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Speaker 1: From UFOs to psychic powers and government conspiracies. History is

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Speaker 1: riddled with unexplained events. You can turn back now or

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Speaker 1: learn the stuff they don't want you to know. A

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Speaker 1: production of I Heart Grady. Hello, welcome back to the show.

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Speaker 1: My name is Matt Our colleague Nol is not here today,

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Speaker 1: but we'll return tomorrow very soon. They called me Ben.

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Speaker 1: We're joined as always with our super producer Paul Mission

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Speaker 1: controlled deconds. Most importantly, you are you. You are here.

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Speaker 1: That makes this the stuff they don't want you to know.

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Speaker 1: Before we get started, shot Spotter did reach out to

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Speaker 1: us regarding some of the statistics provided in our episodes

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Speaker 1: and their position with respect to the Williams case. Shot

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Speaker 1: spot a disclaims all responsibility and Mr Williams incarceration and

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Speaker 1: disagrees the associated press is factual assertions regarding changing locations.

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Speaker 1: If you're interested in reviewing additional information from shot Spotter

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Speaker 1: on these topics, please visit www dot shot spotter dot com.

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Speaker 1: Heads up, fellow conspiracy realist. This is part two of

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Speaker 1: a two part series. We are continuing our discussion of

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Speaker 1: the famous or infamous, certainly controversial technology known as shot

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Speaker 1: spot Er. Previously, we discussed the terrifying reality of gun

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Speaker 1: deaths in these United States. We also looked at the

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Speaker 1: ways in which law enforcement, local and federal level political bodies,

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Speaker 1: and average average citizens right just the folks you would

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Speaker 1: see on next door trying to figure out a solution. Um.

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Speaker 1: And it's funny, Matt, because in the interim between you

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Speaker 1: Noel and I recording Part one of shot Spotter and

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Speaker 1: Part two of shot Spotter, I I went out, I

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Speaker 1: hit the streets and I looked for some shot Spotter arrays,

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Speaker 1: and I'm pretty sure I found some. Maybe they're not

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Speaker 1: brand name, but I'm pretty sure. I like knowing now

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Speaker 1: what we know about sort of the combo meal of

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Speaker 1: how these things are set up. Just for a quick

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Speaker 1: refresher for everybody. Shot Spotter is using some pretty impressive

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Speaker 1: algorithmic software and some pretty cool microphones. In part one,

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Speaker 1: we were not able to discern the specific brand name

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Speaker 1: of these microphones, but we do know the following there's

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Speaker 1: something like fifteen to twenty per square mile, and these

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Speaker 1: microphone arrangements also include the following a GPS and onboard

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Speaker 1: computer transmitter and some never we have some of the

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Speaker 1: algorithmic stuff on site, so it can quickly do a calculation.

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Speaker 1: Is that a dump truck? Is that someone with a

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Speaker 1: legendary fart. I think we nailed down the science, right.

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Speaker 1: No one can fart such that shot Spotter will mistake

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Speaker 1: it for a gunshot. Right to our knowledge, that's not possible.

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Speaker 1: It hasn't happened yet. That isn't to say, you know,

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Speaker 1: some fast food company will invent something to allow that

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Speaker 1: to to occur. Just on the microphones, I'm going to say,

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Speaker 1: their quote audio detection devices unquote are likely proprietary. So

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Speaker 1: they probably are labeled shot Spotter right as like a

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Speaker 1: brand name or something like that, And we just don't

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Speaker 1: know what they are be because it is it's technology

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Speaker 1: that they've invented or at least patented, right. So the

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Speaker 1: big thing to be aware of its expensive for a

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Speaker 1: police department to run this thing in a city, and

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Speaker 1: it costs taxpayers a ton of money. And what we're

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Speaker 1: gonna talk about today is that the system itself. While

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Speaker 1: the company you know says, hey, this thing works great,

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Speaker 1: while there are a lot of people on the internet

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Speaker 1: you can find that say hey, this thing is amazing

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Speaker 1: and it works great. A lot of listeners to stuff

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Speaker 1: they don't want you to know are saying the same thing, yes,

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Speaker 1: or at least they're aware of how function it seems

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Speaker 1: to function well. But there are a lot of detractors,

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Speaker 1: people who say, there's a whole other side to this coin,

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Speaker 1: and we're going to explore that today. Yeah, and when

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Speaker 1: we do a two parter. We don't do these super often,

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Speaker 1: but when we do a two parter, it's usually because

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Speaker 1: we have, um, we've got a little bit deeper in

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Speaker 1: the rabbit hole, and we didn't have time to make

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Speaker 1: a brief. You know, That's one of the infamous jokes

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Speaker 1: about writing Matt, which I'm sure you've heard, is I

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Speaker 1: can't remember who it was. Fellow conspiracy realists help us

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Speaker 1: out here. But there are multiple instances of these writers

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Speaker 1: corresponding with each other and sending their version of an

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Speaker 1: essay length text and ending it with sorry in a hurry,

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Speaker 1: didn't have time to make this brief, so we didn't

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Speaker 1: We didn't have the time to make this brief. And

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Speaker 1: you know, when Matt, Noel and I are hanging out

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Speaker 1: on air or off, we often run into these sorts

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Speaker 1: of these sorts of moments where we say, how can

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Speaker 1: we accurately concentrate the facts, the controversy, the conspiracy into

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Speaker 1: something that doesn't become a four are our long weird

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Speaker 1: hangout session. If you want to learn what our endless

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Speaker 1: hangout sessions are like without the you know, without the

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Speaker 1: constraints of podcasting, do check out our book. Stuff. They

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Speaker 1: don't want you to know. We we even had this

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Speaker 1: is how weird we get with it, folks. We even

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Speaker 1: had um our publishers come to us several times and say, hey,

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Speaker 1: this chapter is a little long, but I think we're

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Speaker 1: able to get a lot of stuff in there. What

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Speaker 1: do you think. Yeah, to my knowledge, we were able

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Speaker 1: to fit everything we wanted to in there. We did

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Speaker 1: have to pare it down to government only stuff from

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Speaker 1: all the research we gathered right right the the cryptic

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Speaker 1: chapter cryptozoology chapter is one of my favorites, and maybe

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Speaker 1: oh yeah, yeah, oh gosh, we went deep on that too. Basically,

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Speaker 1: we have another book, is what we're saying. We kind

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Speaker 1: of have another book that will have to will have

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Speaker 1: to be on the way too soon, and the best

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Speaker 1: way to help us make that happen is to check

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Speaker 1: out this first book. We hope that you enjoyed it's

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Speaker 1: available wherever you find your favorite books, whether that's your

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Speaker 1: local bookstore, whether that's Amazon. Well, yeah, and and let

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Speaker 1: let daddy bezos know how you feel about the book

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Speaker 1: by going on Amazon and reviewing it. Seriously, if you've

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Speaker 1: got any kind of Amazon account and you do like

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Speaker 1: the book, please review it. That that helps a ton,

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Speaker 1: it does it? Weirdly helps a ton? Uh? And yeah,

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Speaker 1: we want to be we want to be your five

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Speaker 1: star crew, you know what I mean. And because we're asking,

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Speaker 1: we're asking you, our fellow listeners uh for for a

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Speaker 1: little help at the top in terms of ratings, especially,

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Speaker 1: we want to give you something in return. Right, So

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Speaker 1: we spend a lot of time and shot Spotter, here

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Speaker 1: are the facts. Back to the backstory. Okay, Matt, you

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Speaker 1: and I haven't spoken for a weird amount of time

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Speaker 1: for us, because we've both been doing some crazy things.

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Speaker 1: Matt is executive producer on one of my favorite seasonal shows,

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Speaker 1: Thirteen Days of Halloween. You and are pal Alex Williams,

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Speaker 1: and I guess our pal Clancy Brown. We can say

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Speaker 1: it now right, Uh? Yes that guy. Uh. I've been

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Speaker 1: working on this wonderful horror show and it's coming out

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Speaker 1: now as you hear this episode, you should be able

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Speaker 1: to hear all thirteen episodes of Thirteen Days of Halloween.

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Speaker 1: I'm super biased. I always get a chance to to

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Speaker 1: write a couple of episodes and it's um it makes

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Speaker 1: my year, honestly. But in that interim, I was also

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Speaker 1: working on a secret project, which may hear Matt and

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Speaker 1: Noel allude to cryptically in our listener mail and strange

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Speaker 1: news segments. I hate to miss stuff, but it's it's

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Speaker 1: been that kind of thing, you know, been in the

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Speaker 1: absence of transparency. M hm, speculation. That sounds good. Who

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Speaker 1: wrote that? So the the thing I found about shot Spotter,

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Speaker 1: It's fascinating. It was I I found, but had not

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Speaker 1: have not confirmed, some sort of audio tracking relays here

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Speaker 1: in the neighborhood of our old office. Additionally, you know,

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Speaker 1: as we get, as we get closer and closer to Halloween,

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Speaker 1: I heard more and more gun shots, and I heard

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Speaker 1: two very close gun shots, and I was thinking back

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Speaker 1: to um an earlier conversation we had. I can't remember

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Speaker 1: whether it was on air or off where we were.

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Speaker 1: I think it's when we were reading letters from fellow

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Speaker 1: listeners in law enforcement and we started asking each other

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Speaker 1: which of us had ever called the police to report

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Speaker 1: a gunshot. I don't remember what you said, did you know?

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Speaker 1: For me, it was pointing out that where I lived previously,

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Speaker 1: I would hear gunshots pretty consistently. Um. I would also

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Speaker 1: hear fireworks, and it was difficult at times to tell

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Speaker 1: what was what. Depending on the distance right you could

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Speaker 1: you could kind of make it out. If I was, like,

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Speaker 1: if you're standing outside right at the time when one

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Speaker 1: of these shots or fireworks occurred, you could usually discern

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Speaker 1: and you could kind of tell even the caliber, often

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Speaker 1: depending on several factors. But what it sounds like, you

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Speaker 1: just kind of get used to the sound of certain weapons. Um.

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Speaker 1: But if you're inside your house, especially like if it's

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Speaker 1: late at night, many people, including myself, wouldn't be able

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Speaker 1: to discern and then likely wouldn't call the police unless

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Speaker 1: you know, you're a particularly I'm not crabby person, but

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Speaker 1: somebody who's really concerned and is often in contact with

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Speaker 1: the police, right right, right, right, Um, what may be

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Speaker 1: called a busy body sometimes. But also we have to

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Speaker 1: we have to remember that, you know, there's a reason

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Speaker 1: good Samaritan laws and so on exists. Many people are

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Speaker 1: rightly concerned about engaging involving themselves in this situation, and

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Speaker 1: so often people won't call contact first responders or emergency

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Speaker 1: services unless they feel those gunshots are aimed at them.

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Speaker 1: Even then, you know, if you think about it rationally,

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Speaker 1: if you call your local police department, depending on how

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Speaker 1: close you are to that department, by the time a

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Speaker 1: shot has been fired, you've heard it, you've called the police,

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Speaker 1: they've dispatched an officer or two, and then the officer

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Speaker 1: makes it out there, it's very unlikely the devil police

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Speaker 1: officer will be able to do anything to prevent further

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Speaker 1: crime or stop whatever is happening. Uh. It is often

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Speaker 1: if there was an actual shooting, that officer is going

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Speaker 1: to be a first responder to a gunshot victim rather

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Speaker 1: than you know, stopping a crime. The response time is huge.

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Speaker 1: Also side note, if you are ever in a situation

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Speaker 1: where you feel someone is outside your house or threatening

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Speaker 1: you with entering your house or something, or you know,

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Speaker 1: your apartment, wherever you live, your domicile, always remember a

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Speaker 1: lot of people mess this part up. Don't say I'm

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Speaker 1: going to call the police or I'm I'm going to

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Speaker 1: call nine one one say I have called, they are

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Speaker 1: on their way. You know what I mean, Because what

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Speaker 1: you're doing if you threaten to call instead of just

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Speaker 1: making it is you're essentially telling people there is a

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Speaker 1: time interval to to your point, Matt, there's a time

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Speaker 1: interval before these folks arrive, right, And Look, this can

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Speaker 1: be nasty stuff, but the idea of shot Spotter and

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Speaker 1: similar technologies is to fight against this, to mitigate that

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Speaker 1: interval of time between a gunshot being fired and someone

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Speaker 1: getting to the scene. When it works, as we explain

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Speaker 1: in Part one, it can definitely absolutely save lives, save

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Speaker 1: lives of law enforcement individuals, first responders e m t

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Speaker 1: s can know what they're getting into. Uh, it can

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Speaker 1: save the lives of civilians. It can also help cut

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Speaker 1: short spree shooters, you know, mass shooters who might have

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Speaker 1: might be proceeding on foot through an brewood right or

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Speaker 1: through some certain region or community. So this can make

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Speaker 1: a difference when it works. Yeah, and according to shot Spotter,

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Speaker 1: it also increases the likelihood that ballistics evidence will be recovered,

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Speaker 1: which would help close a case if there was an

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Speaker 1: actual crime that occurred that led to a trial or something. Yes, perfect,

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Speaker 1: that's perfect. Foreshadowing here because you see part two of

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Speaker 1: this of this series could probably be called shot Spotter

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Speaker 1: goes to Court. So what happens what happens when this

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Speaker 1: private company it starts helping save or put people away.

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Speaker 1: We're gonna pause for word from our sponsor and we'll

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Speaker 1: be back. Here's where it gets crazy, Matt. You set

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Speaker 1: this up so very well. That's one that's we're back

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Speaker 1: in the land of controversies about this technology. And again,

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Speaker 1: to be clear, we are exploring this stuff that critics

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Speaker 1: and proponents have said about shot Spotter. Additionally, shot Spotter

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Speaker 1: is not the only audio tracing software or system out there.

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Speaker 1: It's like how Coca Cola is not the only soda

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Speaker 1: you can buy at the grocery store. It's just the

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Speaker 1: one you might be most aware of. So shot Spotter

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Speaker 1: probably gets a lot more public attention because it is

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Speaker 1: more pervasive than a lot of these audio tracking other

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Speaker 1: audio tracking private entities. What you need to know is

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Speaker 1: there is more than one. However, shot Spotter is the

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Speaker 1: and that comes up the most because they right now

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Speaker 1: have the most data out of again the private companies.

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Speaker 1: And we're going to get into some sticky stuff about

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Speaker 1: the future man. I know you saw the stat Matt

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Speaker 1: shot spotter itself. How accurate is it? Right? Like, how

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Speaker 1: many times has someone responded on the ground to a

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Speaker 1: shot spotter report and found it was just a dump truck,

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Speaker 1: a superhuman farter road construction, you know, stuff like that.

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Speaker 1: How accurate is it? I saw the statistic and the

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Speaker 1: statistic feels bold. Yeah, it is a bold statistic. Shots

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Speaker 1: Water claims that their system has a nineties seven percent

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Speaker 1: accuracy rate when it comes to identifying gunshots versus other

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Speaker 1: loud sounds in a city where it's placed seven percent.

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Speaker 1: It's very important to remember that is accuracy into scarning

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Speaker 1: whether or not the sound emanated that it's tracking was

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Speaker 1: a shot right, right? And and with that statistic, we

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Speaker 1: also have to acknowledge that statistics can be so slippery.

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Speaker 1: What they're saying is cent of the time they are

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Speaker 1: right when they say something is or is not a

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Speaker 1: gun shot. That's not the same thing as saying cent

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Speaker 1: of the time they're right about the specifics of the firearm,

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Speaker 1: not at all. It's just it's like up there with um.

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Speaker 1: You know, before Google went full alphabet, they had that

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Speaker 1: motto don't be evil, and we used to We used

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Speaker 1: to joke back and forth about how there's a world

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Speaker 1: of difference between don't be evil and be good. You know. So,

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Speaker 1: so they're not saying of the time they can tell you,

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Speaker 1: you know, this was an a k versus something else. Right.

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Speaker 1: What they are saying is that they know the difference

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Speaker 1: between the stuff that is just you know, the burps

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Speaker 1: of farts of your average city versus actual discharge of

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Speaker 1: a firearm. The thing is a lot of folks dispute this.

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Speaker 1: One of the biggest customers for shot Spotter, and specific

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Speaker 1: in the United States is the beautiful city of Chicago. Chicago,

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Speaker 1: we miss you. We're gonna hit the road soon and

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Speaker 1: say hello in person. But the thing about Chicago for

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Speaker 1: our purposes is that as it is such a huge

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Speaker 1: customer of shot Spotter, it also generates a lot of

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Speaker 1: data to pour into to look at this claim of accuracy.

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Speaker 1: The conspiracy here from the critics is the idea that

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Speaker 1: shot Spotter is uh being purposely misleading about that percent statistic,

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Speaker 1: which again is bold. Another other thing we have to

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Speaker 1: realize about this statistic is you will see different claims.

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Speaker 1: They're They're always going to be you know, a pretty

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Speaker 1: high percentile, but you will see different specific claims about

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Speaker 1: the accuracy. We went to shot Spotter's own website, we

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Speaker 1: looked at their literature and the nine seven percent claim

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Speaker 1: is from twenty nineteen, and they said the system overall

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Speaker 1: had an aggregate accuracy rate across the entirety of their

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Speaker 1: customer base, which you know, you and I work with

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Speaker 1: marketing departments. We love ours. But it does sound a

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Speaker 1: little bit like marketing, doesn't it matter? Yeah, it does

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Speaker 1: sound quite a bit like marketing. It's strange. They're in

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Speaker 1: that same statement, they're saying there's a very small false

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Speaker 1: positive rate of less than half of a percentage of

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Speaker 1: all reported gunfire incidents. That's that's inten okay. Yeah. They

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Speaker 1: also point out in their own literature that the system

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Speaker 1: and the process are evolving constantly. Right, work in progress.

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Speaker 1: We are building the playing that we are flying, which

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Speaker 1: can be taken one of two ways. One it can

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Speaker 1: be seen by the more optimistic as an inspiring thing.

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Speaker 1: You know, one day, right, there will be a hundred

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Speaker 1: percent accuracy. The other, more cynical response would be, hey,

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Speaker 1: why don't you finish building it before you put it out?

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Speaker 1: You know what I mean? And that just I call

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Speaker 1: it the Larry David scale, Like, how Larry David, are

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Speaker 1: you going to be about some stuff? But this is unfortunately,

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Speaker 1: this is not you know, this is not complaining about

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Speaker 1: the big salad or whatever. People's lives are at stake,

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Speaker 1: and the question becomes, is this a tool deployed in

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Speaker 1: good faith to save human lives or is there's something

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Speaker 1: else at play? And that's that's the scary part, and

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Speaker 1: that's why we have to talk about these statistics, which

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Speaker 1: get a little crazy. So Chicago huge customer for shots Spotter.

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Speaker 1: Shot spotter itself says percent accuracy overall as of twenty nineteen.

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Speaker 1: Other cities, other local journalists in the community will will

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Speaker 1: have better stats about how shot spotter functions in their

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Speaker 1: neck of the global woods. Uh now, I think I'm

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Speaker 1: thinking of that San Francisco Examiner article right when they

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Speaker 1: point into it a different um was that different overall

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Speaker 1: accuracy rate for shot Spotter? Uh yeah, I can read

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Speaker 1: you a quote from it. It comes from seventeen and

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Speaker 1: again it's a statement from shot Spotter. They, according to

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Speaker 1: this article, guarantee accuracy of the time and that is

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Speaker 1: speaking again. Well, I don't know, I don't know the

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Speaker 1: context here. I guess that's in detecting an actual gunshot. Yeah,

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Speaker 1: and that comes from the CEO, Ralph Clark. Ralph Clark

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Speaker 1: is named in multiple articles on shot Spotter. Often he's

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Speaker 1: talking about things he feels journalists got wrong, you know

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Speaker 1: what I mean. And and it's another get we made

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Speaker 1: a joke about in part one. But shot Spotter is litigious,

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Speaker 1: so they're I mean, they're not scientology level litigious. But

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Speaker 1: that's why if you look at outfits like Associated Press

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Speaker 1: or Vice News, you will see some specific retractions or

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Speaker 1: clarifications published. Shot Spotter wants people two avoid criticizing it,

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Speaker 1: which is, you know, that's what every business wants, right. However,

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Speaker 1: people are disputing the statistics, and the people who are

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Speaker 1: disputing these claims are not um, you know, your uncle

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Speaker 1: on Facebook or whatever. There are places like the MacArthur

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Speaker 1: Justice Center in Chicago. They studied more than forty thousand

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Speaker 1: shot Spotter dispatches and they found that eighty nine percent

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Speaker 1: of those dispatches resulted in no gun related crime. That's

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Speaker 1: a quote from MacArthur. Uh, and they said eighty six

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Speaker 1: percent resulted in no crime at all. That's interesting. We

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Speaker 1: see another three percent discrepancy, right, So so it's like

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Speaker 1: the did the cops just show up and someone was

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Speaker 1: like throwing rocks and orphans and they were like, that's

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Speaker 1: not a gun shot, that's a crime. Well, again, that's

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Speaker 1: what we're talking about. It doesn't matter how fast and

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Speaker 1: accurate shot spotter is in detecting a gun shot. By

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Speaker 1: the time an officer is able to arrive on scene,

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Speaker 1: there may be no evidence of a gun being fired. Theoretically,

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Speaker 1: there would be shell casings on the ground if someone

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Speaker 1: just was firing a weapon and then you know, got

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Speaker 1: out of there quickly, as is often the case, and

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Speaker 1: they would probably not take the time to pick up

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Speaker 1: any shell casings. But it depends on where the thing

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Speaker 1: where the shots occur. It depends on what the you know,

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Speaker 1: ground looks like. There. Is it gravel, is it dirt,

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Speaker 1: is it asphalt? Is it, uh, you know, tall grass,

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Speaker 1: And there's so many factors. It's tough for me thinking

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Speaker 1: about that resulted in no gun really crime, no crime

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Speaker 1: at all when it comes to because that really doesn't

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Speaker 1: mean that shot spotter is failing. That means that when

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Speaker 1: officers arrive on scene, there's no evidence of a crime.

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Speaker 1: There's nobody to apprehend that, there's nobody to even probably

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Speaker 1: talk to unless there is somebody there, right, and then

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Speaker 1: they become highly suspicious, no matter what they had to

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Speaker 1: do or didn't have to do with firing a weapon.

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Speaker 1: You know, we're talking about someone who is maybe just

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Speaker 1: hanging out on the street, maybe someone who's unhoused, right,

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Speaker 1: maybe someone who lives there and is on their porch,

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Speaker 1: you know what I mean. And that's not that's not

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Speaker 1: bad police work either. That's hey, let me get an

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Speaker 1: eye witness, you were here, tell me what's going on.

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Speaker 1: Or it could be bad police work too. It could

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Speaker 1: be because that person, that person could automatically be seen

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Speaker 1: as a suspect as or somebody potentially had something to

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Speaker 1: do with the gunfire. So it's just it's especially if

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Speaker 1: that person has priors, right, if they have a jacket already,

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Speaker 1: you know what I mean. This is amusing jack as

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Speaker 1: the slang term for your record in a folder in

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Speaker 1: an incarceration system. But if they already have some priors,

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Speaker 1: then of course suspicion automatically falls on them. You could

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Speaker 1: be a good person in the wrong place at the

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Speaker 1: wrong time, and all of a sudden you're jammed up,

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Speaker 1: But consider the other side. We're still in Chicago with

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Speaker 1: this um. The Chicago Inspector General kind of backed up

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Speaker 1: these findings from the MacArthur Justice Center and they found

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Speaker 1: that this was expensive for police departments. Just like in

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Speaker 1: part one, we talked about this really fascinating deep dive

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Speaker 1: into St. Louis, Missouri, uh in Police Chief magazine, which

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Speaker 1: I still didn't still surprised that's a magazine, but but

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Speaker 1: there's this deep dive into the hidden cost of systems

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Speaker 1: like this. Four police departments and therefore four taxpayers in

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Speaker 1: those cities, in those towns, in those communities. Anyway, Chicago

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Speaker 1: Inspector General backs up MacArthur Justice which also backs up

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Speaker 1: some of the findings in St. Louis, and they say,

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Speaker 1: these police officers are changing their practice. They're stopping and

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Speaker 1: searching people, right, stopping frisk basically for no other reason

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Speaker 1: than they were in that place where shot spotter frequently

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Speaker 1: gives alerts in the vicinity. Yeah, so not having to

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Speaker 1: do with with responding to a shot spotter hit. Yeah,

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Speaker 1: not a specific pop just hey right, that sounds a

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Speaker 1: little bit like an attempt at predicting crime. Just a

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Speaker 1: little bit. Sounds like trying to get in front of something,

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Speaker 1: but it's also missing the big step one. Why are

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Speaker 1: there many shots spotter alerts? Because you have shot spot

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Speaker 1: ter there, you wouldn't have those alerts because there would

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Speaker 1: still be the old dispatch who calls the police kind

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Speaker 1: of system. But also some l e O s are

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Speaker 1: not super into this law enforcement officers because they're thinking

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Speaker 1: of the time it takes. Right, if I normalize responding

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Speaker 1: to this, I get the call, I get the buzz,

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Speaker 1: it sounds like it's legit. I'm on the way, it's over.

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Speaker 1: There's no victim, you know what I mean, there's no

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Speaker 1: case sing, there's nobody on the street to even you know,

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Speaker 1: detained for a while. Well, then how many other calls

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Speaker 1: have I missed while doing that right in a resource

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Speaker 1: strapped environment? Well, and if you also, I think it

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Speaker 1: would be hard for a police department to justify not responding.

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Speaker 1: If you're shot spot er software goes off right or

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Speaker 1: the hardware detect a gun shot, how could you justify

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Speaker 1: not investigating that or sending out a car to at

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00:29:08,240 --> 00:29:11,720
Speaker 1: least check it out? Uh? Yeah, which is puts a

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Speaker 1: real drain. Ye shot spotter told us there was automatic

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Speaker 1: gunfire at a place that has a lot of reports

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Speaker 1: of gun crime, and we just chose to ignore it.

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Speaker 1: That doesn't look good. And there are all of these

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Speaker 1: factors that we're considering in this episode, and it is

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Speaker 1: obviously extremely complicated. This is not a clear cut issue, right. Uh.

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Speaker 1: One of the ways that we could better evaluate this

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Speaker 1: is if we had more hard information, information that wasn't

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Speaker 1: let's say, put together by a sales team to go

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Speaker 1: out and sell more shot Spotters in more cities. If

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Speaker 1: we if we could actually see how does this system

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Speaker 1: actually function in the real world. Um, but we're not

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Speaker 1: really getting that. We are getting specifics coming out of

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Speaker 1: cases that have arisen because of a Shot Spotter call.

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Speaker 1: Though m HM and I love what you just did.

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Speaker 1: Their men. You subtly slid in a reference to a

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Speaker 1: quote by a Shot spot Ter employee, Paul Green, who said, quote,

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Speaker 1: our guarantee was put together by our sales and marketing department,

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00:30:27,880 --> 00:30:34,160
Speaker 1: not our engineers. That's referring to the accuracy quote right right,

428
00:30:34,400 --> 00:30:39,640
Speaker 1: and then, as we as we know, was apparently seven

429
00:30:39,640 --> 00:30:44,760
Speaker 1: percent according to their marketing folks. So you point out

430
00:30:44,760 --> 00:30:47,960
Speaker 1: a great thing we can look at specific cases. We

431
00:30:48,080 --> 00:30:52,680
Speaker 1: can look at something like the case of Corey Ali Mohammad,

432
00:30:52,960 --> 00:30:59,000
Speaker 1: who was a mass shooter apprehended in April with help

433
00:30:59,640 --> 00:31:06,480
Speaker 1: from shot Spotter. So being able to triangulate where these

434
00:31:07,480 --> 00:31:12,720
Speaker 1: shots were occurring played a massive role in helping law

435
00:31:12,840 --> 00:31:19,240
Speaker 1: enforcement in Fresno catch this guy before he killed more people. Um,

436
00:31:19,960 --> 00:31:22,760
Speaker 1: this was you know, this was a hate crime, racially

437
00:31:22,840 --> 00:31:28,520
Speaker 1: motivated kind of spree killing, and more people probably would

438
00:31:28,520 --> 00:31:30,440
Speaker 1: have died if the law wasn't able to get to

439
00:31:31,600 --> 00:31:35,680
Speaker 1: this criminal when they did. And that's that's the shot

440
00:31:35,720 --> 00:31:39,200
Speaker 1: Spotter story. But the thing is, that's like one, one

441
00:31:39,360 --> 00:31:43,240
Speaker 1: specific case, right, that's a success story, as weird as

442
00:31:43,280 --> 00:31:48,680
Speaker 1: it sounds, because this guy did get convicted of four murders,

443
00:31:49,560 --> 00:31:52,680
Speaker 1: but it's not five, it's not eight, and you know

444
00:31:52,720 --> 00:31:56,520
Speaker 1: it's not twenty. And that's what shot Spotter saying. They

445
00:31:56,760 --> 00:32:00,680
Speaker 1: helped create the alped to mitigate the situation. And this

446
00:32:00,760 --> 00:32:04,080
Speaker 1: is the thing. Shot Spotter is supposed to be a

447
00:32:04,200 --> 00:32:09,640
Speaker 1: diagnostic outfit. It's supposed to be an investigative tool for

448
00:32:09,760 --> 00:32:14,720
Speaker 1: the police. An investigative tool is not the same thing

449
00:32:15,040 --> 00:32:18,360
Speaker 1: as primary evidence in a court of law. In the

450
00:32:18,440 --> 00:32:23,200
Speaker 1: United States, evidence is a different thing, and so some

451
00:32:23,280 --> 00:32:25,960
Speaker 1: of the critics are saying this is a bridge too far,

452
00:32:26,680 --> 00:32:30,400
Speaker 1: This is the wrong rubicon to cross for shot spot

453
00:32:30,560 --> 00:32:34,800
Speaker 1: Er data to be shared out as evidence in a

454
00:32:34,920 --> 00:32:40,240
Speaker 1: court of law. And this is not just us on

455
00:32:40,240 --> 00:32:43,520
Speaker 1: on stuff they don't want you to know saying that stuff,

456
00:32:43,720 --> 00:32:46,600
Speaker 1: is saying this can be a sketchy operation. This is

457
00:32:46,640 --> 00:32:49,840
Speaker 1: a particular concern to places like the a c l U.

458
00:32:50,760 --> 00:32:54,800
Speaker 1: They say they had a quote that really stood out

459
00:32:54,840 --> 00:33:00,840
Speaker 1: to me because their accusation is that shot Spotter is

460
00:33:00,920 --> 00:33:07,120
Speaker 1: kind of cherry picking the data, the information that it

461
00:33:07,160 --> 00:33:10,120
Speaker 1: does or does not share, and it's not showing its

462
00:33:10,160 --> 00:33:15,720
Speaker 1: work either, but it's doing it in court, meaning that

463
00:33:16,040 --> 00:33:21,440
Speaker 1: people can go to prison without transparency on this stuff. Yeah,

464
00:33:21,520 --> 00:33:24,400
Speaker 1: at best, I think shot Spotter could and probably should

465
00:33:24,480 --> 00:33:28,640
Speaker 1: be used as supporting evidence. You can if you're going

466
00:33:28,720 --> 00:33:32,720
Speaker 1: through a trial and as a prosecutor you are trying

467
00:33:32,760 --> 00:33:36,800
Speaker 1: to prove that a gun was fired in this vicinity

468
00:33:36,880 --> 00:33:40,720
Speaker 1: at this specific time, you would have time stamps associated

469
00:33:40,760 --> 00:33:44,400
Speaker 1: with that weapon being fired with shot Spotter. But again

470
00:33:44,440 --> 00:33:47,640
Speaker 1: that's not that's not the primary evidence to prove that

471
00:33:47,960 --> 00:33:50,960
Speaker 1: whoever your suspect is, is the person who fired that

472
00:33:51,000 --> 00:33:53,680
Speaker 1: weapon that is just supporting the fact that a weapon

473
00:33:53,720 --> 00:33:57,280
Speaker 1: was fired at that time. Does that make yes? Yeah,

474
00:33:57,760 --> 00:34:00,360
Speaker 1: I was just I don't know if we're on video,

475
00:34:00,440 --> 00:34:03,240
Speaker 1: if this will make it into into our YouTube channel,

476
00:34:03,320 --> 00:34:07,479
Speaker 1: check that out. But I was, I was doing a

477
00:34:07,480 --> 00:34:12,160
Speaker 1: lot of non verbal hyping and agreements. Yes, you have

478
00:34:12,239 --> 00:34:17,239
Speaker 1: described it better than I could have. Supporting evidence. There's

479
00:34:17,239 --> 00:34:22,560
Speaker 1: a line, you know, so a c LU says, in particular,

480
00:34:22,640 --> 00:34:26,279
Speaker 1: they say quote shot spot as methodology is used to

481
00:34:26,320 --> 00:34:31,040
Speaker 1: provide evidence against defendants in criminal cases, but isn't transparent

482
00:34:31,120 --> 00:34:35,400
Speaker 1: and hasn't been peer reviewed or otherwise independently evaluated. That

483
00:34:35,480 --> 00:34:38,720
Speaker 1: simply isn't acceptable for data that is used in court.

484
00:34:39,920 --> 00:34:42,680
Speaker 1: A c l U is still on that mid mid

485
00:34:42,719 --> 00:34:47,319
Speaker 1: Atlantic or transatlantic voice, and then he goes further than this.

486
00:34:48,160 --> 00:34:52,920
Speaker 1: There are allegations of some knoughty things going on at

487
00:34:53,080 --> 00:34:56,520
Speaker 1: shot Spotter and between shot Spotter and police departments and

488
00:34:56,560 --> 00:35:00,880
Speaker 1: other law enforcement agencies, talking about some corruption, some cover ups,

489
00:35:01,360 --> 00:35:04,439
Speaker 1: the things that this show likes to talk about. And

490
00:35:04,520 --> 00:35:07,680
Speaker 1: we're going to address that right when we return from

491
00:35:07,680 --> 00:35:21,600
Speaker 1: a word from our sponsor, and we have returned cover ups, corruption, conspiracy.

492
00:35:23,520 --> 00:35:28,640
Speaker 1: There's a lot here, and just to walk you to

493
00:35:29,120 --> 00:35:32,759
Speaker 1: like mid rabbit hole. Let's call this mid rabbit hole

494
00:35:33,040 --> 00:35:36,200
Speaker 1: because we can't we can't go too far until the

495
00:35:36,239 --> 00:35:40,560
Speaker 1: next book, right unless you know someone's legal department or

496
00:35:40,560 --> 00:35:45,680
Speaker 1: our own legal department comes for us. So being objective here,

497
00:35:45,680 --> 00:35:50,400
Speaker 1: here's what we can say. Mentioned Vice News Associated Press earlier.

498
00:35:50,960 --> 00:35:57,320
Speaker 1: They stated with what appeared to be pretty compelling quantitative

499
00:35:57,360 --> 00:36:03,680
Speaker 1: evidence that shot spotters human analysts were changing their judgments

500
00:36:03,719 --> 00:36:08,200
Speaker 1: on results. So that's the human peace right We know

501
00:36:08,400 --> 00:36:14,720
Speaker 1: that um shot spotter engineers and management themselves that said, yes,

502
00:36:14,880 --> 00:36:19,040
Speaker 1: we have algorithms, we have some very fancy monitors and microphones,

503
00:36:19,520 --> 00:36:22,640
Speaker 1: but we always have a human they're at the past

504
00:36:23,160 --> 00:36:27,520
Speaker 1: right two to help steer. There's the same reason, or

505
00:36:27,640 --> 00:36:32,320
Speaker 1: the same way in which autonomous vehicles in testing phases

506
00:36:33,280 --> 00:36:36,759
Speaker 1: still have someone sitting behind the driver's wheel. Right, there's

507
00:36:36,760 --> 00:36:40,399
Speaker 1: still some there's still a body there, is what they're saying, Well,

508
00:36:40,440 --> 00:36:41,960
Speaker 1: there is a body there, and that's one of the

509
00:36:42,000 --> 00:36:44,440
Speaker 1: big things that shot spot are always tells there's a

510
00:36:44,560 --> 00:36:48,799
Speaker 1: human being making the determination. Highly trained human being will

511
00:36:48,840 --> 00:36:51,440
Speaker 1: be able to discern the difference between a gunshot and

512
00:36:51,840 --> 00:36:56,279
Speaker 1: whatever backfire. And this is super tricky because shot spot

513
00:36:56,280 --> 00:36:59,240
Speaker 1: are also teuts that that analysts, that highly trained person

514
00:36:59,320 --> 00:37:01,359
Speaker 1: is going to make it to termination within I think

515
00:37:01,400 --> 00:37:04,600
Speaker 1: a minute or ninety seconds or something some time frame

516
00:37:04,640 --> 00:37:08,160
Speaker 1: that is super short. So if that person does make

517
00:37:08,200 --> 00:37:12,800
Speaker 1: that determination initially this was a gun shot. The coordinates

518
00:37:12,840 --> 00:37:14,920
Speaker 1: that were given to us by the shot Spotter system

519
00:37:14,960 --> 00:37:17,400
Speaker 1: are here, and it's a gun shot for sure. Then

520
00:37:17,920 --> 00:37:21,760
Speaker 1: that person goes back and changes it to something else,

521
00:37:21,920 --> 00:37:25,920
Speaker 1: after officers have already been dispatched, Like is that for

522
00:37:26,040 --> 00:37:29,759
Speaker 1: shot Spotters records? Is that for, you know, so that

523
00:37:29,800 --> 00:37:32,759
Speaker 1: they can change their accuracy rating and put it on

524
00:37:32,800 --> 00:37:35,279
Speaker 1: their website, Like what is that for? Why would they

525
00:37:35,280 --> 00:37:39,760
Speaker 1: do that? Or hypothetically, what if it gets changed after

526
00:37:39,920 --> 00:37:46,360
Speaker 1: conversations with with the local law enforcement right before something

527
00:37:46,400 --> 00:37:49,880
Speaker 1: goes to court. Spooky stuff, you know. This is a

528
00:37:50,200 --> 00:37:53,319
Speaker 1: shot Spotter, by the way, disputes this idea of the

529
00:37:53,360 --> 00:37:59,759
Speaker 1: company's analysts changing those judgments, and they dispute I mean

530
00:38:00,040 --> 00:38:02,759
Speaker 1: very diplomatic when I say dispute. By the way, they

531
00:38:04,040 --> 00:38:09,080
Speaker 1: dispute the idea of law enforcement being able to put

532
00:38:09,120 --> 00:38:12,160
Speaker 1: their thumb on the scale right and say no no

533
00:38:12,160 --> 00:38:14,400
Speaker 1: no no, no, no no no call this a gunshot

534
00:38:15,040 --> 00:38:17,759
Speaker 1: or or no no no no, no, no, change the

535
00:38:17,840 --> 00:38:23,080
Speaker 1: time stamp here. Uh, they did have Vice and associated

536
00:38:23,160 --> 00:38:28,799
Speaker 1: Press change part of their original reporting after threatening lawsuits.

537
00:38:29,640 --> 00:38:31,879
Speaker 1: So if you read these Vice articles, if you read

538
00:38:31,920 --> 00:38:34,920
Speaker 1: the AP articles, you read a couple other things, you'll

539
00:38:34,960 --> 00:38:38,720
Speaker 1: see at the very end there's this, Hey, we got

540
00:38:38,800 --> 00:38:43,560
Speaker 1: this court case and this specific thing is not exactly

541
00:38:43,560 --> 00:38:45,960
Speaker 1: the way it may have sounded in the original article.

542
00:38:46,719 --> 00:38:49,800
Speaker 1: But that's not the same thing as saying, oh wait,

543
00:38:50,160 --> 00:38:54,200
Speaker 1: there's no problem at all. There's huge problems. There are

544
00:38:54,320 --> 00:38:57,319
Speaker 1: huge problems. I don't think that's a hot take. I

545
00:38:57,440 --> 00:39:00,160
Speaker 1: don't think that's too far for us to say that

546
00:39:00,280 --> 00:39:04,799
Speaker 1: there is opportunity for corruption and conspiracy in this thing,

547
00:39:05,360 --> 00:39:09,399
Speaker 1: right because also, I mean this is this could run

548
00:39:09,400 --> 00:39:14,280
Speaker 1: into some of the same problems as um automated facial

549
00:39:14,360 --> 00:39:19,880
Speaker 1: recognition technology bias against people of color. It's a little

550
00:39:19,920 --> 00:39:26,359
Speaker 1: more complicated because it goes into intergenerational things, right, kind

551
00:39:26,400 --> 00:39:31,520
Speaker 1: of like how um god, I love that food deserts episode. Man,

552
00:39:32,320 --> 00:39:37,560
Speaker 1: Big food deserts are an accidental conspiracy. They're just people

553
00:39:37,840 --> 00:39:42,520
Speaker 1: not knowing, people in power, not understanding nor caring about

554
00:39:42,600 --> 00:39:46,560
Speaker 1: the full consequences of their actions for short term gain.

555
00:39:47,160 --> 00:39:52,120
Speaker 1: And now you know uh, with shot Spotter. We see

556
00:39:53,320 --> 00:39:55,120
Speaker 1: you know, really went to bat with him. I know

557
00:39:55,200 --> 00:39:57,360
Speaker 1: you read this. Two man who really went to bat

558
00:39:57,520 --> 00:40:00,960
Speaker 1: with shot Spotter was the A C l You And

559
00:40:01,000 --> 00:40:07,319
Speaker 1: they're like clarifications in corrections. Part of their report is

560
00:40:07,440 --> 00:40:11,120
Speaker 1: much longer, and it's all about how they had a

561
00:40:11,160 --> 00:40:16,760
Speaker 1: fight on the phone and how the the CEO called

562
00:40:16,920 --> 00:40:19,640
Speaker 1: the folks at the a c l U and said

563
00:40:20,000 --> 00:40:22,000
Speaker 1: no no, no, no, no no no. But you know

564
00:40:22,040 --> 00:40:24,759
Speaker 1: the A c l U is lawyers, so threatening them

565
00:40:24,800 --> 00:40:28,399
Speaker 1: with a defamation lawsuit that's not really going to swing

566
00:40:28,440 --> 00:40:32,040
Speaker 1: the needle. So back to our guy Clark, the CEO

567
00:40:32,040 --> 00:40:37,320
Speaker 1: of shot Spotter. He was saying, look, these algorithms are

568
00:40:37,480 --> 00:40:42,600
Speaker 1: doing the math. They're not spooky robo minds. They're doing

569
00:40:43,000 --> 00:40:46,240
Speaker 1: basic math that could be done by hand, pen and paper.

570
00:40:46,560 --> 00:40:50,400
Speaker 1: They're just doing it faster. Again, it's all about the

571
00:40:50,440 --> 00:40:54,319
Speaker 1: response time to your earlier pointment. But he then he

572
00:40:54,360 --> 00:40:59,280
Speaker 1: talks about the more complex algorithm. Both of these are proprietary,

573
00:40:59,360 --> 00:41:03,040
Speaker 1: by the way. That's the black box stuff. Also, the CEO,

574
00:41:03,239 --> 00:41:06,960
Speaker 1: Mr Clark does not like us using the phrase black box,

575
00:41:08,320 --> 00:41:11,880
Speaker 1: but it kind of is a black box. No one's

576
00:41:11,920 --> 00:41:15,240
Speaker 1: been under the hood, No one can peer review this stuff.

577
00:41:15,600 --> 00:41:19,760
Speaker 1: That's a huge problem if you're going into a court

578
00:41:19,760 --> 00:41:25,280
Speaker 1: of law. Right you're saying, Hey, I have proof that

579
00:41:25,440 --> 00:41:28,839
Speaker 1: this is the kind of gun that was fired here.

580
00:41:29,160 --> 00:41:31,080
Speaker 1: So what's your proof? Do you have by witnesses? No?

581
00:41:31,960 --> 00:41:35,520
Speaker 1: Do you have a recording? Yes? How does it work? Magic?

582
00:41:36,280 --> 00:41:39,720
Speaker 1: It just doesn't. It doesn't look good. And I again,

583
00:41:39,760 --> 00:41:42,239
Speaker 1: I'm not trying to dunk too hard. I know these

584
00:41:42,280 --> 00:41:47,120
Speaker 1: are good faith actors, but if you can't show your work,

585
00:41:48,080 --> 00:41:51,160
Speaker 1: then the law gets really tricky, right, Yeah, it does.

586
00:41:51,400 --> 00:41:55,799
Speaker 1: It gets extremely tricky. Why don't we jump to what

587
00:41:55,920 --> 00:41:58,080
Speaker 1: the other critics are saying, Like there's a there's a

588
00:41:58,120 --> 00:42:01,640
Speaker 1: lot of criticism of this thing that we're talking about Chicago.

589
00:42:01,680 --> 00:42:06,560
Speaker 1: Earlier in this episode we talked about nineteen and as

590
00:42:06,719 --> 00:42:10,760
Speaker 1: of I believe it's this year and maybe the summer

591
00:42:10,880 --> 00:42:14,640
Speaker 1: of two. There's a case right now, a lawsuit that

592
00:42:14,760 --> 00:42:18,240
Speaker 1: is attempting to remove shot Spotter completely from the city

593
00:42:18,239 --> 00:42:21,560
Speaker 1: of Chicago because there's a thirteen year old boy named

594
00:42:21,560 --> 00:42:25,760
Speaker 1: Adam Toledo who was killed, and there's a ton of

595
00:42:26,320 --> 00:42:30,040
Speaker 1: information coming out about how shot Spotter technology is actually

596
00:42:30,080 --> 00:42:32,680
Speaker 1: being used in the city right now, And I want

597
00:42:32,719 --> 00:42:36,080
Speaker 1: to stay in Chicago there because we're it's had problems

598
00:42:36,200 --> 00:42:40,160
Speaker 1: for a long time with this system. In March of

599
00:42:40,239 --> 00:42:44,560
Speaker 1: this year, the Associated Press reported how there's a person

600
00:42:44,640 --> 00:42:48,480
Speaker 1: named Michael Williams living in Chicago, but he went to

601
00:42:48,560 --> 00:42:52,240
Speaker 1: jail in August one for the murder of of somebody

602
00:42:52,239 --> 00:42:57,000
Speaker 1: in his neighborhood. Okay, he went to jail for murdering somebody.

603
00:42:57,160 --> 00:42:59,800
Speaker 1: And a key part of that evidence, guess what it

604
00:42:59,800 --> 00:43:03,360
Speaker 1: will us It was a quote clip of noiseless security

605
00:43:03,440 --> 00:43:07,120
Speaker 1: video showing a car driving through an intersection and allowed

606
00:43:07,200 --> 00:43:15,200
Speaker 1: bang picked up by shot spotter. Uh yeah, okay, okay,

607
00:43:15,280 --> 00:43:19,920
Speaker 1: So again, is this wrong place, wrong time, or is

608
00:43:19,960 --> 00:43:23,759
Speaker 1: it working perfectly? Is that exactly what happened? Or is

609
00:43:23,800 --> 00:43:26,799
Speaker 1: that exactly what happened? How would you put how can

610
00:43:26,840 --> 00:43:28,880
Speaker 1: you put those things together? I mean, if they if

611
00:43:28,880 --> 00:43:33,759
Speaker 1: they're time stamped, then logically it would make sense if

612
00:43:33,800 --> 00:43:36,759
Speaker 1: you can track that sound down and you know, triangulated

613
00:43:36,840 --> 00:43:39,759
Speaker 1: the way shot Spotter allegedly does and it's supposed to do,

614
00:43:40,760 --> 00:43:46,520
Speaker 1: then maybe that's great evidence, but it does seems it

615
00:43:46,600 --> 00:43:49,799
Speaker 1: seems tough to connect an audio and video feed from

616
00:43:49,800 --> 00:43:53,040
Speaker 1: separate sources like that and call it enough evidence to

617
00:43:53,080 --> 00:43:57,280
Speaker 1: send somebody to jail. Yeah, that's that's the thing. Like, um,

618
00:43:57,560 --> 00:44:01,640
Speaker 1: he does go to jail. Williams ends almost a year

619
00:44:01,680 --> 00:44:05,080
Speaker 1: in jail. And this is this guy has lived in

620
00:44:05,080 --> 00:44:09,719
Speaker 1: this neighborhood for a while. He's got grandkids, you know

621
00:44:09,719 --> 00:44:15,560
Speaker 1: what I mean. He's not some hot shot, unhinged, lunatic

622
00:44:16,040 --> 00:44:21,080
Speaker 1: nineteen year old pulling pranks for TikTok cloud. He's a dude.

623
00:44:21,880 --> 00:44:25,560
Speaker 1: He's a dude who has children and grandchildren. He's not

624
00:44:25,920 --> 00:44:29,560
Speaker 1: the person you would look at and automatically think criminal.

625
00:44:30,239 --> 00:44:34,280
Speaker 1: But Shah Sponner said he was because it pinpointed where

626
00:44:34,280 --> 00:44:38,600
Speaker 1: that gunshot came from. Supposedly. And again, like we said

627
00:44:38,640 --> 00:44:42,480
Speaker 1: in part one, the that system, when it works, it

628
00:44:42,600 --> 00:44:47,520
Speaker 1: has a high degree of fidelity or sophistication. Right, I

629
00:44:47,520 --> 00:44:52,080
Speaker 1: don't want to say accuracy, sophistication, I'll say but but

630
00:44:53,800 --> 00:44:59,000
Speaker 1: good news for Williams here. Eventually the case is dismissed.

631
00:44:59,680 --> 00:45:03,279
Speaker 1: He us is almost a year of his life at

632
00:45:03,320 --> 00:45:07,160
Speaker 1: an age where one year matters quite a bit. And

633
00:45:07,960 --> 00:45:12,239
Speaker 1: the prosecutors dismissed this because they ultimately say they have

634
00:45:12,320 --> 00:45:17,480
Speaker 1: insufficient evidence. They say, this video footage, this, uh, the

635
00:45:17,640 --> 00:45:22,280
Speaker 1: statements of people in the neighborhood, and particularly this data

636
00:45:22,400 --> 00:45:27,200
Speaker 1: from shot Spotter, which again overall the process cannot be

637
00:45:27,360 --> 00:45:31,680
Speaker 1: pure reviewed. They say it's just not enough. So Associated

638
00:45:31,719 --> 00:45:35,920
Speaker 1: Press goes on to say, look, our investigation found some

639
00:45:36,000 --> 00:45:42,440
Speaker 1: really troubling stuff here, like some Orwellian style surveillance economy

640
00:45:42,600 --> 00:45:47,480
Speaker 1: kind of stuff. Uh, it's the idea that we'll just

641
00:45:47,520 --> 00:45:49,680
Speaker 1: give you the quote already did a quote? Do you

642
00:45:49,719 --> 00:45:52,520
Speaker 1: want to do this quote? Yes. According to the AP quote,

643
00:45:52,600 --> 00:45:56,840
Speaker 1: shot Spotter employees can and often do change the source

644
00:45:56,880 --> 00:46:00,160
Speaker 1: of sounds picked up by its sensors after listening to

645
00:46:00,200 --> 00:46:05,319
Speaker 1: audio recordings, introducing the possibility of human bias into the

646
00:46:05,320 --> 00:46:09,640
Speaker 1: gunshot detection algorithm. That's a great point. In the end,

647
00:46:09,760 --> 00:46:13,640
Speaker 1: it is a human using their training and their understanding

648
00:46:14,440 --> 00:46:20,160
Speaker 1: to make the determination and possibly leveraging their relationships. Right

649
00:46:20,520 --> 00:46:23,719
Speaker 1: because still, you know, if you're listening to this and

650
00:46:23,719 --> 00:46:26,479
Speaker 1: you're human, you know the score, you know the game.

651
00:46:26,560 --> 00:46:30,640
Speaker 1: You know humans are often so AP continues and they

652
00:46:30,680 --> 00:46:35,719
Speaker 1: claim quote employees can and do modify the location or

653
00:46:35,840 --> 00:46:40,759
Speaker 1: number of shots fired at the request of police, according

654
00:46:40,840 --> 00:46:44,080
Speaker 1: to court records. And in the past we're still in

655
00:46:44,120 --> 00:46:48,400
Speaker 1: this quote. And in the past, city dispatchers or police

656
00:46:48,600 --> 00:46:55,200
Speaker 1: themselves could make some of these changes end quote. Not

657
00:46:55,360 --> 00:47:00,239
Speaker 1: a good look. Well yeah, and of course shot spotter says, no,

658
00:47:00,640 --> 00:47:05,480
Speaker 1: that is not true. No, no, And they say that

659
00:47:05,560 --> 00:47:09,400
Speaker 1: too many claims that are similar to this, um And

660
00:47:09,520 --> 00:47:13,240
Speaker 1: ultimately it's not it's not necessarily like you're completely wrong.

661
00:47:13,360 --> 00:47:17,840
Speaker 1: It's more like this is misleading. This isn't quite accurate,

662
00:47:17,960 --> 00:47:25,200
Speaker 1: this is not accurate. Nice. Yeah, I mean, of course,

663
00:47:25,280 --> 00:47:28,279
Speaker 1: of course there are going it's a business. This is

664
00:47:28,320 --> 00:47:33,080
Speaker 1: not a nonprofit, this is not a foundation. They're out

665
00:47:33,120 --> 00:47:37,520
Speaker 1: here to hustle and sell and hopefully, um, hopefully save

666
00:47:37,640 --> 00:47:43,200
Speaker 1: human lives. Again, that's that's the idea. But because it's

667
00:47:43,239 --> 00:47:46,040
Speaker 1: a for profit business, what are they going to say?

668
00:47:47,480 --> 00:47:49,840
Speaker 1: What are they can be like, oh, geez a, p

669
00:47:50,320 --> 00:47:54,439
Speaker 1: you got me, good game. No, they're going to use

670
00:47:54,520 --> 00:47:57,919
Speaker 1: some of these massive profits to sue the Christ out

671
00:47:57,920 --> 00:48:01,440
Speaker 1: of people who are making them look ad. That's just

672
00:48:01,680 --> 00:48:08,880
Speaker 1: rational actors. Right. So shot Spotter spoiler Alert vigorously contest

673
00:48:09,000 --> 00:48:12,400
Speaker 1: these and similar claims. And like you said, Matt, there

674
00:48:13,239 --> 00:48:17,560
Speaker 1: saying these are missing nuance, misleading, they don't paint the

675
00:48:17,600 --> 00:48:24,359
Speaker 1: full picture. Look, we require this human participation. Our algorithms

676
00:48:24,360 --> 00:48:27,960
Speaker 1: are constantly being evaluated tweaked you know what I mean,

677
00:48:28,400 --> 00:48:31,799
Speaker 1: we're building the plane we're flying, and we think that

678
00:48:31,880 --> 00:48:36,959
Speaker 1: as a good thing, just like Vice. Associated Press later

679
00:48:37,040 --> 00:48:41,800
Speaker 1: issues a statement correcting their original claims because both articles

680
00:48:41,800 --> 00:48:47,240
Speaker 1: are talking about uh, these court cases, right, specific instances

681
00:48:47,280 --> 00:48:49,799
Speaker 1: of court cases. But this is a moment where we

682
00:48:49,880 --> 00:48:55,399
Speaker 1: need to exercise not um analysis of anecdotes, not analysis

683
00:48:55,480 --> 00:49:00,799
Speaker 1: of specific cases. We need to exercise structural thoughts. Right.

684
00:49:01,120 --> 00:49:05,360
Speaker 1: The problems are bigger than one sick tree in the forest.

685
00:49:05,800 --> 00:49:09,320
Speaker 1: We need, we need what we need. What the suits

686
00:49:09,320 --> 00:49:11,759
Speaker 1: at our job called the what was it? There's the

687
00:49:11,880 --> 00:49:17,400
Speaker 1: forty foot view and then high level views. Yeah, I

688
00:49:17,440 --> 00:49:20,319
Speaker 1: mean you go, you can see most things in your

689
00:49:20,360 --> 00:49:24,920
Speaker 1: general area. Yeah, the full picture, sure, the whole shebey.

690
00:49:25,760 --> 00:49:31,480
Speaker 1: So shot Spotter is still a business. It is actively

691
00:49:32,719 --> 00:49:36,399
Speaker 1: is actively expanding or attempting to of course, as all

692
00:49:36,480 --> 00:49:43,360
Speaker 1: businesses do. It is actively you know, um monitoring about

693
00:49:43,440 --> 00:49:46,520
Speaker 1: nine hundred and eleven square miles of the United States

694
00:49:46,800 --> 00:49:52,280
Speaker 1: twenty four hours a day, seven days a week, no holidays.

695
00:49:52,320 --> 00:49:58,319
Speaker 1: But a number of cities are spooked by this, by

696
00:49:58,360 --> 00:50:01,719
Speaker 1: this court of public opinion and stuff going on, right,

697
00:50:02,239 --> 00:50:07,240
Speaker 1: and they've stopped using shot Spotter technology because they going

698
00:50:07,280 --> 00:50:10,560
Speaker 1: back to what you pointed out earlier, Man, they have

699
00:50:10,800 --> 00:50:15,279
Speaker 1: decided that shot Spotter creates too many false positives and

700
00:50:15,480 --> 00:50:18,040
Speaker 1: false negatives. Could you break down for us what what

701
00:50:18,239 --> 00:50:21,080
Speaker 1: is a false positive and false negative? In this context?

702
00:50:21,520 --> 00:50:24,160
Speaker 1: False positive would be shot Spotter says there was a

703
00:50:24,160 --> 00:50:27,000
Speaker 1: gun shot when there was not. A false negative would

704
00:50:27,000 --> 00:50:30,160
Speaker 1: be shot Spotter missing a real gun shot that occurred

705
00:50:30,200 --> 00:50:35,839
Speaker 1: in the city, missing a gun shot, calling it something else. Right,

706
00:50:36,200 --> 00:50:38,920
Speaker 1: Oh wow, Yeah, that's just a fart, That's just a

707
00:50:39,000 --> 00:50:42,000
Speaker 1: dump truck. I gotta get off this fartest gunshot thing.

708
00:50:42,360 --> 00:50:47,440
Speaker 1: It's it's ruining the show. But the but but so

709
00:50:47,920 --> 00:50:52,279
Speaker 1: a false false negative. It's kind of like going to

710
00:50:52,320 --> 00:50:56,759
Speaker 1: a McDonald's and they don't have fries. Right, that's exactly right.

711
00:50:57,440 --> 00:51:00,120
Speaker 1: Isn't this your job? Is it? Is it that like

712
00:51:00,200 --> 00:51:06,719
Speaker 1: your main thing to do? Yeah? What profits center? Right?

713
00:51:07,000 --> 00:51:11,240
Speaker 1: One of McDonald's biggest profits as a corporation is actually

714
00:51:11,320 --> 00:51:15,480
Speaker 1: real estate. Just like Target, the department store here in

715
00:51:15,480 --> 00:51:18,719
Speaker 1: the US makes a ton of money off surveillance systems.

716
00:51:20,280 --> 00:51:23,000
Speaker 1: We're fun at parties here. We know we're going long.

717
00:51:23,360 --> 00:51:28,440
Speaker 1: But one last piece of this episode is incredibly important.

718
00:51:28,760 --> 00:51:31,840
Speaker 1: We hope We have done an all right job of

719
00:51:32,000 --> 00:51:35,600
Speaker 1: giving you the lay of the land, the forty foot view,

720
00:51:37,160 --> 00:51:39,080
Speaker 1: and showing you some of the support, some of the

721
00:51:39,120 --> 00:51:44,279
Speaker 1: reasons why people believe in systems like this, and some

722
00:51:44,320 --> 00:51:48,600
Speaker 1: of the reasons why people are concerned. But we have

723
00:51:48,800 --> 00:51:53,320
Speaker 1: to end on the future. Shot Spotter is not going away.

724
00:51:53,440 --> 00:51:58,279
Speaker 1: Technology like this is not going away. It is evolving.

725
00:51:58,640 --> 00:52:06,000
Speaker 1: We found this stat the Department of Justice says that overall, UM,

726
00:52:06,120 --> 00:52:10,440
Speaker 1: quite recently Uncle Sam has spent six point nine million

727
00:52:10,480 --> 00:52:16,600
Speaker 1: dollars on gunshot detection systems. This includes shot Spotter. But

728
00:52:16,719 --> 00:52:20,520
Speaker 1: this number, it feels kind of small. Six point nine million, right,

729
00:52:20,760 --> 00:52:24,240
Speaker 1: but that doesn't count many more millions spent by state

730
00:52:24,360 --> 00:52:28,640
Speaker 1: and local governments. It's just the fetes. This is a

731
00:52:28,680 --> 00:52:33,239
Speaker 1: good business, and we we kind of teased cynically the

732
00:52:33,360 --> 00:52:37,920
Speaker 1: idea of predicting crime. This is Yeah, this is the

733
00:52:37,960 --> 00:52:40,279
Speaker 1: spookiest part, at least for me, Matt. This is the

734
00:52:40,320 --> 00:52:44,080
Speaker 1: spookiest part. Yeah, because shot spot already has all this technology.

735
00:52:44,080 --> 00:52:47,360
Speaker 1: It's already in all of these cities across the United States.

736
00:52:47,800 --> 00:52:50,520
Speaker 1: And then in they went and did a little thing

737
00:52:51,040 --> 00:52:56,600
Speaker 1: called an acquisition. They acquired another company. Sorry, in the words,

738
00:52:57,840 --> 00:53:01,799
Speaker 1: UM they acquired a company called hunt Lab, and then

739
00:53:01,880 --> 00:53:06,640
Speaker 1: hunch Lab takes its AI model and integrates it with

740
00:53:06,840 --> 00:53:11,799
Speaker 1: shot spotters shot detection models and guess what they can

741
00:53:11,840 --> 00:53:14,840
Speaker 1: predict where gunshots may happen or are going to have

742
00:53:14,920 --> 00:53:19,359
Speaker 1: a an increased likely likelihood of happening. Wait wait, wait,

743
00:53:19,360 --> 00:53:23,799
Speaker 1: wait wait wait, the algorithms and the machine learning and

744
00:53:23,840 --> 00:53:28,160
Speaker 1: the data can predict or the marketing department says they

745
00:53:28,160 --> 00:53:31,200
Speaker 1: can predict because this is part of the problem. Right,

746
00:53:31,520 --> 00:53:33,520
Speaker 1: Let's put it this way. It's again it's building the

747
00:53:33,560 --> 00:53:36,280
Speaker 1: plane as you go kind of thing. So it will

748
00:53:36,320 --> 00:53:40,279
Speaker 1: likely increase. Their accuracy will likely increase if they put

749
00:53:40,280 --> 00:53:43,360
Speaker 1: it in enough places, if they have it active for

750
00:53:43,480 --> 00:53:47,080
Speaker 1: long enough, then it probably will increase and it will

751
00:53:47,120 --> 00:53:50,960
Speaker 1: get more predictive. But right now, who knows, well, I

752
00:53:51,000 --> 00:53:52,840
Speaker 1: mean they've had a couple of years. We'll see what happens.

753
00:53:53,280 --> 00:54:00,319
Speaker 1: How is that not violating fundamental aspects of US governance? Right,

754
00:54:00,960 --> 00:54:06,279
Speaker 1: Innocent until proven guilty, you know, like you are, you

755
00:54:06,320 --> 00:54:13,080
Speaker 1: are prejudged. That's the idea. Ultimately, that's that's where this goes, right,

756
00:54:13,160 --> 00:54:17,400
Speaker 1: the we and we don't have visibility on how granular

757
00:54:17,480 --> 00:54:19,960
Speaker 1: this might get. And I know that's how that also

758
00:54:20,040 --> 00:54:24,000
Speaker 1: sounds like corporate gobbledegook. But since we don't know whether

759
00:54:24,040 --> 00:54:32,399
Speaker 1: they're saying hey the month this month in December, we

760
00:54:32,600 --> 00:54:36,840
Speaker 1: we statistically will see higher levels of carth after something

761
00:54:37,400 --> 00:54:43,560
Speaker 1: versus three forty two pm April seventeen on the corner

762
00:54:43,600 --> 00:54:47,279
Speaker 1: of ninth and two below. We need to have two

763
00:54:47,280 --> 00:54:50,200
Speaker 1: cop cars because they're going to be there's going to

764
00:54:50,239 --> 00:54:52,200
Speaker 1: be a heist, you know what I mean. We don't

765
00:54:52,200 --> 00:54:56,400
Speaker 1: know how how close they can zoom in on the future,

766
00:54:56,760 --> 00:54:59,520
Speaker 1: but they're definitely trying to zoom in on the future

767
00:54:59,560 --> 00:55:04,799
Speaker 1: a little bit. And that should scare you because there

768
00:55:04,840 --> 00:55:10,000
Speaker 1: are no real laws yet. There's no real precedent to

769
00:55:10,239 --> 00:55:14,960
Speaker 1: protect civilians right and to protect law enforcement. To be

770
00:55:15,760 --> 00:55:20,600
Speaker 1: about it, there are no real laws in effect to

771
00:55:20,840 --> 00:55:25,400
Speaker 1: protect human beings in a situation that becomes closer and

772
00:55:25,480 --> 00:55:31,920
Speaker 1: closer to prophetic algorithmic time travel. And I know I'm

773
00:55:31,960 --> 00:55:34,520
Speaker 1: like styling a little bit on this Welcome to our

774
00:55:34,840 --> 00:55:39,960
Speaker 1: creepy accidental spoken word performance. But but you do need

775
00:55:40,040 --> 00:55:42,759
Speaker 1: to worry about this. Whether you are a supporter of

776
00:55:43,040 --> 00:55:45,520
Speaker 1: things like shot spot or whether you are an opponent.

777
00:55:46,120 --> 00:55:48,480
Speaker 1: The reason you need to worry is that I would

778
00:55:48,520 --> 00:55:52,880
Speaker 1: posit this evolution in technology is already happening. It's on

779
00:55:52,920 --> 00:55:57,080
Speaker 1: the way, It's inevitable. And Matt, I thought of you

780
00:55:57,320 --> 00:56:00,160
Speaker 1: when I was when I was thinking this part. It

781
00:56:00,200 --> 00:56:03,560
Speaker 1: feels like a classic Matt thing man. People are so

782
00:56:03,640 --> 00:56:12,000
Speaker 1: worried about these proprietary audio surveillance systems. Oh, microphones are everywhere.

783
00:56:12,040 --> 00:56:17,800
Speaker 1: What kind of world is that gonna be the big hurdle? Oh,

784
00:56:17,840 --> 00:56:20,759
Speaker 1: I'm sure I'm doing the class. I'm doing your move man,

785
00:56:20,840 --> 00:56:23,480
Speaker 1: where you like you hold up the phone whenever people

786
00:56:23,560 --> 00:56:26,520
Speaker 1: talk about you know, I can't be tracked, and then

787
00:56:26,600 --> 00:56:29,680
Speaker 1: they're like tweeting us and stuff. I had that conversation

788
00:56:29,760 --> 00:56:31,880
Speaker 1: yesterday with a couple of really nice A D T

789
00:56:32,280 --> 00:56:34,200
Speaker 1: reps that came out to the house to try and

790
00:56:34,239 --> 00:56:37,200
Speaker 1: sell me a security system, and we ended up talking

791
00:56:37,239 --> 00:56:40,239
Speaker 1: for about forty minutes outside on my front stoop. And

792
00:56:40,560 --> 00:56:42,640
Speaker 1: that was there. One of the big concerns was like

793
00:56:44,120 --> 00:56:46,080
Speaker 1: government spying and that kind of thing, and I did

794
00:56:46,200 --> 00:56:48,080
Speaker 1: exactly that, just held up my phone. Was like, you

795
00:56:48,080 --> 00:56:53,040
Speaker 1: guys were really worried about the government. I said, meta

796
00:56:53,200 --> 00:56:57,160
Speaker 1: in alphabet already got you, bro? You know what help

797
00:56:57,239 --> 00:57:00,719
Speaker 1: us mess up the search terms I'll I'm pretty sure

798
00:57:00,760 --> 00:57:04,200
Speaker 1: I know how the I'm pretty sure I got under

799
00:57:04,239 --> 00:57:07,320
Speaker 1: the hood of the targeted advertise on social media and

800
00:57:09,600 --> 00:57:15,560
Speaker 1: not I'm nine seven percent sure that I figured it out, Matt,

801
00:57:15,600 --> 00:57:18,200
Speaker 1: And it might be an off conversation. Yeah, we gotta

802
00:57:18,240 --> 00:57:22,880
Speaker 1: figure You and I have to have to, you know,

803
00:57:23,000 --> 00:57:25,680
Speaker 1: huddle up and decide whether or not it's worth going

804
00:57:25,720 --> 00:57:29,600
Speaker 1: to air with. But it doesn't involve the dismantled microwave

805
00:57:33,000 --> 00:57:37,240
Speaker 1: you do, you know one, look, don't try it at home.

806
00:57:37,920 --> 00:57:40,200
Speaker 1: But it's like, it's kind of cool, right, you would

807
00:57:40,200 --> 00:57:43,240
Speaker 1: have done it if you were if you were hanging out.

808
00:57:43,320 --> 00:57:44,840
Speaker 1: This is before we knew each other, you would have

809
00:57:45,440 --> 00:57:49,520
Speaker 1: been so down. We should have continued anyway, the like.

810
00:57:49,640 --> 00:57:53,400
Speaker 1: So the surveillance state, dismantled mike waves aside, I think

811
00:57:53,440 --> 00:57:56,040
Speaker 1: the legal department requires us to tell you not to

812
00:57:56,120 --> 00:58:01,240
Speaker 1: do it, dismantled mic waves aside the to The only

813
00:58:01,320 --> 00:58:06,880
Speaker 1: real big hurdles to a constant surveillance state now would

814
00:58:06,920 --> 00:58:10,400
Speaker 1: be privacy concerns, if you want to put it really simply,

815
00:58:10,840 --> 00:58:15,120
Speaker 1: privacy concerns, right, the software of the mind and the

816
00:58:15,200 --> 00:58:19,240
Speaker 1: community and society. That's what we mean with privacy concerns,

817
00:58:19,560 --> 00:58:23,080
Speaker 1: how could it be normalized? And the second thing would

818
00:58:23,120 --> 00:58:27,240
Speaker 1: be the physical aspect infrastructure. But as we said, we're

819
00:58:27,280 --> 00:58:31,360
Speaker 1: holding up our phones. The infrastructure is already there. It

820
00:58:31,600 --> 00:58:38,240
Speaker 1: is technically easier. I know this. Some people may have

821
00:58:38,320 --> 00:58:44,160
Speaker 1: problem with this. It is technically easier to deploy a nationwide, pervasive,

822
00:58:45,040 --> 00:58:49,680
Speaker 1: around the clock surveillance system than it is to have

823
00:58:49,840 --> 00:58:55,280
Speaker 1: everybody driving electric cars. Because you need you need physical infrastructure.

824
00:58:55,360 --> 00:58:58,680
Speaker 1: It's already there. It's the easiest part of the equation

825
00:58:58,760 --> 00:59:05,080
Speaker 1: to solve millions of Americans or US residents, I should say, well, no,

826
00:59:05,080 --> 00:59:10,360
Speaker 1: North American continent, South American continent millions, millions, millions already

827
00:59:10,440 --> 00:59:15,360
Speaker 1: carry a phone constantly, and ethics aside, How difficult is

828
00:59:15,440 --> 00:59:19,320
Speaker 1: it to do the Christopher Nolan Dark Knight Batman trick

829
00:59:19,720 --> 00:59:22,680
Speaker 1: and just link them all up and have the best

830
00:59:22,760 --> 00:59:25,680
Speaker 1: echolocation ever, the kind of stuff that would make a

831
00:59:25,720 --> 00:59:30,520
Speaker 1: bat jealous or a batman. That was awkward, but let's

832
00:59:30,600 --> 00:59:35,560
Speaker 1: keep it in You like, uh, Jez thinks, well, you

833
00:59:35,640 --> 00:59:40,360
Speaker 1: know any landing you can walk away from, right shot spotter,

834
00:59:42,200 --> 00:59:45,919
Speaker 1: build your play. No, that was too far. Alright, Well

835
00:59:45,960 --> 00:59:48,760
Speaker 1: that's our show. Thank you so much for tuning in

836
00:59:49,000 --> 00:59:54,840
Speaker 1: our pal Noel will be back soon. I personally can't

837
00:59:54,840 --> 00:59:59,280
Speaker 1: wait to listen to our strange news listener mail segments. Uh.

838
00:59:59,360 --> 01:00:02,760
Speaker 1: And we also can't wait for you to be a

839
01:00:02,800 --> 01:00:05,600
Speaker 1: part of the show, folks, So let us know your

840
01:00:05,640 --> 01:00:08,720
Speaker 1: thoughts again. One of the reasons this was a two

841
01:00:08,720 --> 01:00:15,520
Speaker 1: part episode is entirely because we received so much in depth,

842
01:00:16,040 --> 01:00:22,520
Speaker 1: well thought out correspondence with people who clearly are on

843
01:00:22,680 --> 01:00:27,040
Speaker 1: different have different perspectives about whether or not shot Spotter

844
01:00:27,200 --> 01:00:30,640
Speaker 1: is good, whether or not this technology is inevitable, whether

845
01:00:30,680 --> 01:00:35,280
Speaker 1: or not it's even worth the money, right, and everybody

846
01:00:35,560 --> 01:00:39,680
Speaker 1: came with fire, you know what I mean? And this

847
01:00:39,680 --> 01:00:42,080
Speaker 1: this is one of the reasons we do this show.

848
01:00:42,120 --> 01:00:44,560
Speaker 1: So we can't when we say we can't wait to

849
01:00:44,600 --> 01:00:46,640
Speaker 1: hear what you think, we mean it. We try to

850
01:00:46,640 --> 01:00:50,840
Speaker 1: be easy to find online Facebook, Twitter, YouTube. Do we

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Speaker 1: have the MySpace Yeah, no, still know MySpace one day man.

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Speaker 1: One thing. We do have an Instagram conspiracy stuff. We

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Speaker 1: do have a phone number one eight three three st

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01:01:03,440 --> 01:01:06,320
Speaker 1: d w y t K. Just call in. It's a voicemail,

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01:01:06,480 --> 01:01:09,200
Speaker 1: super easy, three minutes. Give yourself a nickname and let

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Speaker 1: us know if we can use your message and voice

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01:01:11,640 --> 01:01:13,800
Speaker 1: on the air. If you don't want to do that,

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01:01:13,920 --> 01:01:16,480
Speaker 1: why not instead send us a good old fashioned email.

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01:01:16,600 --> 01:01:39,080
Speaker 1: We are conspiracy at i heeart radio dot com. Stuff

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01:01:39,120 --> 01:01:41,000
Speaker 1: they don't want you to know. Is a production of

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Speaker 1: I heart Radio. For more podcasts from my heart Radio,

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01:01:44,160 --> 01:01:47,000
Speaker 1: visit the i heart Radio app, Apple Podcasts, or wherever

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01:01:47,080 --> 01:01:48,360
Speaker 1: you listen to your favorite shows.