:44,480 --> 00:08:47,199 Speaker 4: next to me. Out here in La you know, fireworks 157 00:08:47,200 --> 00:08:50,000 Speaker 4: are kind of how we celebrate and it's a different 158 00:08:50,040 --> 00:08:51,000 Speaker 4: kind of language out here. 159 00:08:51,040 --> 00:08:52,720 Speaker 3: You know, fireworks happen all the time. 160 00:08:52,840 --> 00:08:55,920 Speaker 4: So once you start getting into looking at some of 161 00:08:55,960 --> 00:08:58,520 Speaker 4: the data that I have been able to get, you 162 00:08:58,559 --> 00:09:03,000 Speaker 4: start seeing that, you know, maybe they claim it was 163 00:09:03,040 --> 00:09:05,800 Speaker 4: a gunshot, but when police show up, they don't find 164 00:09:05,800 --> 00:09:09,400 Speaker 4: any evidence of a gun crime, and sometimes they find 165 00:09:09,440 --> 00:09:12,520 Speaker 4: out it was a car backfiring or construction equipment and 166 00:09:12,600 --> 00:09:16,720 Speaker 4: all of that. And that just kind of shows, you know, 167 00:09:17,320 --> 00:09:21,680 Speaker 4: their claim that it's effective at identifying gunshots as you know, 168 00:09:22,080 --> 00:09:24,199 Speaker 4: very questionable to make that claim. 169 00:09:25,640 --> 00:09:28,960 Speaker 1: Yeah, and you know their ninety seven like the ninety 170 00:09:29,040 --> 00:09:32,080 Speaker 1: seven percent figure that they cite in their marketing material 171 00:09:32,640 --> 00:09:37,600 Speaker 1: is based on police reporting back to ShotSpotter that there 172 00:09:37,800 --> 00:09:41,640 Speaker 1: was a mistake, right like for Shotswatter to count a 173 00:09:42,679 --> 00:09:44,719 Speaker 1: the to count like a gunshot or to count of 174 00:09:44,800 --> 00:09:47,320 Speaker 1: sound as an error, the police have to report it 175 00:09:47,360 --> 00:09:50,520 Speaker 1: back to ShotSpotter, right, So it's almost like by default, 176 00:09:50,520 --> 00:09:53,120 Speaker 1: if they hear nothing, they have one hundred percent accuracy rate. 177 00:09:53,320 --> 00:09:56,320 Speaker 1: But it's that you know, they're informed of this, they 178 00:09:56,480 --> 00:09:59,200 Speaker 1: you know, will adjust that rate well. 179 00:09:59,200 --> 00:10:02,080 Speaker 2: And also I mean that that's a metric that relies 180 00:10:02,240 --> 00:10:05,360 Speaker 2: on the cops telling them. It relies on the cops 181 00:10:05,400 --> 00:10:09,480 Speaker 2: taking an extra step in an investigation. And these are like, 182 00:10:09,720 --> 00:10:12,120 Speaker 2: you are dealing with one of the most notoriously lazy 183 00:10:12,160 --> 00:10:15,319 Speaker 2: group of people, like in the entire country. Like I 184 00:10:15,880 --> 00:10:18,160 Speaker 2: have I have watched these people on duty in Chicago. 185 00:10:18,240 --> 00:10:20,360 Speaker 2: They spend like eighty percent of their time standing around 186 00:10:20,360 --> 00:10:23,520 Speaker 2: on their phones playing candy Crush, right, Like this this 187 00:10:23,720 --> 00:10:29,000 Speaker 2: entire statistics thing requires them to do another step. It's like, like, 188 00:10:29,360 --> 00:10:32,280 Speaker 2: what percentage of the time is a cop going to 189 00:10:32,360 --> 00:10:35,200 Speaker 2: admit that they ran out to this thing and like 190 00:10:35,320 --> 00:10:39,160 Speaker 2: drew their guns and we're doing their like whole oh 191 00:10:39,240 --> 00:10:42,000 Speaker 2: there's been gunshots thing, and then there's just nothing there, 192 00:10:45,120 --> 00:10:48,439 Speaker 2: Like I don't know, it seems like cast a pall 193 00:10:48,520 --> 00:10:52,160 Speaker 2: over even this, even even the sort of potential that 194 00:10:52,200 --> 00:10:55,839 Speaker 2: their data can be right, right, I mean we all 195 00:10:55,880 --> 00:11:00,240 Speaker 2: know that cops lie, Yeah, but we've seen them kind 196 00:11:00,240 --> 00:11:01,760 Speaker 2: of you shot spot or alerts. 197 00:11:01,840 --> 00:11:04,800 Speaker 4: You know, Chicago was one of the examples where they 198 00:11:04,800 --> 00:11:07,720 Speaker 4: were using it as cover to make illegal stops. 199 00:11:07,480 --> 00:11:10,280 Speaker 3: And you know, yeah that sort of thing. 200 00:11:10,400 --> 00:11:14,800 Speaker 4: So, you know, if there's room for that, it's hard 201 00:11:15,040 --> 00:11:18,320 Speaker 4: to then take what data police are giving them in 202 00:11:18,400 --> 00:11:23,040 Speaker 4: this way as accurate. And then again it goes back to, well, 203 00:11:23,120 --> 00:11:25,240 Speaker 4: we don't the public doesn't get to see any of 204 00:11:25,240 --> 00:11:28,920 Speaker 4: that information, so we don't get to make that I 205 00:11:28,960 --> 00:11:32,880 Speaker 4: guess distinction between the two and you know, know what's 206 00:11:32,920 --> 00:11:36,360 Speaker 4: best for people's communities because of that. 207 00:11:36,360 --> 00:11:39,360 Speaker 2: That's one of the things about this program that is 208 00:11:39,400 --> 00:11:42,760 Speaker 2: really alarming is that you have this massive aillance technology 209 00:11:44,000 --> 00:11:48,400 Speaker 2: and the people in charge of it were like the 210 00:11:48,440 --> 00:11:50,600 Speaker 2: people people who'd be in charge of sort of like 211 00:11:51,120 --> 00:11:54,920 Speaker 2: deciding whether or not you want it. It's like like 212 00:11:54,960 --> 00:11:59,120 Speaker 2: both both the general public and like city mayors et cetera, 213 00:11:59,160 --> 00:12:02,440 Speaker 2: et cetera, seem to have of so little information about 214 00:12:02,480 --> 00:12:07,080 Speaker 2: whether about what it's even doing that it's incredibly difficult 215 00:12:07,120 --> 00:12:08,959 Speaker 2: to make any kind of like any kind of sort 216 00:12:08,960 --> 00:12:11,440 Speaker 2: of data based choice. All you have is sort of 217 00:12:12,520 --> 00:12:17,000 Speaker 2: this combination of like the company going oh yeah, well, 218 00:12:17,040 --> 00:12:19,280 Speaker 2: obviously their stuff works, and then this sort of well, 219 00:12:19,400 --> 00:12:20,920 Speaker 2: this is the thing that's been happening in Chicago, is 220 00:12:20,920 --> 00:12:22,720 Speaker 2: this sort of like crime panic stuff they just that 221 00:12:22,760 --> 00:12:25,360 Speaker 2: people just fall back on, and they combine this sort 222 00:12:25,400 --> 00:12:28,360 Speaker 2: of crime panic with just the assumption that it works 223 00:12:28,720 --> 00:12:31,760 Speaker 2: because that's what it says in the box. And that's 224 00:12:31,760 --> 00:12:34,079 Speaker 2: a I think a really alarming combination to me. 225 00:12:35,160 --> 00:12:39,479 Speaker 1: Yeah, I mean, I think the fact that city council 226 00:12:39,960 --> 00:12:42,880 Speaker 1: members are kept in the dark about the locations of 227 00:12:42,920 --> 00:12:45,040 Speaker 1: these things, as are you know, the police departments to 228 00:12:45,120 --> 00:12:47,880 Speaker 1: pay for the cities to pay for it. I think 229 00:12:47,880 --> 00:12:51,720 Speaker 1: it's you know, something that's really been quite interesting as 230 00:12:51,840 --> 00:12:54,200 Speaker 1: after we published is that, you know, I've gotten a 231 00:12:54,200 --> 00:12:56,640 Speaker 1: bunch of emails from city council members asking, you know, 232 00:12:56,679 --> 00:12:58,360 Speaker 1: asking me if I can provide them data about the 233 00:12:58,400 --> 00:13:02,800 Speaker 1: locations because they can't even get them from the company, right. So, yeah, 234 00:13:02,800 --> 00:13:04,560 Speaker 1: there's a lot of transparency issues here. 235 00:13:05,640 --> 00:13:08,920 Speaker 4: Yeah, and you know, this is a public this is 236 00:13:08,920 --> 00:13:12,600 Speaker 4: a tool being paid with public money. You know. Another 237 00:13:12,640 --> 00:13:14,840 Speaker 4: thing we found in the data was that there are 238 00:13:14,840 --> 00:13:17,400 Speaker 4: a list of sensors that are broken or out of 239 00:13:17,480 --> 00:13:22,880 Speaker 4: service or anything like that. In talking to various police departments, 240 00:13:23,240 --> 00:13:27,520 Speaker 4: ShotSpotter doesn't let them know when that happens, and you know, 241 00:13:27,800 --> 00:13:30,440 Speaker 4: referred us to talk as the ShotSpot about that. So 242 00:13:31,240 --> 00:13:34,080 Speaker 4: you know, not even the functionality of like how many 243 00:13:34,280 --> 00:13:38,160 Speaker 4: sensors are down are really communicated and that's a huge problem. 244 00:13:38,280 --> 00:13:43,839 Speaker 4: But like again this data, as a journalist to investigate it, 245 00:13:43,880 --> 00:13:48,600 Speaker 4: to request documents, I can count at least three separate 246 00:13:48,960 --> 00:13:52,280 Speaker 4: cities where shot spot are intervened and said the release 247 00:13:52,280 --> 00:13:57,199 Speaker 4: of the data would be a trade secret. And so therefore, yeah, 248 00:13:57,480 --> 00:14:03,520 Speaker 4: so like even any data that shows transparency of like 249 00:14:04,040 --> 00:14:08,040 Speaker 4: anything more detailed than just an alert that many cities have, 250 00:14:09,000 --> 00:14:12,640 Speaker 4: ShotSpotter won't release because it is quote unquote a trade secret. 251 00:14:13,320 --> 00:14:18,320 Speaker 4: Mind you, I have gotten documents from other cities that 252 00:14:18,360 --> 00:14:21,080 Speaker 4: are more detailed, and then when I request those from 253 00:14:21,120 --> 00:14:23,560 Speaker 4: other cities, shot Spotter intervenes and goes, no, that's a 254 00:14:23,560 --> 00:14:24,320 Speaker 4: trade secret. 255 00:14:24,440 --> 00:14:27,360 Speaker 3: So it's this kind of. 256 00:14:28,880 --> 00:14:32,680 Speaker 4: Trying to hide the transparency that then adds more skepticism 257 00:14:32,760 --> 00:14:38,840 Speaker 4: to the effectiveness and usefulness of the product, which the 258 00:14:38,880 --> 00:14:42,840 Speaker 4: public I believe everyone would agree deserves a right to know, 259 00:14:42,960 --> 00:14:45,080 Speaker 4: especially if it's taxpayer money. 260 00:14:45,760 --> 00:14:49,160 Speaker 2: Yeah, and so it's a lot of money too, So 261 00:14:49,200 --> 00:14:50,920 Speaker 2: speaking of a lot of money, Unfortunately we have to 262 00:14:50,920 --> 00:14:53,440 Speaker 2: take an ad break, so we will be back in 263 00:14:53,520 --> 00:15:00,479 Speaker 2: a second. 264 00:15:05,240 --> 00:15:06,120 Speaker 3: Okay, we are back. 265 00:15:06,600 --> 00:15:09,560 Speaker 2: Something I wanted to talk about with the way that 266 00:15:09,600 --> 00:15:14,360 Speaker 2: these sensors are used, so about actually, okay, sorry, I 267 00:15:14,360 --> 00:15:17,960 Speaker 2: should have actually figured out the exact date after which 268 00:15:17,960 --> 00:15:20,640 Speaker 2: the story originally came out, but maybe like four or 269 00:15:20,680 --> 00:15:22,840 Speaker 2: five days after your story came out. There was a 270 00:15:22,880 --> 00:15:26,640 Speaker 2: story that came out of Chicago about a sort of 271 00:15:27,520 --> 00:15:32,040 Speaker 2: effectively the cover up of a case where CPD was 272 00:15:32,440 --> 00:15:35,480 Speaker 2: responding to a shot spotter ping and it was just 273 00:15:35,600 --> 00:15:38,560 Speaker 2: like a thirteen year old kid shooting off fireworks and 274 00:15:38,720 --> 00:15:41,120 Speaker 2: the cop showed up and immediately started shooting, and like, 275 00:15:41,160 --> 00:15:44,560 Speaker 2: thankfully cops can't set of a barn, so the kid 276 00:15:44,560 --> 00:15:48,120 Speaker 2: didn't get shot, but like, this child had a cop 277 00:15:48,160 --> 00:15:50,880 Speaker 2: shoot at him while the kid was running towards the 278 00:15:50,880 --> 00:15:51,520 Speaker 2: cop going. 279 00:15:51,360 --> 00:15:53,760 Speaker 3: No, it was fireworks. Yeah. 280 00:15:53,800 --> 00:15:58,160 Speaker 2: So I was wondering what kind, like, you know, how 281 00:15:58,240 --> 00:15:59,960 Speaker 2: many of those kinds of stories did you run into 282 00:16:00,080 --> 00:16:03,040 Speaker 2: when you were sort of like doing this, running into 283 00:16:03,040 --> 00:16:06,360 Speaker 2: the story, and yeah, what is the impact of that 284 00:16:06,400 --> 00:16:07,200 Speaker 2: stuff sort of been? 285 00:16:08,160 --> 00:16:10,400 Speaker 3: You know, I. 286 00:16:10,320 --> 00:16:14,880 Speaker 1: Think that that example that you bring up is particularly egregious. 287 00:16:15,360 --> 00:16:20,120 Speaker 1: But what happens more often, I think, are these there's 288 00:16:20,120 --> 00:16:23,960 Speaker 1: sort of like less dramatic events where you know, sound 289 00:16:24,000 --> 00:16:28,800 Speaker 1: thinking or ShotSpotter will detect two shots and deploy cops 290 00:16:28,880 --> 00:16:33,960 Speaker 1: to a corner, and you know, they'll detain someone on 291 00:16:34,000 --> 00:16:36,400 Speaker 1: the scene, run their name through their you know, their 292 00:16:36,440 --> 00:16:38,840 Speaker 1: databases and find that this guy's got a bench warrant, 293 00:16:39,120 --> 00:16:41,520 Speaker 1: or you know, pick someone up on a misdemeanor. Right. So, 294 00:16:41,680 --> 00:16:45,800 Speaker 1: like I think, you know, while there are some really 295 00:16:45,840 --> 00:16:49,360 Speaker 1: egregious examples, the thing that that I think about a 296 00:16:49,400 --> 00:16:53,320 Speaker 1: lot here is that is just how much unnecessary, how 297 00:16:53,320 --> 00:16:56,720 Speaker 1: many unnecessary arrests are happening because of ShotSpotter, right, how 298 00:16:56,760 --> 00:17:00,440 Speaker 1: many people are being picked up on bullshit essentially? 299 00:17:02,280 --> 00:17:05,520 Speaker 4: Yeah, and you know that that recent case in Chicago 300 00:17:05,520 --> 00:17:08,239 Speaker 4: with the kid with the firework, and you know, it 301 00:17:08,280 --> 00:17:11,880 Speaker 4: wasn't too long ago that you know, Adam Toledo was shot. Yeah, 302 00:17:11,920 --> 00:17:14,600 Speaker 4: you know, a thirteen year old kid for the same reason. 303 00:17:14,640 --> 00:17:17,919 Speaker 4: Cops were responding to a shot spotter alert. And the 304 00:17:18,040 --> 00:17:21,320 Speaker 4: Chicago o i G and their report about it kind 305 00:17:21,320 --> 00:17:24,159 Speaker 4: of highlighted one of the things, which is cops are 306 00:17:24,240 --> 00:17:29,439 Speaker 4: just primed to be you know, expecting you know, gunfire 307 00:17:29,880 --> 00:17:33,080 Speaker 4: somebody shooting at them and everything, and you know, I 308 00:17:33,080 --> 00:17:35,760 Speaker 4: think that's that is a danger. You know, but again 309 00:17:36,040 --> 00:17:38,280 Speaker 4: to what Dreve's saying is like it also leads to 310 00:17:38,280 --> 00:17:42,280 Speaker 4: a lot of unnecessary stops. It opens up people to 311 00:17:42,320 --> 00:17:46,760 Speaker 4: be profiled and padded down, and you know, so both 312 00:17:47,560 --> 00:17:50,159 Speaker 4: options are not great, you know, when you consider the 313 00:17:50,960 --> 00:17:54,320 Speaker 4: harm that this causes. It's just we all know that, Like, 314 00:17:54,760 --> 00:17:57,439 Speaker 4: cops are very jumpy to begin with, So you know, 315 00:17:57,480 --> 00:18:00,320 Speaker 4: they hear a firework or you know, an acorn hits 316 00:18:00,359 --> 00:18:02,800 Speaker 4: their cop car or something. We all know that, Like, 317 00:18:03,680 --> 00:18:07,840 Speaker 4: that's probably not what we need police to be expecting 318 00:18:07,960 --> 00:18:10,280 Speaker 4: on a call. And so you're just telling people, oh, 319 00:18:10,400 --> 00:18:13,040 Speaker 4: gunshots and then they're going to run in expecting to 320 00:18:13,080 --> 00:18:17,480 Speaker 4: be fired upon. And I don't think that's great for society. 321 00:18:18,440 --> 00:18:20,960 Speaker 3: Garrison, you were talking a bit about that, So. 322 00:18:24,560 --> 00:18:26,440 Speaker 5: I don't know if there's much more to say. 323 00:18:26,480 --> 00:18:29,359 Speaker 3: I think that's true. We do a lot of acord cop. 324 00:18:29,640 --> 00:18:31,960 Speaker 5: I think the A Corn incident stands on itself. I 325 00:18:31,960 --> 00:18:33,720 Speaker 5: don't think it needs to even be talked about. I 326 00:18:33,800 --> 00:18:39,240 Speaker 5: think it I think one sentences uh speaks speaks a 327 00:18:39,280 --> 00:18:43,200 Speaker 5: whole a whole book's worth of possible analysis of police 328 00:18:43,240 --> 00:18:48,640 Speaker 5: behavior and no being the notion of police rushing into 329 00:18:48,640 --> 00:18:51,840 Speaker 5: every situation thinking that there's there was there was a 330 00:18:51,840 --> 00:18:54,840 Speaker 5: gunshot obviously has its inherent problems. 331 00:18:55,320 --> 00:18:58,560 Speaker 4: Now, like mind you, a lot of the times, you know, 332 00:18:58,680 --> 00:19:02,520 Speaker 4: and at least in in other cities, it's sixty to 333 00:19:02,680 --> 00:19:06,040 Speaker 4: eighty percent of the time they don't find anything, you know, 334 00:19:06,119 --> 00:19:11,119 Speaker 4: which I think is good in the sense that nobody's 335 00:19:11,160 --> 00:19:13,560 Speaker 4: being harmed or stopped, but it's also bad when you 336 00:19:13,640 --> 00:19:18,639 Speaker 4: consider the effectiveness and utility of the device, you know, 337 00:19:18,720 --> 00:19:22,639 Speaker 4: which Shaw Spotter has kind of distanced themselves from, you know, 338 00:19:22,720 --> 00:19:25,879 Speaker 4: this idea of preventing gun crime or lowering croun crime 339 00:19:25,960 --> 00:19:29,800 Speaker 4: and more in terms of like safety and arriving to 340 00:19:29,880 --> 00:19:35,680 Speaker 4: a scene quicker to render aid and help police find 341 00:19:35,720 --> 00:19:38,520 Speaker 4: shell case scenes. You know, you've seen over the years 342 00:19:38,800 --> 00:19:42,520 Speaker 4: the kind of switch of focus on what the technology does, 343 00:19:42,560 --> 00:19:45,520 Speaker 4: and that most certainly happened around the time they changed 344 00:19:45,560 --> 00:19:49,040 Speaker 4: their name to Sound thinking, yeah, I. 345 00:19:49,080 --> 00:19:50,720 Speaker 1: Think you know. The other thing I wanted to mention 346 00:19:50,840 --> 00:19:54,520 Speaker 1: here is that you know, from a different leaked internal 347 00:19:54,560 --> 00:19:59,560 Speaker 1: report from the State's Attorney's Office in Illinois, in Cook County, Illinois, 348 00:20:00,320 --> 00:20:03,040 Speaker 1: it found that like a third of arrests stemming from 349 00:20:03,080 --> 00:20:05,040 Speaker 1: a shot spotter alert actually had nothing to do with 350 00:20:05,080 --> 00:20:07,320 Speaker 1: the gun in the first place. So it's not even like, 351 00:20:07,520 --> 00:20:11,080 Speaker 1: you know, there eighty nine percent of alerts, don't you know, 352 00:20:11,200 --> 00:20:14,000 Speaker 1: result in finding a shell casing. It's that even when 353 00:20:14,000 --> 00:20:17,720 Speaker 1: there are arrests that occur from a ShotSpotter alert, thirty 354 00:20:17,720 --> 00:20:19,760 Speaker 1: percent of them have nothing to do with a gun, right, 355 00:20:19,800 --> 00:20:21,760 Speaker 1: And that just shows you sort of the criminalization of 356 00:20:21,800 --> 00:20:25,600 Speaker 1: what happened are of people in areas that have these microphones. 357 00:20:26,600 --> 00:20:29,560 Speaker 2: Yeah, and I think I think the combination of those 358 00:20:29,560 --> 00:20:33,040 Speaker 2: two things gets you to this point about Shotspotter's effectiveness, 359 00:20:33,080 --> 00:20:35,679 Speaker 2: which is that like, okay, so we've had ShotSpotter for. 360 00:20:35,680 --> 00:20:38,680 Speaker 3: A while in Chicago, right, Chicago. 361 00:20:38,280 --> 00:20:42,800 Speaker 2: Police do not solve murders like it's sub it's their 362 00:20:43,880 --> 00:20:45,640 Speaker 2: murder clearance rate. And you have to keep in mind 363 00:20:45,640 --> 00:20:48,360 Speaker 2: that murder clearance doesn't actually mean they solved a murder. 364 00:20:48,840 --> 00:20:51,560 Speaker 2: But like, even that jacked up murder clearance rate I 365 00:20:51,560 --> 00:20:54,520 Speaker 2: don't think has had like I think they may have 366 00:20:54,680 --> 00:20:57,240 Speaker 2: had one year in my entire life, over fifty percent, 367 00:20:57,280 --> 00:21:01,920 Speaker 2: and that was because murder clearance. If two people both 368 00:21:01,960 --> 00:21:04,439 Speaker 2: shoot each other and they both die, that counts as 369 00:21:04,440 --> 00:21:07,520 Speaker 2: a clearance or they find us their suspect dies in 370 00:21:07,560 --> 00:21:10,520 Speaker 2: like another way. So it's it's pretty clear that it's 371 00:21:10,560 --> 00:21:15,879 Speaker 2: not actually substantively contributing to Chicago Police Department solving murders. 372 00:21:15,920 --> 00:21:17,639 Speaker 2: Like you have a better than coin flip odds if 373 00:21:17,680 --> 00:21:20,920 Speaker 2: you kill someone in Chicago that like the police aren't 374 00:21:20,960 --> 00:21:24,600 Speaker 2: even gonna like really try to figure out what happened. 375 00:21:24,640 --> 00:21:26,040 Speaker 2: And so yeah, I think I think it makes a 376 00:21:26,080 --> 00:21:29,199 Speaker 2: lot of sense that they've been pivoting away from even 377 00:21:29,280 --> 00:21:32,560 Speaker 2: like even claiming that this can do anything to solve 378 00:21:32,560 --> 00:21:36,479 Speaker 2: gun violence, because it just clearly hasn't at all, and 379 00:21:36,640 --> 00:21:38,760 Speaker 2: it's instead it seems to be doing a bunch of 380 00:21:38,800 --> 00:21:43,200 Speaker 2: other stuff, which is like either throwing cops around doing 381 00:21:43,520 --> 00:21:47,240 Speaker 2: is like throwing cops around chasing like shadows, which either 382 00:21:47,280 --> 00:21:53,600 Speaker 2: results in them arresting just random people or like having 383 00:21:53,640 --> 00:21:59,720 Speaker 2: these really sort of terrifying incidents, or it just results 384 00:21:59,800 --> 00:22:01,800 Speaker 2: in straight up nothing. 385 00:22:02,560 --> 00:22:04,680 Speaker 3: Yeah, we're gonna go to ads. We'll be back in 386 00:22:05,359 --> 00:22:21,400 Speaker 3: however long capitalism dictates, So see you then we're back. 387 00:22:22,240 --> 00:22:25,120 Speaker 5: And I think, like looking at the effectiveness, like two 388 00:22:25,200 --> 00:22:28,280 Speaker 5: cities that have continued to deny shot spot or contracts 389 00:22:28,560 --> 00:22:32,760 Speaker 5: are Atlanta and Portland, two cities that spend a lot 390 00:22:32,840 --> 00:22:36,440 Speaker 5: of time thinking about how they equip their police, spend 391 00:22:36,480 --> 00:22:39,280 Speaker 5: a lot of time like making sure that their police 392 00:22:39,760 --> 00:22:45,359 Speaker 5: are able to serve the largest amount of the community possible. 393 00:22:47,119 --> 00:22:50,639 Speaker 5: And the fact that, like specifically Atlanta, with their massive 394 00:22:50,680 --> 00:22:54,280 Speaker 5: like flock program of and integrated camera network across the 395 00:22:54,280 --> 00:22:56,680 Speaker 5: whole city, like it is one of the most surveilled 396 00:22:56,720 --> 00:23:01,919 Speaker 5: cities in the country, if not most. The fact that 397 00:23:01,960 --> 00:23:04,640 Speaker 5: they are turning down this equipment for not being effective 398 00:23:04,720 --> 00:23:08,239 Speaker 5: enough and it being too costly is a sign for like, 399 00:23:08,720 --> 00:23:10,520 Speaker 5: beyond it just being a sign, it's also like a 400 00:23:10,560 --> 00:23:13,920 Speaker 5: look at what why other police departments are interested in 401 00:23:13,960 --> 00:23:17,320 Speaker 5: this and like what it allows them to do and 402 00:23:17,400 --> 00:23:21,639 Speaker 5: being deployed to these various communities that have the what 403 00:23:21,760 --> 00:23:25,320 Speaker 5: like twenty five thousand sensors. But no, I mean like 404 00:23:25,920 --> 00:23:28,320 Speaker 5: they've constantly tried to send this stuff to Atlanta and 405 00:23:28,359 --> 00:23:31,679 Speaker 5: it's like and it's just it's simply not happening. And 406 00:23:31,840 --> 00:23:36,240 Speaker 5: even after twenty twenty that Portland's like, no, it's it's 407 00:23:36,440 --> 00:23:41,840 Speaker 5: it's it's two super super useful examples to measure how 408 00:23:41,920 --> 00:23:44,040 Speaker 5: much this technology actually is going to get used for 409 00:23:44,040 --> 00:23:47,480 Speaker 5: what they say it's being used for versus just having 410 00:23:47,520 --> 00:23:52,280 Speaker 5: an excuse to act like there's gunfire all across the city. 411 00:23:53,760 --> 00:23:58,359 Speaker 4: Yeah, and you know, I think when we start more 412 00:23:59,040 --> 00:24:03,440 Speaker 4: police departments are going to start relying more on technology, sure, 413 00:24:04,119 --> 00:24:08,960 Speaker 4: largely because many departments cannot hire more cops. Now this 414 00:24:09,080 --> 00:24:12,240 Speaker 4: isn't advocating me. You know, I don't want police departments 415 00:24:12,240 --> 00:24:15,000 Speaker 4: to hire more cops. You know, they've slowly defunded themselves 416 00:24:15,040 --> 00:24:18,480 Speaker 4: in that way. But like you know, cities like Los 417 00:24:18,520 --> 00:24:21,440 Speaker 4: Angeles are trying to grow their surveillance capabilities for that reason. 418 00:24:21,480 --> 00:24:24,760 Speaker 3: They just do not have enough. They say, they do 419 00:24:24,800 --> 00:24:25,720 Speaker 3: not have enough cops. 420 00:24:26,119 --> 00:24:28,879 Speaker 4: And so this is where kind of this surveillance capitalism 421 00:24:29,280 --> 00:24:32,479 Speaker 4: is going to really thrive, is police departments are going 422 00:24:32,480 --> 00:24:35,120 Speaker 4: to get desperate and they're going to start reaching out 423 00:24:35,480 --> 00:24:38,120 Speaker 4: and getting more invasive surveillance technology. 424 00:24:38,400 --> 00:24:40,240 Speaker 3: And you know, I. 425 00:24:40,119 --> 00:24:44,680 Speaker 4: Think in some cities, shot spot is kind of they're 426 00:24:44,720 --> 00:24:49,480 Speaker 4: a way of quieting the narratives about you know, the 427 00:24:49,520 --> 00:24:52,800 Speaker 4: growing gun violence and everything in their communities. You know, 428 00:24:52,840 --> 00:24:54,680 Speaker 4: they're like, oh, look, we've deployed this new toy to 429 00:24:54,760 --> 00:24:58,080 Speaker 4: kind of help us without really solving anything. Because we 430 00:24:58,160 --> 00:25:01,880 Speaker 4: all know cops aren't really good at solving crime. Yeah, 431 00:25:01,960 --> 00:25:04,600 Speaker 4: so it kind of gives them cover of like we're 432 00:25:04,640 --> 00:25:07,720 Speaker 4: bad at our jobs. So how do we make it 433 00:25:07,760 --> 00:25:10,399 Speaker 4: look like we're better. Well, let's you know, invest in 434 00:25:10,440 --> 00:25:13,040 Speaker 4: some new technology, so it looks like we're trying something. 435 00:25:13,640 --> 00:25:15,240 Speaker 4: But you know, at the end of the day, it's 436 00:25:15,240 --> 00:25:17,359 Speaker 4: a waste of money, and then the impacts of that 437 00:25:17,560 --> 00:25:20,560 Speaker 4: is harm, you know, greater than the good. 438 00:25:21,240 --> 00:25:23,840 Speaker 2: Yeah, it's like we're spending an enormous amount of money 439 00:25:23,880 --> 00:25:26,000 Speaker 2: to hurt people for no reason. 440 00:25:27,440 --> 00:25:28,280 Speaker 3: Yeah. 441 00:25:28,359 --> 00:25:31,280 Speaker 1: And you know, I think shot Spatter is only one 442 00:25:31,520 --> 00:25:36,160 Speaker 1: of Sound Thinking's offerings, right, Like they you know, when 443 00:25:36,160 --> 00:25:38,359 Speaker 1: they change their name to sound Thinking, it's sort of 444 00:25:38,400 --> 00:25:41,720 Speaker 1: like reflected this pivot in the company where now they 445 00:25:41,720 --> 00:25:45,160 Speaker 1: were going to start thinking more about like resource management. Right, 446 00:25:45,160 --> 00:25:48,400 Speaker 1: how do we how do we convince departments that our 447 00:25:48,440 --> 00:25:51,840 Speaker 1: technology is going to better help them allocate their resources? 448 00:25:51,840 --> 00:25:53,520 Speaker 1: And you know, surveillance is the way to do that. 449 00:25:53,560 --> 00:25:55,720 Speaker 1: We can measure where crime is, we can measure where 450 00:25:55,720 --> 00:25:57,920 Speaker 1: gunshots is and where gunshots are, and we can. 451 00:25:57,840 --> 00:25:59,320 Speaker 3: Deploy police there. 452 00:25:59,840 --> 00:26:02,960 Speaker 1: And one of shot spot or recently Sound Thinking had 453 00:26:03,119 --> 00:26:07,600 Speaker 1: acquired like a notorious predictive policing company called Predpole. That 454 00:26:07,760 --> 00:26:12,160 Speaker 1: happens I think earlier this year, so you know, they're 455 00:26:12,200 --> 00:26:14,760 Speaker 1: trying to expand their offerings here to be this kind 456 00:26:14,760 --> 00:26:19,720 Speaker 1: of resource management solution for departments semia. 457 00:26:19,800 --> 00:26:23,639 Speaker 5: Did you have anything else you wanted to bring up here? 458 00:26:24,280 --> 00:26:26,000 Speaker 2: Yeah, I guess, I guess there's one more thing I 459 00:26:26,000 --> 00:26:27,800 Speaker 2: wanted to talk about what you said. So one of 460 00:26:27,840 --> 00:26:30,800 Speaker 2: the things that I've heard from places that have gotten 461 00:26:30,840 --> 00:26:34,679 Speaker 2: rid of their contracts is that shot spotters not like 462 00:26:34,960 --> 00:26:39,040 Speaker 2: taking their censors down even when cities stopped doing contracts. 463 00:26:39,200 --> 00:26:40,919 Speaker 2: I was wondering what you two sort of know about that. 464 00:26:41,840 --> 00:26:42,080 Speaker 5: Yeah. 465 00:26:42,160 --> 00:26:45,520 Speaker 4: I reached out to Dayton, Ohio, who recently got rid 466 00:26:45,560 --> 00:26:48,400 Speaker 4: of their contracts, and I reached out, you know, because 467 00:26:48,440 --> 00:26:52,520 Speaker 4: I was like asking departments who had it, like are 468 00:26:52,520 --> 00:26:56,080 Speaker 4: you aware of the status of the censors or do 469 00:26:56,119 --> 00:26:57,160 Speaker 4: you know the locations? 470 00:26:57,600 --> 00:26:57,800 Speaker 3: You know? 471 00:26:57,880 --> 00:27:01,600 Speaker 4: Both knows. And then I asked Dayton, you know, well, 472 00:27:02,119 --> 00:27:04,800 Speaker 4: now that the contract's over, what happens to the sensors? 473 00:27:05,040 --> 00:27:09,720 Speaker 4: And they basically said, we don't know. That's Shotswater's responsibility, 474 00:27:10,160 --> 00:27:16,199 Speaker 4: and their responsibility is maintenance and care and removal and installation. 475 00:27:16,920 --> 00:27:18,399 Speaker 3: So who knows. 476 00:27:18,880 --> 00:27:23,400 Speaker 4: Obviously somebody knows because It's not like some person can 477 00:27:23,480 --> 00:27:28,040 Speaker 4: just start climbing telephone poles and installing surveillance equipment. So obviously, 478 00:27:28,080 --> 00:27:30,680 Speaker 4: you know, somebody is issuing permits to install stuff and 479 00:27:31,200 --> 00:27:33,920 Speaker 4: put stuff up there. But like you know, as we're 480 00:27:33,960 --> 00:27:37,280 Speaker 4: finding out, city council members don't know, police departments don't know, 481 00:27:37,880 --> 00:27:41,520 Speaker 4: and so who knows what happens to these devices afterwards? 482 00:27:41,600 --> 00:27:45,120 Speaker 4: And then say a city like Chicago, you know, say 483 00:27:45,119 --> 00:27:48,440 Speaker 4: they cancel their contracts, Well, a new mayor can come 484 00:27:48,440 --> 00:27:51,600 Speaker 4: in and then just instantly turn them back on, you know, 485 00:27:51,680 --> 00:27:54,120 Speaker 4: and that way, And so that's kind of the other 486 00:27:54,160 --> 00:27:56,440 Speaker 4: thing we're solely starting to learn here. It's more cities 487 00:27:56,800 --> 00:28:00,639 Speaker 4: start canceling their contracts or not renewing them. You know, 488 00:28:00,720 --> 00:28:04,879 Speaker 4: it is what happens to the technology afterwards, and we 489 00:28:04,960 --> 00:28:05,520 Speaker 4: don't know. 490 00:28:06,600 --> 00:28:08,000 Speaker 3: Which is not a great sign. 491 00:28:08,720 --> 00:28:11,880 Speaker 2: Like, I mean, you know, it's not good that there's 492 00:28:11,920 --> 00:28:15,280 Speaker 2: just a bunch of state surveillance technology around all the time, 493 00:28:15,960 --> 00:28:20,679 Speaker 2: but it somehow feels even worse that we don't have 494 00:28:20,720 --> 00:28:22,879 Speaker 2: any idea what happens to even if the state decides 495 00:28:22,920 --> 00:28:25,800 Speaker 2: it doesn't want to use it. So yeah, I guess 496 00:28:25,920 --> 00:28:28,480 Speaker 2: on that somewhat disquieting note. 497 00:28:29,680 --> 00:28:31,960 Speaker 3: Do you have anything else you wanted to make sure 498 00:28:32,000 --> 00:28:32,440 Speaker 3: you get to. 499 00:28:33,920 --> 00:28:34,720 Speaker 1: Oh nothing for me. 500 00:28:36,359 --> 00:28:36,439 Speaker 6: No. 501 00:28:36,600 --> 00:28:39,360 Speaker 4: I mean this is you know, thanks to you know, 502 00:28:40,040 --> 00:28:42,640 Speaker 4: somebody brave enough to send us the info and it's 503 00:28:42,640 --> 00:28:44,960 Speaker 4: the only way this information has been able to get out. 504 00:28:45,040 --> 00:28:50,960 Speaker 4: And I think if I implore the public to really 505 00:28:51,040 --> 00:28:54,080 Speaker 4: research and dig into this technology, if their cities are 506 00:28:54,080 --> 00:28:57,080 Speaker 4: thinking about extending their contracts or bringing a contract in 507 00:28:57,600 --> 00:29:00,200 Speaker 4: and really questioning and trying to get shots by are 508 00:29:00,200 --> 00:29:02,880 Speaker 4: on the record to answer for some of these things. 509 00:29:03,000 --> 00:29:07,160 Speaker 4: And you know, we know what works and what doesn't work. 510 00:29:07,640 --> 00:29:10,080 Speaker 4: And I think most cities are starting to find out 511 00:29:10,120 --> 00:29:13,160 Speaker 4: that there is a better use of that amount of 512 00:29:13,200 --> 00:29:17,760 Speaker 4: money to stop these sort of gun crimes, interventions and 513 00:29:18,400 --> 00:29:22,280 Speaker 4: other more community based solutions rather than just dumping money 514 00:29:22,320 --> 00:29:26,720 Speaker 4: into surveillance technology. And you know, you can get a 515 00:29:26,720 --> 00:29:30,960 Speaker 4: lot done with an eight million dollars. Yeah, you know, 516 00:29:32,160 --> 00:29:34,080 Speaker 4: it's just it's just like there's always money in the 517 00:29:34,080 --> 00:29:36,080 Speaker 4: Banana Stan sort of thing, and there's like there's always 518 00:29:36,080 --> 00:29:39,280 Speaker 4: money for police. So it's just like, why don't we 519 00:29:39,400 --> 00:29:42,640 Speaker 4: just retransform that money into things that actually work in 520 00:29:42,680 --> 00:29:44,080 Speaker 4: these communities. 521 00:29:43,560 --> 00:29:46,000 Speaker 3: And you know, go behind that. 522 00:29:46,240 --> 00:29:50,880 Speaker 2: So yeah, and I think I don't know. Hopefully, hopefully 523 00:29:50,920 --> 00:29:58,719 Speaker 2: this will encourage more cities to stop paying for this shit. Yeah, 524 00:29:58,760 --> 00:30:02,560 Speaker 2: So where can people find you choose work? I mean, 525 00:30:02,600 --> 00:30:04,400 Speaker 2: I know, like obviously this one's on Wired, but I 526 00:30:04,720 --> 00:30:10,320 Speaker 2: are online places, et cetera, et cetera, social media places, 527 00:30:10,360 --> 00:30:16,400 Speaker 2: plug yourself, go yayh. 528 00:30:13,960 --> 00:30:16,360 Speaker 1: Well, I'm I'm. You can find my stuff on Wired 529 00:30:16,400 --> 00:30:20,880 Speaker 1: dot com and I'm on X or Twitter or whatever 530 00:30:20,880 --> 00:30:23,360 Speaker 1: you want to call it at d Marrow and on 531 00:30:23,440 --> 00:30:25,840 Speaker 1: Blue Sky at d Marrow d M e h r. Oh. 532 00:30:27,760 --> 00:30:33,160 Speaker 4: And you can find me on Instagram and Twitter with 533 00:30:33,720 --> 00:30:37,240 Speaker 4: the user name joey never and Joe And then my 534 00:30:37,280 --> 00:30:40,360 Speaker 4: writings have been in a local press out here in 535 00:30:40,560 --> 00:30:44,200 Speaker 4: La La Public Press and Knock La. 536 00:30:45,440 --> 00:30:47,560 Speaker 2: Yeah, and thank you to both so much for coming on, 537 00:30:48,560 --> 00:30:49,400 Speaker 2: Thanks for having. 538 00:30:49,320 --> 00:30:52,480 Speaker 4: Us, Thank you so much, appreciate it. 539 00:30:53,400 --> 00:30:56,520 Speaker 2: Yeah, and I'm gonna encourage everyone else to go get 540 00:30:56,520 --> 00:30:59,880 Speaker 2: your city to not use this stuff because it sucks. 541 00:31:00,280 --> 00:31:02,760 Speaker 2: All right, this has been this has been naked appened here. 542 00:31:02,800 --> 00:31:04,920 Speaker 2: You can find us in the usual places. 543 00:31:06,760 --> 00:31:15,240 Speaker 6: Goodbye, it could happen here as a production of cool 544 00:31:15,320 --> 00:31:18,200 Speaker 6: Zone Media. For more podcasts from cool Zone Media, visit 545 00:31:18,280 --> 00:31:20,960 Speaker 6: our website. Coolzonemedia dot com or check us out on 546 00:31:21,000 --> 00:31:24,680 Speaker 6: the iHeartRadio app, Apple podcasts, or wherever you listen to podcasts, 547 00:31:24,920 --> 00:31:27,040 Speaker 6: you can find sources for It could happen here, Updated 548 00:31:27,120 --> 00:31:31,160 Speaker 6: monthly at coolzonemedia dot com slash sources. Thanks for listening.