Speaker 1: Al Zone Media.
Speaker 2: Welcome dick it app and here a podcast sometimes occasion,
Speaker 2: not even that occasionally, that's about a bunch of not
Speaker 2: very functional mass surveillance technology that's being deployed against all
Speaker 2: of us. I'm your host, Miah Wong, also with me
Speaker 2: as Garrison Davis.
Speaker 3: Yeah, and so today.
Speaker 2: We're gonna be talking more about something we've talked about
Speaker 2: on the show I think a couple of times, but
Speaker 2: that is the shot Spotter program, and there was recently
Speaker 2: a leak of the locations of all of shot spotters
Speaker 2: like gunfire sensors, and with us to talk about it
Speaker 2: are the people who got the leak and wrote the
Speaker 2: article about about where the shotspatter sensors are, and that
Speaker 2: is Drew Mei Rotra who's a staff writer at Wired,
Speaker 2: and Joey Scott, who's a freelance investigative journalist and photographer.
Speaker 3: And both of you two, welcome to the show.
Speaker 1: Thanks for having us.
Speaker 4: Yes, thank you.
Speaker 2: Yeah, I'm glad to be talking to you about this. So,
Speaker 2: I guess for the people who don't remember or like
Speaker 2: have not listened to other episodes we've done about this,
Speaker 2: or have read this article which you should go read
Speaker 2: at Wired it's great. Can you describe what ShotSpotter is
Speaker 2: and what it's supposed to do versus what it actually does?
Speaker 1: Sure, Joey, you want me to take this or do
Speaker 1: you want to do it?
Speaker 3: Yeah? Go ahead, sure well.
Speaker 1: ShotSpotter is a sort of controversial gunshot detection system built
Speaker 1: by the company Sound Thinking. On the face of it,
Speaker 1: the tech is sort of straightforward. The company will basically
Speaker 1: install little sensors on street lights and traffic signs and
Speaker 1: a jurisdiction, and these sensors are sort of like algorithmically
Speaker 1: tuned to detect gunshots. So when one of these sensors
Speaker 1: here's something, it basically will send an alert to an
Speaker 1: incident review center will then like vet the sound make
Speaker 1: sure it was actually a gunshot before we're then forwarding
Speaker 1: it to dispatchers who send a cop to investigate the sound.
Speaker 1: You know, activists and academics have been basically saying for
Speaker 1: years that this tech is inaccurate and primes police basically
Speaker 1: to go to low income community communities of color expecting
Speaker 1: gunshots when likely they won't find any.
Speaker 2: Yeah, and I think I think the specifically low income
Speaker 2: communities of color thing is a big part of this
Speaker 2: because so you all created a map of where the
Speaker 2: shot spotter sensors are from the data you got, and
Speaker 2: I looked at Chicago one and immediately I was like,
Speaker 2: I recognized that map. That is the map of where
Speaker 2: the not white people are in the City's.
Speaker 1: Yeah, yeah, it's it's it's it's stark. I think a
Speaker 1: lot a lot of the responses that I've seen on
Speaker 1: Twitter and you know, in my email inboxer essentially that like, look,
Speaker 1: this is just a map of where all the not
Speaker 1: white people are and whatever city that it's deployed in.
Speaker 2: Yeah, and y'all did some analysis of what you found
Speaker 2: sort of statistically about where these centers ended up and
Speaker 2: like the sort of the the I guess like class
Speaker 2: and racial composition of those places. Can you talk about
Speaker 2: that a little bit?
Speaker 1: Sure, I'll take this one just because I worked on
Speaker 1: the analysis. So yeah, I mean, what we found is
Speaker 1: that more than twelve million Americans live in a neighborhood
Speaker 1: with at least one shot Spotter sensor. We basically joined
Speaker 1: census data onto the locations of every single SHOTSPO microphone
Speaker 1: and looked at the demographic composition of those neighborhoods. And
Speaker 1: you know what, we found is that an aggregate, nearly
Speaker 1: seventy percent of the people who live in a neighborhood
Speaker 1: with one set with at least one sensor identify as
Speaker 1: either black or Latine. Nearly three quarters of those neighborhoods
Speaker 1: are a majority not white, and the average household income
Speaker 1: in a neighborhood with at least one sensor is fifty
Speaker 1: dollars a year, So these are low income communities of color.
Speaker 1: It's kind of hard to describe it in any other way.
Speaker 2: Yeah, And you know, one of the things, and this
Speaker 2: has been a thing for so I'm in Chicago. There's
Speaker 2: been a huge series of fights over getting rid Ofshotspotter here.
Speaker 2: And one of the things you hear all the time
Speaker 2: that shot is the shots are People will go, no, well,
Speaker 2: we don't use race as a factor for yeah, But
Speaker 2: like ShotSpotter insists that they don't use race at all
Speaker 2: in in determining where where they put these sensors. But kabba,
Speaker 2: they've still managed to somehow create this map. And I
Speaker 2: don't know, I I'm wondering what you think about like
Speaker 2: their response and whether you and I guess this is
Speaker 2: more of a subjective think, like how much do you
Speaker 2: actually believe them when they when they say.
Speaker 4: This, Well, I think when you know, we are investigating this,
Speaker 4: we found that the police don't even know where these
Speaker 4: locations are, and so they're just given ShotSpot data of
Speaker 4: where to put this stuff. So the police can kind
Speaker 4: of wipe their hands of like, oh, we insisted that
Speaker 4: they put it in this place or anything like that.
Speaker 4: And I think, you know, Drove can probably speak to this,
Speaker 4: but you know, the argument is this is where all
Speaker 4: the shootings are, and so that's where they are. But
Speaker 4: you know, when you investigate that, it doesn't call into
Speaker 4: effect like in other parts of the country outside of
Speaker 4: like Chicago or something. You look at gun violence and
Speaker 4: where these alerts are, you know, they aren't just where
Speaker 4: the alerts are. And you know, Pasadena is an example.
Speaker 4: You know, shootings happen outside of where the alerts are,
Speaker 4: but they're specifically in a very specific part of Pasadena
Speaker 4: that is poor and non white.
Speaker 1: So yeah, yeah, and I think you know, when we
Speaker 1: spoke to sound Thinking, you know, I think it's important
Speaker 1: to point out here that they did not dispute or
Speaker 1: findings or the sort of authenticity of the dock. But
Speaker 1: you know, they said what you would expect that the
Speaker 1: sensor deployment is not really informed by race, and you know,
Speaker 1: the way it works, as Joey says, is that the
Speaker 1: company basically asks police department who purchased the systems for
Speaker 1: data about gun violence, which Sound Thinking says is objective.
Speaker 1: But we have no idea what that data actually looks like, right.
Speaker 1: We don't know if it's all crime data, which might
Speaker 1: be you know, a subject to enforcement bias, right if
Speaker 1: they include things like drug crimes on their drug arrests.
Speaker 1: So we just don't really know why Sound Thinking, you know,
Speaker 1: makes a recommended plan for their sensor deployment. The other
Speaker 1: thing that Sound Thinking had told me is that, you know,
Speaker 1: sometimes they'll ask for data and they'll do it this
Speaker 1: sort of data informed way, but other times cops will
Speaker 1: just say, like, look, we want the we want the
Speaker 1: deployment in this area, and that might include like a
Speaker 1: stadium or a school or places where people gather.
Speaker 3: So you know, it's kind of we don't really know.
Speaker 1: Why exactly Sound Thinking is deploying its censors in any
Speaker 1: given location.
Speaker 2: Yeah, and having them be deployed by cops is like
Speaker 2: he is a spectacular way to have cop brain in
Speaker 2: terms in terms of locations, which not not not not
Speaker 2: I don't know, not an especially good way to get
Speaker 2: a statistically unbiased sampling of where you would potentially want
Speaker 2: these things. So I guess I guess the a thing
Speaker 2: we should talk about in terms of what the issues
Speaker 2: with the system is are. Okay, So shot spotter claims
Speaker 2: that it and this is something I've seen over and
Speaker 2: over and over again. It claims it is a ninety
Speaker 2: seven percent accuracy rate of detecting gunshots. There's just I
Speaker 2: I don't believe it. None of the research I've ever
Speaker 2: seen backs that up. You talk a bit about a
Speaker 2: bit about like what it what it's actually detecting, versus
Speaker 2: what what they sort of claim it is.
Speaker 4: Yeah, I think, well, I guess the overreaching kind of
Speaker 4: theme here is we just don't know. ShotSpotter is very
Speaker 4: not transparent about their data. There have been really no
Speaker 4: peer reviewed, independent studies of the technology. So when we
Speaker 4: make when we talk about, you know, how effective it is,
Speaker 4: that that is a claim that ShotSpotter makes based off of,
Speaker 4: you know, very little information given to the public about it,
Speaker 4: you know, And that's kind of the big issue is
Speaker 4: when you start getting down into the nitty gritty of
Speaker 4: like what's actually going on. You notice that a lot
Speaker 4: of the times what they consider a gunshot, police will
Speaker 4: investigate and find out it was a firework, which if
Speaker 4: you live in you know, I use Pasadena because it's
Speaker 4: next to me. Out here in La you know, fireworks
Speaker 4: are kind of how we celebrate and it's a different
Speaker 4: kind of language out here.
Speaker 3: You know, fireworks happen all the time.
Speaker 4: So once you start getting into looking at some of
Speaker 4: the data that I have been able to get, you
Speaker 4: start seeing that, you know, maybe they claim it was
Speaker 4: a gunshot, but when police show up, they don't find
Speaker 4: any evidence of a gun crime, and sometimes they find
Speaker 4: out it was a car backfiring or construction equipment and
Speaker 4: all of that. And that just kind of shows, you know,
Speaker 4: their claim that it's effective at identifying gunshots as you know,
Speaker 4: very questionable to make that claim.
Speaker 1: Yeah, and you know their ninety seven like the ninety
Speaker 1: seven percent figure that they cite in their marketing material
Speaker 1: is based on police reporting back to ShotSpotter that there
Speaker 1: was a mistake, right like for Shotswatter to count a
Speaker 1: the to count like a gunshot or to count of
Speaker 1: sound as an error, the police have to report it
Speaker 1: back to ShotSpotter, right, So it's almost like by default,
Speaker 1: if they hear nothing, they have one hundred percent accuracy rate.
Speaker 1: But it's that you know, they're informed of this, they
Speaker 1: you know, will adjust that rate well.
Speaker 2: And also I mean that that's a metric that relies
Speaker 2: on the cops telling them. It relies on the cops
Speaker 2: taking an extra step in an investigation. And these are like,
Speaker 2: you are dealing with one of the most notoriously lazy
Speaker 2: group of people, like in the entire country. Like I
Speaker 2: have I have watched these people on duty in Chicago.
Speaker 2: They spend like eighty percent of their time standing around
Speaker 2: on their phones playing candy Crush, right, Like this this
Speaker 2: entire statistics thing requires them to do another step. It's like, like,
Speaker 2: what percentage of the time is a cop going to
Speaker 2: admit that they ran out to this thing and like
Speaker 2: drew their guns and we're doing their like whole oh
Speaker 2: there's been gunshots thing, and then there's just nothing there,
Speaker 2: Like I don't know, it seems like cast a pall
Speaker 2: over even this, even even the sort of potential that
Speaker 2: their data can be right, right, I mean we all
Speaker 2: know that cops lie, Yeah, but we've seen them kind
Speaker 2: of you shot spot or alerts.
Speaker 4: You know, Chicago was one of the examples where they
Speaker 4: were using it as cover to make illegal stops.
Speaker 3: And you know, yeah that sort of thing.
Speaker 4: So, you know, if there's room for that, it's hard
Speaker 4: to then take what data police are giving them in
Speaker 4: this way as accurate. And then again it goes back to, well,
Speaker 4: we don't the public doesn't get to see any of
Speaker 4: that information, so we don't get to make that I
Speaker 4: guess distinction between the two and you know, know what's
Speaker 4: best for people's communities because of that.
Speaker 2: That's one of the things about this program that is
Speaker 2: really alarming is that you have this massive aillance technology
Speaker 2: and the people in charge of it were like the
Speaker 2: people people who'd be in charge of sort of like
Speaker 2: deciding whether or not you want it. It's like like
Speaker 2: both both the general public and like city mayors et cetera,
Speaker 2: et cetera, seem to have of so little information about
Speaker 2: whether about what it's even doing that it's incredibly difficult
Speaker 2: to make any kind of like any kind of sort
Speaker 2: of data based choice. All you have is sort of
Speaker 2: this combination of like the company going oh yeah, well,
Speaker 2: obviously their stuff works, and then this sort of well,
Speaker 2: this is the thing that's been happening in Chicago, is
Speaker 2: this sort of like crime panic stuff they just that
Speaker 2: people just fall back on, and they combine this sort
Speaker 2: of crime panic with just the assumption that it works
Speaker 2: because that's what it says in the box. And that's
Speaker 2: a I think a really alarming combination to me.
Speaker 1: Yeah, I mean, I think the fact that city council
Speaker 1: members are kept in the dark about the locations of
Speaker 1: these things, as are you know, the police departments to
Speaker 1: pay for the cities to pay for it. I think
Speaker 1: it's you know, something that's really been quite interesting as
Speaker 1: after we published is that, you know, I've gotten a
Speaker 1: bunch of emails from city council members asking, you know,
Speaker 1: asking me if I can provide them data about the
Speaker 1: locations because they can't even get them from the company, right. So, yeah,
Speaker 1: there's a lot of transparency issues here.
Speaker 4: Yeah, and you know, this is a public this is
Speaker 4: a tool being paid with public money. You know. Another
Speaker 4: thing we found in the data was that there are
Speaker 4: a list of sensors that are broken or out of
Speaker 4: service or anything like that. In talking to various police departments,
Speaker 4: ShotSpotter doesn't let them know when that happens, and you know,
Speaker 4: referred us to talk as the ShotSpot about that. So
Speaker 4: you know, not even the functionality of like how many
Speaker 4: sensors are down are really communicated and that's a huge problem.
Speaker 4: But like again this data, as a journalist to investigate it,
Speaker 4: to request documents, I can count at least three separate
Speaker 4: cities where shot spot are intervened and said the release
Speaker 4: of the data would be a trade secret. And so therefore, yeah,
Speaker 4: so like even any data that shows transparency of like
Speaker 4: anything more detailed than just an alert that many cities have,
Speaker 4: ShotSpotter won't release because it is quote unquote a trade secret.
Speaker 4: Mind you, I have gotten documents from other cities that
Speaker 4: are more detailed, and then when I request those from
Speaker 4: other cities, shot Spotter intervenes and goes, no, that's a
Speaker 4: trade secret.
Speaker 3: So it's this kind of.
Speaker 4: Trying to hide the transparency that then adds more skepticism
Speaker 4: to the effectiveness and usefulness of the product, which the
Speaker 4: public I believe everyone would agree deserves a right to know,
Speaker 4: especially if it's taxpayer money.
Speaker 2: Yeah, and so it's a lot of money too, So
Speaker 2: speaking of a lot of money, Unfortunately we have to
Speaker 2: take an ad break, so we will be back in
Speaker 2: a second.
Speaker 3: Okay, we are back.
Speaker 2: Something I wanted to talk about with the way that
Speaker 2: these sensors are used, so about actually, okay, sorry, I
Speaker 2: should have actually figured out the exact date after which
Speaker 2: the story originally came out, but maybe like four or
Speaker 2: five days after your story came out. There was a
Speaker 2: story that came out of Chicago about a sort of
Speaker 2: effectively the cover up of a case where CPD was
Speaker 2: responding to a shot spotter ping and it was just
Speaker 2: like a thirteen year old kid shooting off fireworks and
Speaker 2: the cop showed up and immediately started shooting, and like,
Speaker 2: thankfully cops can't set of a barn, so the kid
Speaker 2: didn't get shot, but like, this child had a cop
Speaker 2: shoot at him while the kid was running towards the
Speaker 2: cop going.
Speaker 3: No, it was fireworks. Yeah.
Speaker 2: So I was wondering what kind, like, you know, how
Speaker 2: many of those kinds of stories did you run into
Speaker 2: when you were sort of like doing this, running into
Speaker 2: the story, and yeah, what is the impact of that
Speaker 2: stuff sort of been?
Speaker 3: You know, I.
Speaker 1: Think that that example that you bring up is particularly egregious.
Speaker 1: But what happens more often, I think, are these there's
Speaker 1: sort of like less dramatic events where you know, sound
Speaker 1: thinking or ShotSpotter will detect two shots and deploy cops
Speaker 1: to a corner, and you know, they'll detain someone on
Speaker 1: the scene, run their name through their you know, their
Speaker 1: databases and find that this guy's got a bench warrant,
Speaker 1: or you know, pick someone up on a misdemeanor. Right. So,
Speaker 1: like I think, you know, while there are some really
Speaker 1: egregious examples, the thing that that I think about a
Speaker 1: lot here is that is just how much unnecessary, how
Speaker 1: many unnecessary arrests are happening because of ShotSpotter, right, how
Speaker 1: many people are being picked up on bullshit essentially?
Speaker 4: Yeah, and you know that that recent case in Chicago
Speaker 4: with the kid with the firework, and you know, it
Speaker 4: wasn't too long ago that you know, Adam Toledo was shot. Yeah,
Speaker 4: you know, a thirteen year old kid for the same reason.
Speaker 4: Cops were responding to a shot spotter alert. And the
Speaker 4: Chicago o i G and their report about it kind
Speaker 4: of highlighted one of the things, which is cops are
Speaker 4: just primed to be you know, expecting you know, gunfire
Speaker 4: somebody shooting at them and everything, and you know, I
Speaker 4: think that's that is a danger. You know, but again
Speaker 4: to what Dreve's saying is like it also leads to
Speaker 4: a lot of unnecessary stops. It opens up people to
Speaker 4: be profiled and padded down, and you know, so both
Speaker 4: options are not great, you know, when you consider the
Speaker 4: harm that this causes. It's just we all know that, Like,
Speaker 4: cops are very jumpy to begin with, So you know,
Speaker 4: they hear a firework or you know, an acorn hits
Speaker 4: their cop car or something. We all know that, Like,
Speaker 4: that's probably not what we need police to be expecting
Speaker 4: on a call. And so you're just telling people, oh,
Speaker 4: gunshots and then they're going to run in expecting to
Speaker 4: be fired upon. And I don't think that's great for society.
Speaker 3: Garrison, you were talking a bit about that, So.
Speaker 5: I don't know if there's much more to say.
Speaker 3: I think that's true. We do a lot of acord cop.
Speaker 5: I think the A Corn incident stands on itself. I
Speaker 5: don't think it needs to even be talked about. I
Speaker 5: think it I think one sentences uh speaks speaks a
Speaker 5: whole a whole book's worth of possible analysis of police
Speaker 5: behavior and no being the notion of police rushing into
Speaker 5: every situation thinking that there's there was there was a
Speaker 5: gunshot obviously has its inherent problems.
Speaker 4: Now, like mind you, a lot of the times, you know,
Speaker 4: and at least in in other cities, it's sixty to
Speaker 4: eighty percent of the time they don't find anything, you know,
Speaker 4: which I think is good in the sense that nobody's
Speaker 4: being harmed or stopped, but it's also bad when you
Speaker 4: consider the effectiveness and utility of the device, you know,
Speaker 4: which Shaw Spotter has kind of distanced themselves from, you know,
Speaker 4: this idea of preventing gun crime or lowering croun crime
Speaker 4: and more in terms of like safety and arriving to
Speaker 4: a scene quicker to render aid and help police find
Speaker 4: shell case scenes. You know, you've seen over the years
Speaker 4: the kind of switch of focus on what the technology does,
Speaker 4: and that most certainly happened around the time they changed
Speaker 4: their name to Sound thinking, yeah, I.
Speaker 1: Think you know. The other thing I wanted to mention
Speaker 1: here is that you know, from a different leaked internal
Speaker 1: report from the State's Attorney's Office in Illinois, in Cook County, Illinois,
Speaker 1: it found that like a third of arrests stemming from
Speaker 1: a shot spotter alert actually had nothing to do with
Speaker 1: the gun in the first place. So it's not even like,
Speaker 1: you know, there eighty nine percent of alerts, don't you know,
Speaker 1: result in finding a shell casing. It's that even when
Speaker 1: there are arrests that occur from a ShotSpotter alert, thirty
Speaker 1: percent of them have nothing to do with a gun, right,
Speaker 1: And that just shows you sort of the criminalization of
Speaker 1: what happened are of people in areas that have these microphones.
Speaker 2: Yeah, and I think I think the combination of those
Speaker 2: two things gets you to this point about Shotspotter's effectiveness,
Speaker 2: which is that like, okay, so we've had ShotSpotter for.
Speaker 3: A while in Chicago, right, Chicago.
Speaker 2: Police do not solve murders like it's sub it's their
Speaker 2: murder clearance rate. And you have to keep in mind
Speaker 2: that murder clearance doesn't actually mean they solved a murder.
Speaker 2: But like, even that jacked up murder clearance rate I
Speaker 2: don't think has had like I think they may have
Speaker 2: had one year in my entire life, over fifty percent,
Speaker 2: and that was because murder clearance. If two people both
Speaker 2: shoot each other and they both die, that counts as
Speaker 2: a clearance or they find us their suspect dies in
Speaker 2: like another way. So it's it's pretty clear that it's
Speaker 2: not actually substantively contributing to Chicago Police Department solving murders.
Speaker 2: Like you have a better than coin flip odds if
Speaker 2: you kill someone in Chicago that like the police aren't
Speaker 2: even gonna like really try to figure out what happened.
Speaker 2: And so yeah, I think I think it makes a
Speaker 2: lot of sense that they've been pivoting away from even
Speaker 2: like even claiming that this can do anything to solve
Speaker 2: gun violence, because it just clearly hasn't at all, and
Speaker 2: it's instead it seems to be doing a bunch of
Speaker 2: other stuff, which is like either throwing cops around doing
Speaker 2: is like throwing cops around chasing like shadows, which either
Speaker 2: results in them arresting just random people or like having
Speaker 2: these really sort of terrifying incidents, or it just results
Speaker 2: in straight up nothing.
Speaker 3: Yeah, we're gonna go to ads. We'll be back in
Speaker 3: however long capitalism dictates, So see you then we're back.
Speaker 5: And I think, like looking at the effectiveness, like two
Speaker 5: cities that have continued to deny shot spot or contracts
Speaker 5: are Atlanta and Portland, two cities that spend a lot
Speaker 5: of time thinking about how they equip their police, spend
Speaker 5: a lot of time like making sure that their police
Speaker 5: are able to serve the largest amount of the community possible.
Speaker 5: And the fact that, like specifically Atlanta, with their massive
Speaker 5: like flock program of and integrated camera network across the
Speaker 5: whole city, like it is one of the most surveilled
Speaker 5: cities in the country, if not most. The fact that
Speaker 5: they are turning down this equipment for not being effective
Speaker 5: enough and it being too costly is a sign for like,
Speaker 5: beyond it just being a sign, it's also like a
Speaker 5: look at what why other police departments are interested in
Speaker 5: this and like what it allows them to do and
Speaker 5: being deployed to these various communities that have the what
Speaker 5: like twenty five thousand sensors. But no, I mean like
Speaker 5: they've constantly tried to send this stuff to Atlanta and
Speaker 5: it's like and it's just it's simply not happening. And
Speaker 5: even after twenty twenty that Portland's like, no, it's it's
Speaker 5: it's it's two super super useful examples to measure how
Speaker 5: much this technology actually is going to get used for
Speaker 5: what they say it's being used for versus just having
Speaker 5: an excuse to act like there's gunfire all across the city.
Speaker 4: Yeah, and you know, I think when we start more
Speaker 4: police departments are going to start relying more on technology, sure,
Speaker 4: largely because many departments cannot hire more cops. Now this
Speaker 4: isn't advocating me. You know, I don't want police departments
Speaker 4: to hire more cops. You know, they've slowly defunded themselves
Speaker 4: in that way. But like you know, cities like Los
Speaker 4: Angeles are trying to grow their surveillance capabilities for that reason.
Speaker 3: They just do not have enough. They say, they do
Speaker 3: not have enough cops.
Speaker 4: And so this is where kind of this surveillance capitalism
Speaker 4: is going to really thrive, is police departments are going
Speaker 4: to get desperate and they're going to start reaching out
Speaker 4: and getting more invasive surveillance technology.
Speaker 3: And you know, I.
Speaker 4: Think in some cities, shot spot is kind of they're
Speaker 4: a way of quieting the narratives about you know, the
Speaker 4: growing gun violence and everything in their communities. You know,
Speaker 4: they're like, oh, look, we've deployed this new toy to
Speaker 4: kind of help us without really solving anything. Because we
Speaker 4: all know cops aren't really good at solving crime. Yeah,
Speaker 4: so it kind of gives them cover of like we're
Speaker 4: bad at our jobs. So how do we make it
Speaker 4: look like we're better. Well, let's you know, invest in
Speaker 4: some new technology, so it looks like we're trying something.
Speaker 4: But you know, at the end of the day, it's
Speaker 4: a waste of money, and then the impacts of that
Speaker 4: is harm, you know, greater than the good.
Speaker 2: Yeah, it's like we're spending an enormous amount of money
Speaker 2: to hurt people for no reason.
Speaker 3: Yeah.
Speaker 1: And you know, I think shot Spatter is only one
Speaker 1: of Sound Thinking's offerings, right, Like they you know, when
Speaker 1: they change their name to sound Thinking, it's sort of
Speaker 1: like reflected this pivot in the company where now they
Speaker 1: were going to start thinking more about like resource management. Right,
Speaker 1: how do we how do we convince departments that our
Speaker 1: technology is going to better help them allocate their resources?
Speaker 1: And you know, surveillance is the way to do that.
Speaker 1: We can measure where crime is, we can measure where
Speaker 1: gunshots is and where gunshots are, and we can.
Speaker 3: Deploy police there.
Speaker 1: And one of shot spot or recently Sound Thinking had
Speaker 1: acquired like a notorious predictive policing company called Predpole. That
Speaker 1: happens I think earlier this year, so you know, they're
Speaker 1: trying to expand their offerings here to be this kind
Speaker 1: of resource management solution for departments semia.
Speaker 5: Did you have anything else you wanted to bring up here?
Speaker 2: Yeah, I guess, I guess there's one more thing I
Speaker 2: wanted to talk about what you said. So one of
Speaker 2: the things that I've heard from places that have gotten
Speaker 2: rid of their contracts is that shot spotters not like
Speaker 2: taking their censors down even when cities stopped doing contracts.
Speaker 2: I was wondering what you two sort of know about that.
Speaker 5: Yeah.
Speaker 4: I reached out to Dayton, Ohio, who recently got rid
Speaker 4: of their contracts, and I reached out, you know, because
Speaker 4: I was like asking departments who had it, like are
Speaker 4: you aware of the status of the censors or do
Speaker 4: you know the locations?
Speaker 3: You know?
Speaker 4: Both knows. And then I asked Dayton, you know, well,
Speaker 4: now that the contract's over, what happens to the sensors?
Speaker 4: And they basically said, we don't know. That's Shotswater's responsibility,
Speaker 4: and their responsibility is maintenance and care and removal and installation.
Speaker 3: So who knows.
Speaker 4: Obviously somebody knows because It's not like some person can
Speaker 4: just start climbing telephone poles and installing surveillance equipment. So obviously,
Speaker 4: you know, somebody is issuing permits to install stuff and
Speaker 4: put stuff up there. But like you know, as we're
Speaker 4: finding out, city council members don't know, police departments don't know,
Speaker 4: and so who knows what happens to these devices afterwards?
Speaker 4: And then say a city like Chicago, you know, say
Speaker 4: they cancel their contracts, Well, a new mayor can come
Speaker 4: in and then just instantly turn them back on, you know,
Speaker 4: and that way, And so that's kind of the other
Speaker 4: thing we're solely starting to learn here. It's more cities
Speaker 4: start canceling their contracts or not renewing them. You know,
Speaker 4: it is what happens to the technology afterwards, and we
Speaker 4: don't know.
Speaker 3: Which is not a great sign.
Speaker 2: Like, I mean, you know, it's not good that there's
Speaker 2: just a bunch of state surveillance technology around all the time,
Speaker 2: but it somehow feels even worse that we don't have
Speaker 2: any idea what happens to even if the state decides
Speaker 2: it doesn't want to use it. So yeah, I guess
Speaker 2: on that somewhat disquieting note.
Speaker 3: Do you have anything else you wanted to make sure
Speaker 3: you get to.
Speaker 1: Oh nothing for me.
Speaker 6: No.
Speaker 4: I mean this is you know, thanks to you know,
Speaker 4: somebody brave enough to send us the info and it's
Speaker 4: the only way this information has been able to get out.
Speaker 4: And I think if I implore the public to really
Speaker 4: research and dig into this technology, if their cities are
Speaker 4: thinking about extending their contracts or bringing a contract in
Speaker 4: and really questioning and trying to get shots by are
Speaker 4: on the record to answer for some of these things.
Speaker 4: And you know, we know what works and what doesn't work.
Speaker 4: And I think most cities are starting to find out
Speaker 4: that there is a better use of that amount of
Speaker 4: money to stop these sort of gun crimes, interventions and
Speaker 4: other more community based solutions rather than just dumping money
Speaker 4: into surveillance technology. And you know, you can get a
Speaker 4: lot done with an eight million dollars. Yeah, you know,
Speaker 4: it's just it's just like there's always money in the
Speaker 4: Banana Stan sort of thing, and there's like there's always
Speaker 4: money for police. So it's just like, why don't we
Speaker 4: just retransform that money into things that actually work in
Speaker 4: these communities.
Speaker 3: And you know, go behind that.
Speaker 2: So yeah, and I think I don't know. Hopefully, hopefully
Speaker 2: this will encourage more cities to stop paying for this shit. Yeah,
Speaker 2: So where can people find you choose work? I mean,
Speaker 2: I know, like obviously this one's on Wired, but I
Speaker 2: are online places, et cetera, et cetera, social media places,
Speaker 2: plug yourself, go yayh.
Speaker 1: Well, I'm I'm. You can find my stuff on Wired
Speaker 1: dot com and I'm on X or Twitter or whatever
Speaker 1: you want to call it at d Marrow and on
Speaker 1: Blue Sky at d Marrow d M e h r. Oh.
Speaker 4: And you can find me on Instagram and Twitter with
Speaker 4: the user name joey never and Joe And then my
Speaker 4: writings have been in a local press out here in
Speaker 4: La La Public Press and Knock La.
Speaker 2: Yeah, and thank you to both so much for coming on,
Speaker 2: Thanks for having.
Speaker 4: Us, Thank you so much, appreciate it.
Speaker 2: Yeah, and I'm gonna encourage everyone else to go get
Speaker 2: your city to not use this stuff because it sucks.
Speaker 2: All right, this has been this has been naked appened here.
Speaker 2: You can find us in the usual places.
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