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Speaker 1: The public has had a long held fascination with detectives.

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Speaker 1: Detective see a side of life the average person is

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Speaker 1: never exposed to. I spent thirty four years as a cop.

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Speaker 1: For twenty five of those years, I was catching killers.

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Speaker 1: That's what I did for a living. I was a

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Speaker 1: homicide detective. I'm no longer just interviewing bad guys. Instead,

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Speaker 1: I'm taking the public into the world in which I operated.

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Speaker 1: The guests I talk to each week have amazing stories

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Speaker 1: from all sides of the law. The interviews are raw

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Speaker 1: and honest, just like the people I talk to. Some

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Speaker 1: of the content and language might be confronting. That's because

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Speaker 1: no one who comes into contact with crime is left unchanged.

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Speaker 1: Join me now as I take you into this world.

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Speaker 1: Besides using the phone to record a conversation with a

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Speaker 1: person of interest which resulted in me being charged, I've

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Speaker 1: never been accused of being an early adopter of technology.

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Speaker 1: But the technology landscape is changing rapidly in the form

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Speaker 1: of artificial intelligence, and it's going to impact dramatically on crime.

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Speaker 2: Now.

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Speaker 1: I don't want to be left behind, so that's why

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Speaker 1: I reached out the former detective and guest on this podcast,

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Speaker 1: Graham Simpendorf to have a chat. For the past few years,

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Speaker 1: Graham has made it his business to understand AI and

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Speaker 1: see how it can be used to investigate crime and

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Speaker 1: also prevent crime. Graham is the CEO of Perigon Investigations

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Speaker 1: and specializes in using AI in the fight against crime.

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Speaker 1: Now we can bury our head in the sand and

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Speaker 1: pretend things are not going to change, but they are.

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Speaker 1: Have a listened to what Graham has to say. I

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Speaker 1: think it will become obvious that the utilization of AI

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Speaker 1: is going to be the biggest change in criminal investigation

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Speaker 1: since DNA technology came into play in the early nineties.

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Speaker 1: I found the chat with Graham fascinating and informative. If

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Speaker 1: you don't want to be left behind in this brave

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Speaker 1: new world of AI, have a listen to what he

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Speaker 1: has to say. Graham, thanks for I mean on I

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Speaker 1: catch Killer's.

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Speaker 2: My pleasure, Garran conceiging, well, it is good to see you.

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Speaker 1: But I've got a confession to make them a little

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Speaker 1: bit nervous, because when I talk technology, the amount of

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Speaker 1: information I don't know can become blatantly obvious, and I

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Speaker 1: don't want to come across as a dummy. So I

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Speaker 1: give you full permission to not laugh at my questions,

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Speaker 1: but just explain it in the most simple terms so

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Speaker 1: I can understand it. Because when it comes to technology,

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Speaker 1: I think if I can understand that, other people can

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Speaker 1: understand it exactly right.

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Speaker 2: Yeah, I find the best way I'll explain it to

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Speaker 2: some others, as if I'm just explained to my mum

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Speaker 2: because she still struggle to turn on the computer. Yeah,

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Speaker 2: but I think so many people are in that position.

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Speaker 2: It's changing so rapidly. Everyone's finding a bit hard to

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Speaker 2: get their head around.

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Speaker 1: So yeah, yeah, well it has changed so rapidly, and

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Speaker 1: when we talk rapidly, we're talking every sort of six months.

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Speaker 1: We're having conversations that six months ago you wouldn't even comprehend,

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Speaker 1: have you.

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Speaker 2: Yeah, exactly right. And I think that's probably one of

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Speaker 2: the biggest challenges where we are facing in all parts

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Speaker 2: of society, is how rapidly it's changing for us that

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Speaker 2: are stuck in the middle trying to understand it. But

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Speaker 2: then the kids are the next generation that are picking

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Speaker 2: it up so quick and bringing into their employment already.

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Speaker 2: There's a lot of challenges, but it's also exciting at

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Speaker 2: the same time.

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Speaker 1: Well, I think you need to. I half joke, but

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Speaker 1: because I usually get dragged into technology, I don't embrace

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Speaker 1: change too readily. But I'm certainly aware of a situation enough.

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Speaker 1: Now you've got to get ahead of this or you're

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Speaker 1: going to get left.

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Speaker 2: Behind, exactly right. And look, we've seen a bit of

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Speaker 2: that over our time, both in the cops and just

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Speaker 2: in life, where we've seen all these massive changes introductions

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Speaker 2: of just computers and emails and how business is done

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Speaker 2: electronically tapping your phone to pay that these things were

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Speaker 2: unheard of twenty thirty years ago, so now it's just.

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Speaker 1: Part of that day living. I remember the days, and

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Speaker 1: you might. When I first started, it was manual typewriters,

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Speaker 1: and then when the electronic typewriters came in, there was

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Speaker 1: a couple of old detectives that why would they need

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Speaker 1: an electronic typewriter? And then these stupid things computers came

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Speaker 1: in that would never catch on.

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Speaker 2: That's right. Yeah, it doesn't feel like it was that

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Speaker 2: long ago, and probably in the scheme of things it wasn't.

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Speaker 2: But that highlights I guess how rapid this is changing,

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Speaker 2: and it's another technological change that's going to happen really

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Speaker 2: quick in the next couple of years to five years,

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Speaker 2: and we may look back in ten and almost laugh

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Speaker 2: at some of these days.

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Speaker 1: Yeah, whatether you've been doing since you've been out of

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Speaker 1: the police. You've spent twenty seven years in the Victorian

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Speaker 1: Police working as a detective. How long you've been out

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Speaker 1: and what you've been doing.

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Speaker 2: Yeah, well it was actually four years ago last week,

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Speaker 2: so that's gone really quickly. Took me a couple of

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Speaker 2: years to really find myself again and that identity that

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Speaker 2: we've probably spoken of many times, but I eventually got

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Speaker 2: around to start in my own company, Paragrine Investigations and Consultancy,

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Speaker 2: and in that space where you're looking at risk and

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Speaker 2: security for the private sector, but also some private investigations

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Speaker 2: on that higher end scale of finding people, of finding

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Speaker 2: people that don't want to be found. Thanks to some

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Speaker 2: previous stuff with obviously Channel ten.

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Speaker 1: And just for the listeners that don't know that, I'm

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Speaker 1: also sitting opposite a TV star in part because you're

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Speaker 1: on the After you left The Left the Cops, you

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Speaker 1: were on the show called the Hunted.

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Speaker 2: Yeah, that's right. Hunted came in to our screens a

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Speaker 2: couple of years ago now and that's what led me

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Speaker 2: to part of this work. So the people I met

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Speaker 2: through Hunted as now who I work with and collaborate

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Speaker 2: with in the private sector. So it's amazing how these

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Speaker 2: doors open up.

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Speaker 1: And the premise of that show was someone just citizens.

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Speaker 1: It was a reality TV show, but was based on

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Speaker 1: the ability of law enforcement to track down people that

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Speaker 1: were on the runt.

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Speaker 2: Yeah, well we did just about day and day out

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Speaker 2: in the job. But here regular citizens thought they'd give

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Speaker 2: it a go, try and avoid us, and they had

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Speaker 2: twenty one days to try and avoid the team of

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Speaker 2: hunters for the reality show and a lot of fun.

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Speaker 2: But just amazing.

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Speaker 1: Did that upscale you like what was available? People often

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Speaker 1: assume that law enforcement is a cutting edge, but you know,

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Speaker 1: when technology changes, sometimes law enforcements a little bit.

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Speaker 2: It sure did. The team that was put together in

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Speaker 2: the cyber aspect, So everything that was to do with

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Speaker 2: cyber whether it was online, open source, dark web, spoofing

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Speaker 2: on different machines and trying to pretend or trick the

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Speaker 2: people at the other end as to who they were.

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Speaker 2: It was a whole new world to me. And like

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Speaker 2: we've just said, I had to learn and learn really

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Speaker 2: quick and that's obviously led me on to now what

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Speaker 2: I do in the private sector with Peregrine.

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Speaker 1: Okay, you've been on the podcast before and we talked

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Speaker 1: about your career and that, but just for people that

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Speaker 1: hadn't listened to that episode, just give us a bit

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Speaker 1: by way of background your policing career when you're in

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Speaker 1: Victoria and the police.

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Speaker 2: Yeah for sure. So just over twenty seven years in

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Speaker 2: the end before I called time on that went through

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Speaker 2: all the ranks as usual, up from working the van

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Speaker 2: in the streets of Melbourne and then finally into the

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Speaker 2: crime squads with a little bit of time at homicide,

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Speaker 2: robbery and sex offense child abuse units. Two thousand and

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Speaker 2: nine made them move up to the bush up to

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Speaker 2: Wodonga up on the border with New South Wales. Ran

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Speaker 2: the detective's office up there. Then about the next ten

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Speaker 2: years through Black sat Day, Black Summer and all the

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Speaker 2: different events we had up there, and that's where I

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Speaker 2: still am. Yeah, I love it.

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Speaker 1: Okay, Well, we won't revisit your police career, but if

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Speaker 1: people want to hear about your police career, you had

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Speaker 1: some fascinating cases and a lot of stories to tell.

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Speaker 1: The career police tend to tend to carry with them.

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Speaker 1: Another thing that you are doing is you're very much

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Speaker 1: involved in the advocacy for veteran police. Yeah, what made

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Speaker 1: evaded you for that?

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Speaker 2: I think, as I sort of alluded to before that

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Speaker 2: first couple of years out for me, I was struggling,

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Speaker 2: and it was around those COVID times or post COVID,

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Speaker 2: We're just trying to find your identity again. People would

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Speaker 2: ask what do you do for work? And I struggled

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Speaker 2: to come up with an answer and how I felt

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Speaker 2: because you know, I used to be a police officer

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Speaker 2: or I'm not sure what.

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Speaker 1: It was funny that because that is your identity, and

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Speaker 1: I think that's what a lot of people struggle with

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Speaker 1: because you go through life and regardless of everything else

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Speaker 1: you do or even your life, you're identified as a policeman.

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Speaker 1: That becomes your identity. So I do understand what you're saying.

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Speaker 2: Exactly right, So that you know that journey in that

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Speaker 2: couple of years, which was pretty rough, you know, some

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Speaker 2: highs and lows, but come out the other side eventually.

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Speaker 2: With that, I then saw so many mates and you

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Speaker 2: would too, that are going through the same thing and

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Speaker 2: almost the same issues. Of that local identity, loss of identity,

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Speaker 2: I should say, you know, what's my purpose now? Because

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Speaker 2: your purpose was to serve your community, serve your state,

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Speaker 2: and you had your team and I guess you tribe

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Speaker 2: around you. And when that's gone, who am I now?

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Speaker 2: So I saw so many people struggling with that. And

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Speaker 2: it was around the same time as Police Veterans Victoria

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Speaker 2: kicked off sort of a new initiative to fill that gap,

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Speaker 2: because if you didn't go out on work cover, or

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Speaker 2: if you didn't go out physically or mentally, well, you

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Speaker 2: sort of went into a different bucket of no man's

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Speaker 2: land of just you gone.

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Speaker 1: Yeah, so there's no context.

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Speaker 2: Yeah, So the Police Veterans Victoria was starting. I think

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Speaker 2: a lot of states are doing a similar thing. So

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Speaker 2: start to work with them around helping people that were

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Speaker 2: getting out. So we now either help those that are

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Speaker 2: transitioning out or we help sort of the very senior

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Speaker 2: people in their later stages of life that are struggling,

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Speaker 2: but they could connect with us because we understand, we

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Speaker 2: understand what it was like to work in the job,

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Speaker 2: and we just help them through life and the simple

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Speaker 2: things that they might be struggling with. Finances, or struggling

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Speaker 2: with relationships, and we just just sit down and have

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Speaker 2: a coffee with them and have a chat. So I

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Speaker 2: really enjoy that.

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Speaker 1: I think it's worthwhile in that. And I speak with

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Speaker 1: a lot of military guys, and you know, they spend

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Speaker 1: twelve months going through their basic training and turning them

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Speaker 1: into soldiers and becoming part of the military, and then

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Speaker 1: when they leave, they're just cut off. Same with police.

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Speaker 1: We go through the academy and they're breaking you down

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Speaker 1: and rolling you out as a cop. It would be

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Speaker 1: and I don't think it'll be that expense ive just

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Speaker 1: the last couple of months of anyone's career, if they're

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Speaker 1: going out at their own time, wouldn't it be good

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Speaker 1: if they did a decompression basically before they helped them

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Speaker 1: on the little things that you need to be up after.

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Speaker 2: You've left the police, It's right, all those things about

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Speaker 2: just starting your own business or what's that next step,

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Speaker 2: whether it's another full time role or you start your

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Speaker 2: own business. Just all those little things on life that

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Speaker 2: are outside the job can be a lot for some people.

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Speaker 2: So yeah, it's needed. There's a gap there for sure,

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Speaker 2: and I think there's a lot of good space some

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Speaker 2: programs beyond the badge and the police veterans do do

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Speaker 2: a lot of work, but there's more that can be done,

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Speaker 2: that's for sure.

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Speaker 1: Well good on you for putting some effort in there.

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Speaker 1: And I know in the conversations that I had with

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Speaker 1: you too, you were touched by the murder of the

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Speaker 1: two police officers down in Victoria recently. You knew one

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Speaker 1: of them very well.

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Speaker 2: Yeah, well that's that's the area I worked in, so

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Speaker 2: you know, the Wangaratta areas. You know, we would work

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Speaker 2: with them almost daily, if not weekly, of working alongside

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Speaker 2: some of those guys, and you know, very sad to

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Speaker 2: watch that, and that's part of that, you know, grief

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Speaker 2: process as well that you know had. I had a

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Speaker 2: real sort of personal struggle with I'm out but I

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Speaker 2: want to help, but you have I've had to step

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Speaker 2: away and how can I help in other ways? And

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Speaker 2: it was just sort of a few messages here and

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Speaker 2: there to people say, you know, if you need to

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Speaker 2: have a chat, I'm here, do your best, but look

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Speaker 2: after yourself as well. So you're really close to home

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Speaker 2: that one, and we'll be for obviously a long.

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Speaker 1: Time because that's the type of situation especially in the

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Speaker 1: small community and the rural area. That would hit very hard.

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Speaker 1: Everyone with the people.

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Speaker 2: Yeah, exactly everyone you knew someone and the people that

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Speaker 2: responded to, right from ambulance to police to the community,

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Speaker 2: the pubs and just the local businesses that are affected

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Speaker 2: by that search and still are. Yeah, it's really tough.

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Speaker 2: And once that noise sort of stops and things sort

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Speaker 2: of start to die down a little, that's where the

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Speaker 2: real struggle can really come in, and that's when people's

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Speaker 2: mental well being can care the focus.

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Speaker 1: It's just that stage you're going through the grief and

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Speaker 1: then the focus of dealing with it, and then all

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Speaker 1: of a sudden that goes quiet and you're left to

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Speaker 1: your own thoughts.

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Speaker 2: Yeah, that's a dangerous time. So that's the time I

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Speaker 2: like to step in a little bit more than a

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Speaker 2: bit closer and how you're really going, how you're doing

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Speaker 2: because that noise has stopped.

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Speaker 1: Okay, all right, Well, AI is a topic of discussion today,

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Speaker 1: and quite iron I've used AI to get a description.

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Speaker 2: Of what AI is.

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Speaker 1: So I'm going to read this out and then we'll

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Speaker 1: just for those that don't know AI, this is the

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Speaker 1: way AI describes itself. AI or Artificial intelligence is the

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Speaker 1: ability of computer systems to form tasks that typically require

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Speaker 1: human intelligence, such as learning, problem solving, decision making, and

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Speaker 1: understanding language. It is a field of computer science that

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Speaker 1: uses algorithms and data to enable machines to perceive their environment,

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Speaker 1: learn from experiences, and make decisions or take actions to

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Speaker 1: achieve goals. AI can be found in everyday applications like

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Speaker 1: virtual assistance, recommendation systems and advance web search engines. Okay,

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Speaker 1: that's AI's definition of AI. There's a lot there, and

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Speaker 1: my thinking that's describing what we as a human do

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Speaker 1: with our brain exactly.

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Speaker 2: Yeah, and that's I guess that artificial intelligence piece isn't AI.

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Speaker 2: So yeah, but it's only as good as what develops

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Speaker 2: on itself. So as it sort of alluded to, it's

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Speaker 2: doing large analysis of data or problems that the human

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Speaker 2: brain or the human would take hours and hours and hours,

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Speaker 2: if not days a week to do, and it'll do

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Speaker 2: it rapidly in seconds.

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Speaker 1: Well, using a basic example, if we were talking, if

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Speaker 1: we keep it related to a crime or a court matter,

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Speaker 1: if case law, a particular type of case law and

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Speaker 1: precedence is very heavily relied upon in court case law,

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Speaker 1: what's a statue? What are the proofs of certain offense?

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Speaker 1: All that is readily available on AI, that's right. And

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Speaker 1: before that would be a case of we all had

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Speaker 1: those old law books that we'd keep in our office

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Speaker 1: and have the actives looking through case law trying to

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Speaker 1: work out what way to approach an investigation or present

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Speaker 1: a matter at court. It's incredible what's readily available the fingertips.

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Speaker 2: That's right, and it's so reallyvailable. But then it's a

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Speaker 2: matter of also then interpreting that to your case or

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Speaker 2: your particular set of circumstances. So yeah, it's great to

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Speaker 2: be able to draw that data in from so many

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Speaker 2: volumes of the back walls or fill with those books

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Speaker 2: that we talked of, where would you go to find that?

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Speaker 2: Whereas the AI can take you straight to the piece that

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Speaker 2: you need, that piece of legislation or the case law,

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Speaker 2: and then you, as a human element, can apply that

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Speaker 2: to your case or your situation. So it just saves

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Speaker 2: you so much time. And I actually swear by now

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Speaker 2: and obviously now that's part of my world.

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Speaker 1: Well, like going through the detect this course, and I

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Speaker 1: would imagine it would have been very similar for you.

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Speaker 1: A lot of it was learning virtually parrot form about

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Speaker 1: the Crimes Act. Yeah, the proofs of the fence, that

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Speaker 1: type of thing just drummed into you, so you knew

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Speaker 1: what it was. Now that's readily available.

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Speaker 2: Yeah, no excuse now to miss an element of a

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Speaker 2: charge or a point of proof to prove a case.

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Speaker 2: It's all there, ready to go. And I guess that's

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Speaker 2: part of where AI could be used to assist going

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Speaker 2: forward in that next step of what does this look

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Speaker 2: like for investigators?

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Speaker 1: So when we talk AI like, I think you can

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Speaker 1: have the conversation on the street now on ninety percent

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Speaker 1: that people know what you're talking about and acknowledge their

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Speaker 1: views in some form. But when has that sort of

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Speaker 1: come come in the favor.

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Speaker 2: I think on the last couple of years it really

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Speaker 2: everyone sort of embraced the idea because it's been everywhere,

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Speaker 2: whether it's you know, your chat GPT to help draft

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Speaker 2: things or research things. We've seen it within the school.

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Speaker 2: I don't think my sixteen year old daughter will use

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Speaker 2: chat GPT from how and again for some stuff, But

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Speaker 2: you know it's I think we've really sent the last

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Speaker 2: couple of years and people are starting to obviously lean

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Speaker 2: into that a bit more and scratch a bit deeper

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Speaker 2: than just saying, oh, AI AI is everything. AI can

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Speaker 2: be everything, but it actually you need to look at

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Speaker 2: what is the problem or what is issue I'm looking

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Speaker 2: to solve here? What do I want AI for? That's

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Speaker 2: probably the key point, because you know, you can't just say,

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Speaker 2: let's just great AI into investigations. Well, what part of

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Speaker 2: an investigation do you want to look at how it's

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Speaker 2: come into play? Yes, So that's the broader question that

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Speaker 2: needs a lot more thought, And there's so many elements

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Speaker 2: to it, Yeah, which I'm sure we'll go through.

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Speaker 1: But it's someone bought up and that it was going

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Speaker 1: back probably twelve months or so, and it was someone

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Speaker 1: sitting here that we're talking about how AI could be used,

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Speaker 1: and there was a case where the courts rejected it

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Speaker 1: because you know, and I can't remember the specific offense,

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Speaker 1: but if a police officer had reasonable cause to stop

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Speaker 1: and detain a person police officers, So what was acting

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Speaker 1: on my mind at the time, And someone answered that

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Speaker 1: I did because it wasn't facial identification, but something came

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Speaker 1: up from a computer system, so the police officer was

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Speaker 1: being cross examined by the barrister breaking it down so

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Speaker 1: you didn't have reasonable cause and break it down to

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Speaker 1: the point where the officer said, well, actually I didn't,

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Speaker 1: but I had it based on that. Then the matter

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Speaker 1: was was thrown out.

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Speaker 2: Yeah, well yeah, at what point did you form your

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Speaker 2: opinion or your belief? Well, I didn't form.

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Speaker 1: It what was based on Yeah, So chat GPT, I've

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Speaker 1: embraced it in the past, i'd say twelve months. But

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Speaker 1: explain to people what that is.

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Speaker 2: Yeah, well chat GPT is I guess, a whole large

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Speaker 2: system of data that's worldwide, but it is. It's not Australian,

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Speaker 2: so it's obviously has a bias towards that Western world,

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Speaker 2: but not particularly Australia. But you can ask it anything.

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Speaker 2: You can ask it to write you a script for

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Speaker 2: a book or a movie, right you write you a novel,

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Speaker 2: write you emails, or ask about the pyramids. And it's

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Speaker 2: just a large data set of all the information basically

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Speaker 2: that draws off the wear or whatever data is actually

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Speaker 2: put into it. So yeah, it's very broad, but you

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Speaker 2: can just about ask it to do anything or produce anything,

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Speaker 2: produce a photo, produce a recording and anything. So it's

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Speaker 2: the sky's limit.

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Speaker 1: With my reluctance to use it, and I pay homage

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Speaker 1: to my son, and he dragged me into the world

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Speaker 1: about using chat GPT. I argue that it feels like

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Speaker 1: it's cheating, like it was to do a report, like

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Speaker 1: a report on something. It might be a report to

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Speaker 1: a council about something. He's gone, you can do that

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Speaker 1: on chat GPT and what are you talking about, Like,

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Speaker 1: I know how to do reports, and he posed a

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Speaker 1: hypothetical question. We put it to chat GPT and I'm

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Speaker 1: reading it. Jesus, that's done it better than I could.

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Speaker 1: And my reluctance was so I argued, back, game, but

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Speaker 1: that's cheating, Like I want to do it. I'm capable

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Speaker 1: of doing it. And he said he said these words

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Speaker 1: to me, and it sort of resonated with me and

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Speaker 1: changed my thinking on it. He said, when you went

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Speaker 1: to university and did you degree, well, you were relying

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Speaker 1: on textbooks to get information. And then as it evolved,

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Speaker 1: will you relying on the internet to get information to

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Speaker 1: help with it? Well, this is just the next step.

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Speaker 1: It's evolved. So you're not going to embrace technology. You

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Speaker 1: stick with your textbook stat you'll go really really good

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Speaker 1: if you can find the many Moore. That is how

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Speaker 1: it's evolved, isn't. It's something that we've got to embrace.

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Speaker 1: There's no ethical or moral issue against using the available technology.

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Speaker 2: No, I don't think so, and I think it's time

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Speaker 2: saving for me. I embrace it almost every day. And

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Speaker 2: as you said, it's writing either report or writing an email.

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Speaker 2: It could be absolutely anything, but it's that time saving

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Speaker 2: of yes, you can write it, but it spits it

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Speaker 2: out to you in a format that you can just

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Speaker 2: about cut and paste into your email or your document,

410
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Speaker 2: but you still fact check it. I think past some

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Speaker 2: of those issues where people have been caught out is

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Speaker 2: these large reports that might be a couple hundred pages.

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Speaker 2: You've got to have that human lens across it to

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Speaker 2: check it and put in your own style. So I

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Speaker 2: know how I like to write and draft things. And

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Speaker 2: it depends on who your audience is. If you know

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Speaker 2: that person, so you add your personal lens, or if

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Speaker 2: it's to counsel, then you add your facts. But you're

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Speaker 2: just fact checking and it just saves so much time.

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Speaker 2: So that report that you probably would have written yourself

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Speaker 2: may have taken thirty minutes. To an hour or so

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Speaker 2: to really your.

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Speaker 1: Head round the structure, because that's that's the slow part

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Speaker 1: of once I've got the structure in my head or

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Speaker 1: I'm seeing it on paper. Yeah, that's why I've got

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Speaker 1: to address and that that's what that provided I've had,

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Speaker 1: I've used it and tested it. I was looking at

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Speaker 1: a particular investigation and looking at the people involved, and

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Speaker 1: it came back with the wrong names, like I knew

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Speaker 1: the investigator involved or the detective involved, and I asked

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Speaker 1: the question that came back and I corrected and said,

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Speaker 1: well that person wasn't even involved in that case. I

433
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Speaker 1: got back up. We're sorry, we'll correct that. So there

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Speaker 1: are failings of it.

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Speaker 2: Yeah, for sure. Yeah, and that's the I guess my

436
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Speaker 2: main point to those I talked about this, Yeah, you

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Speaker 2: must fact check it. You must look at it and

438
00:20:44,800 --> 00:20:46,359
Speaker 2: have a good read of what it is. So you

439
00:20:46,400 --> 00:20:49,440
Speaker 2: can't just cnut paste and send. You'll get caught out.

440
00:20:49,480 --> 00:20:52,120
Speaker 2: And I think there's already been some pretty large organizations

441
00:20:52,119 --> 00:20:55,600
Speaker 2: in that space. We won't talk about that, but yeah,

442
00:20:55,760 --> 00:20:59,200
Speaker 2: it's a tool, and I think if the more efficient

443
00:20:59,240 --> 00:21:02,240
Speaker 2: and effective you can using that tool, it just adds value.

444
00:21:02,359 --> 00:21:04,800
Speaker 1: That's what I'm finding I think you talk about people

445
00:21:04,800 --> 00:21:07,359
Speaker 1: getting called out. I think there was a barrister that

446
00:21:07,920 --> 00:21:14,760
Speaker 1: was closing submissions and citing and it was identified that

447
00:21:14,840 --> 00:21:18,800
Speaker 1: those cases don't even exist. So clearly he or she

448
00:21:19,240 --> 00:21:24,960
Speaker 1: embraced that using that AI to present a closing argument,

449
00:21:24,960 --> 00:21:27,080
Speaker 1: which I'm sure it could do very well. But you

450
00:21:27,160 --> 00:21:29,280
Speaker 1: do need to fact check over it, don't you You do?

451
00:21:29,359 --> 00:21:32,199
Speaker 2: Yeah, that's right, and otherwise are very embarrassing if not

452
00:21:32,480 --> 00:21:36,920
Speaker 2: the implications there. Yeah, that's pretty embarrassing professionally, but yeah,

453
00:21:36,920 --> 00:21:38,560
Speaker 2: you got a fact check. You've got to look at

454
00:21:38,760 --> 00:21:41,200
Speaker 2: what it is. But for me, it's about saving that time.

455
00:21:41,240 --> 00:21:43,840
Speaker 2: It's not having to worry about the structure. Yeah, particularly

456
00:21:43,880 --> 00:21:47,479
Speaker 2: for the larger pieces. So yeah, it's a great benefit

457
00:21:47,520 --> 00:21:48,240
Speaker 2: to me day to day.

458
00:21:48,800 --> 00:21:52,240
Speaker 1: Looking at let's pick one aspect of AI, how it

459
00:21:52,240 --> 00:21:56,439
Speaker 1: could be used in criminal investigations. A large part of

460
00:21:56,440 --> 00:22:01,600
Speaker 1: our work as police officers were locating. Invariably you're looking

461
00:22:01,600 --> 00:22:03,800
Speaker 1: for the offender, it could be the victim you're looking for,

462
00:22:03,880 --> 00:22:08,919
Speaker 1: or even witnesses. Facial identification. Where are we at with that?

463
00:22:09,640 --> 00:22:14,080
Speaker 2: Yeah? So facial recognition technology or FRT well advanced globally,

464
00:22:14,640 --> 00:22:17,520
Speaker 2: we're a bit behind the eight ball here in Australia

465
00:22:17,560 --> 00:22:19,880
Speaker 2: and we're finding a bit of that is the case

466
00:22:19,920 --> 00:22:24,400
Speaker 2: across the park. But it's well advanced. It's actually really

467
00:22:24,440 --> 00:22:26,879
Speaker 2: accurate these days, but it's as only as good as

468
00:22:26,920 --> 00:22:29,760
Speaker 2: what you can put into it. So again, like we've experienced,

469
00:22:29,760 --> 00:22:33,200
Speaker 2: you get your CCTV back of your drive by shooting

470
00:22:33,359 --> 00:22:36,640
Speaker 2: or your people that have run away from committing their murder,

471
00:22:36,920 --> 00:22:40,520
Speaker 2: and you might have some really ordinary footage. That's the

472
00:22:40,520 --> 00:22:42,720
Speaker 2: point where I can hopefully step up me to clean

473
00:22:42,760 --> 00:22:46,440
Speaker 2: it up, and then I guess run that facial recognition

474
00:22:46,480 --> 00:22:50,240
Speaker 2: technology across data sets that are already available, and that's

475
00:22:50,240 --> 00:22:53,480
Speaker 2: the gap that is needed. So you remember in the

476
00:22:53,560 --> 00:22:55,960
Speaker 2: days you'd actually to put your circular out across around

477
00:22:56,000 --> 00:22:56,439
Speaker 2: the police.

478
00:22:57,480 --> 00:23:00,240
Speaker 1: Came up in the front over the front cat owner

479
00:23:00,280 --> 00:23:02,400
Speaker 1: of the police stations, in the detective's office, and.

480
00:23:02,359 --> 00:23:04,879
Speaker 2: I think if I never had one result, because it

481
00:23:04,960 --> 00:23:08,760
Speaker 2: was fresh in that that investigator's mind, I put that guy, yeah,

482
00:23:09,000 --> 00:23:11,160
Speaker 2: in last week, that's this fellow. But out of twenty

483
00:23:11,200 --> 00:23:14,720
Speaker 2: seven years, that's one. So asking people to do that internally,

484
00:23:14,760 --> 00:23:16,880
Speaker 2: but then we've seen the amount of times you would

485
00:23:16,920 --> 00:23:20,240
Speaker 2: then ask the public because it's your last resort we

486
00:23:20,280 --> 00:23:23,120
Speaker 2: need to find this person and putting that face image

487
00:23:23,200 --> 00:23:26,160
Speaker 2: out to the public, and that that has its own

488
00:23:26,160 --> 00:23:28,639
Speaker 2: inherent issues, doesn't it. So you get everyone ringing.

489
00:23:28,400 --> 00:23:30,680
Speaker 1: In the false sightings.

490
00:23:30,920 --> 00:23:33,000
Speaker 2: Yeah, different things. You know, I know that person and

491
00:23:33,040 --> 00:23:36,480
Speaker 2: you end up with another hundred crime stoppers investigation reports

492
00:23:36,520 --> 00:23:37,600
Speaker 2: that you have to go into.

493
00:23:37,720 --> 00:23:40,600
Speaker 1: How accurate it is it? Like, obviously a picture of

494
00:23:40,640 --> 00:23:42,600
Speaker 1: my face if I was seen walking down the main

495
00:23:42,640 --> 00:23:45,800
Speaker 1: street of Sydney, is it that capacity that they could say, oh,

496
00:23:45,880 --> 00:23:48,520
Speaker 1: if you're looking for jubilant, he was in the main

497
00:23:48,560 --> 00:23:49,480
Speaker 1: street of Sydney.

498
00:23:49,920 --> 00:23:53,920
Speaker 2: Hours agoinndred percent Yeah and some and we talked about

499
00:23:53,960 --> 00:23:56,080
Speaker 2: time and saving. It's very accurate. And that's what I

500
00:23:56,119 --> 00:23:58,040
Speaker 2: mean by globally it's there.

501
00:23:58,680 --> 00:24:01,560
Speaker 1: What's it go from? The the measurements between the eyes,

502
00:24:01,600 --> 00:24:05,439
Speaker 1: the type of the noose, everything. Yeah, so many different

503
00:24:05,440 --> 00:24:06,360
Speaker 1: points identification.

504
00:24:06,480 --> 00:24:09,520
Speaker 2: And you come through like you do as you come

505
00:24:09,560 --> 00:24:12,560
Speaker 2: through customs and that's the FRT technology piece. But how

506
00:24:12,600 --> 00:24:15,240
Speaker 2: you then put that into investigations is the tricky bit

507
00:24:15,440 --> 00:24:17,840
Speaker 2: because you need to know if you're going to be

508
00:24:17,880 --> 00:24:21,399
Speaker 2: comparing against other systems, whether it's driver's licenses or your

509
00:24:21,400 --> 00:24:24,920
Speaker 2: corrections photos. They're the data sets you want to compare against.

510
00:24:24,920 --> 00:24:28,280
Speaker 2: But there's the ethical aspects and the privacy concerns that

511
00:24:28,400 --> 00:24:30,919
Speaker 2: go with that. So that's that's probably the piece that

512
00:24:31,359 --> 00:24:34,400
Speaker 2: still needs to be plugged in here in Australia.

513
00:24:34,520 --> 00:24:37,360
Speaker 1: We're going to protect people's private yeah, yeah.

514
00:24:37,240 --> 00:24:39,560
Speaker 2: And that's the issue where if we're using some of

515
00:24:39,560 --> 00:24:42,399
Speaker 2: these systems that are offshore, you're sending an image of

516
00:24:42,440 --> 00:24:44,879
Speaker 2: someone that is one of our citizens that may or

517
00:24:44,960 --> 00:24:48,720
Speaker 2: may not be your accused. We're sending that offshore overseas. Right,

518
00:24:48,760 --> 00:24:50,600
Speaker 2: that's what needs to be more sovereign based, and it

519
00:24:50,640 --> 00:24:53,879
Speaker 2: needs to be developed here for here, for our culture,

520
00:24:53,920 --> 00:24:54,760
Speaker 2: for our country.

521
00:24:55,160 --> 00:24:59,560
Speaker 1: And what's the dangers of that? You said we need

522
00:24:59,600 --> 00:25:03,880
Speaker 1: as a sovereign system and we need police to have

523
00:25:03,920 --> 00:25:06,840
Speaker 1: the proper authority or law enforcement to have the proper authority,

524
00:25:07,119 --> 00:25:09,600
Speaker 1: Like we don't want law enforcement to know every every

525
00:25:09,600 --> 00:25:14,040
Speaker 1: movement you'll make, correct, So what's how does that? How

526
00:25:14,080 --> 00:25:17,040
Speaker 1: does that work? Do law enforcement need their own system

527
00:25:17,119 --> 00:25:19,600
Speaker 1: or what's the how do we protect.

528
00:25:19,440 --> 00:25:21,119
Speaker 2: Or that that? You just led me straight into what

529
00:25:21,160 --> 00:25:28,560
Speaker 2: my business is. So thank you, So I fed the

530
00:25:28,640 --> 00:25:31,280
Speaker 2: music play. Look, there's a number of aspects there. Number

531
00:25:31,320 --> 00:25:34,600
Speaker 2: one's ethics and there are nine principles that the Australia

532
00:25:34,600 --> 00:25:37,439
Speaker 2: and New Zealand Policing Advisory Agency have given to all

533
00:25:37,520 --> 00:25:40,720
Speaker 2: law enforcement across the country. Those nine principles are trying

534
00:25:40,720 --> 00:25:41,840
Speaker 2: to raiw them all off the top of my head,

535
00:25:41,840 --> 00:25:47,119
Speaker 2: but ethics, accountability and so many different finite points that

536
00:25:47,400 --> 00:25:49,879
Speaker 2: exactly what we're talking about protecting the community's interests. But

537
00:25:49,880 --> 00:25:52,919
Speaker 2: there's also the law enforcement site. Community expects us to

538
00:25:53,040 --> 00:25:57,480
Speaker 2: catch your murderers, catch your sexual offenders, so that in

539
00:25:57,480 --> 00:26:00,680
Speaker 2: that regard, it's got to be built in my view

540
00:26:00,840 --> 00:26:03,359
Speaker 2: here for here, because we don't want our data system,

541
00:26:03,400 --> 00:26:06,720
Speaker 2: particularly a government data system, being offshore. We can't raise

542
00:26:06,880 --> 00:26:11,200
Speaker 2: so many issues, doesn't it security? National security, individual security?

543
00:26:11,240 --> 00:26:14,719
Speaker 2: So if it's built here for here, we've got more

544
00:26:14,720 --> 00:26:17,000
Speaker 2: control of it so it can be independently audited. You

545
00:26:17,000 --> 00:26:19,119
Speaker 2: can actually have a conversation with the people that are

546
00:26:19,160 --> 00:26:23,480
Speaker 2: building those large models and develop it for our bias.

547
00:26:23,680 --> 00:26:25,359
Speaker 2: So if we're putting in a bias that has an

548
00:26:25,359 --> 00:26:28,240
Speaker 2: American or a UK lens, then you may be drawing

549
00:26:28,240 --> 00:26:32,359
Speaker 2: in UK case law or looking at American systems or

550
00:26:32,400 --> 00:26:34,679
Speaker 2: American footage. You need to plug it into what is

551
00:26:34,680 --> 00:26:37,399
Speaker 2: Australian and purely Australian, but you need the access to

552
00:26:37,440 --> 00:26:40,520
Speaker 2: those data sets and that's where the nervousness is. So

553
00:26:40,560 --> 00:26:44,840
Speaker 2: I'm already talking with some police forces and accompanied by

554
00:26:44,960 --> 00:26:46,960
Speaker 2: the name of main Code and Dave Lemfords and his

555
00:26:47,000 --> 00:26:50,959
Speaker 2: team at main Code that are building these massive data centers,

556
00:26:51,240 --> 00:26:54,199
Speaker 2: incredible investment. We're talking millions and means of dollars of

557
00:26:54,280 --> 00:26:56,840
Speaker 2: investment to have the data centers here in Australia, which

558
00:26:56,840 --> 00:26:57,800
Speaker 2: is step number one.

559
00:26:58,000 --> 00:27:00,280
Speaker 1: And in the data centers, they'd have all all that

560
00:27:00,320 --> 00:27:05,040
Speaker 1: information captured and to access that it would be by application.

561
00:27:05,480 --> 00:27:08,880
Speaker 1: If we were looking for a person that was wanted,

562
00:27:09,280 --> 00:27:12,320
Speaker 1: we would track their phone, but invariably you needed authority

563
00:27:12,359 --> 00:27:14,879
Speaker 1: to track the phone. You need the approval from the

564
00:27:14,880 --> 00:27:17,160
Speaker 1: core that it needed to be signed off at some level.

565
00:27:17,720 --> 00:27:20,680
Speaker 1: Is that the type of safeguards you're talking about, because

566
00:27:21,240 --> 00:27:23,000
Speaker 1: we don't need to track you with your phone now.

567
00:27:23,040 --> 00:27:25,840
Speaker 1: But if I'm going where is that Graham, I need

568
00:27:25,880 --> 00:27:27,879
Speaker 1: to speak to him about this matter and put it

569
00:27:27,920 --> 00:27:31,640
Speaker 1: out there. Okay, he was here three hours ago, he's

570
00:27:31,680 --> 00:27:33,600
Speaker 1: here right now and we go grab you. Is that

571
00:27:33,640 --> 00:27:37,600
Speaker 1: the type of privacy that you're concerned that could be invaded?

572
00:27:37,680 --> 00:27:39,720
Speaker 2: Yeah, exactly right, and that's why it still needs to

573
00:27:39,720 --> 00:27:42,560
Speaker 2: have a process, which I'm sure the e Safety Commissioner

574
00:27:42,560 --> 00:27:44,400
Speaker 2: is looking at. It still needs to have a process

575
00:27:44,520 --> 00:27:47,080
Speaker 2: of authority that needs to go through, so you need

576
00:27:47,119 --> 00:27:50,000
Speaker 2: an independent order to look at. In our scenario, we're

577
00:27:50,000 --> 00:27:53,639
Speaker 2: looking at independent orders to oversee in fairness to what

578
00:27:53,680 --> 00:27:56,680
Speaker 2: we're trying to build. Not dissimilar to in Victorio's the

579
00:27:56,720 --> 00:27:59,680
Speaker 2: Office of Police Integrity, so the OPI would overlook all.

580
00:28:00,520 --> 00:28:03,520
Speaker 1: We have the equivalent of le here in New South Wales.

581
00:28:03,640 --> 00:28:06,760
Speaker 2: Yeah, So that's the level of trust that people need

582
00:28:06,800 --> 00:28:08,560
Speaker 2: to have in any system that's going to be built.

583
00:28:08,840 --> 00:28:10,480
Speaker 2: It's very difficult to have that trust if you're going

584
00:28:10,480 --> 00:28:12,360
Speaker 2: to build it off Suore. So we build it here

585
00:28:12,520 --> 00:28:17,840
Speaker 2: for here and independently ordered by Australians. But in answer

586
00:28:17,880 --> 00:28:20,200
Speaker 2: to your question, I guess in my view, crimes against

587
00:28:20,240 --> 00:28:23,200
Speaker 2: the person, so your homicides are robberie are.

588
00:28:23,119 --> 00:28:25,320
Speaker 1: The things that we should be able to access it for.

589
00:28:25,480 --> 00:28:28,040
Speaker 2: Yeah, they're the things that I think our community would

590
00:28:28,040 --> 00:28:30,040
Speaker 2: expect us to be able to act on. But you

591
00:28:30,119 --> 00:28:32,000
Speaker 2: ought to be able to plug into these systems. So

592
00:28:32,200 --> 00:28:36,000
Speaker 2: in the scenario, if you're just wandering down circular key

593
00:28:36,080 --> 00:28:38,360
Speaker 2: or wandering down Burke Street, Melbourne. You need to be

594
00:28:38,360 --> 00:28:40,360
Speaker 2: able to plug into those systems and that's where the

595
00:28:40,400 --> 00:28:41,760
Speaker 2: collaboration and partnership needs.

596
00:28:41,840 --> 00:28:46,600
Speaker 1: Are they government systems or because businesses have their CCTV

597
00:28:46,840 --> 00:28:50,160
Speaker 1: the licensed premises, Every business has cameras. There are we

598
00:28:50,280 --> 00:28:54,120
Speaker 1: talking about gathering all that information or only government authorized ones?

599
00:28:54,480 --> 00:28:56,400
Speaker 2: Yeah, that's a great question, and I guess it depends

600
00:28:56,440 --> 00:28:58,760
Speaker 2: on the appetite on how far it goes. So some

601
00:28:58,880 --> 00:29:02,200
Speaker 2: are privately run. Obviously, the City of Melbourne runs the

602
00:29:02,360 --> 00:29:05,680
Speaker 2: Safe City Center cameras in Victoria, but Victoria police already

603
00:29:05,720 --> 00:29:08,520
Speaker 2: have access to that life. Again, it's about doing it smarter.

604
00:29:08,880 --> 00:29:10,880
Speaker 2: So if you are looking for someone that's just committed

605
00:29:10,920 --> 00:29:12,600
Speaker 2: a very serious crime, we need to know where this

606
00:29:12,640 --> 00:29:15,840
Speaker 2: person is now. They've just fired a shot through some

607
00:29:16,000 --> 00:29:19,640
Speaker 2: building in middle of Melbourne. Here's the image, we've got it.

608
00:29:19,640 --> 00:29:22,800
Speaker 2: It's just going to expedite things. So rather than trying

609
00:29:22,840 --> 00:29:25,719
Speaker 2: to look at phone records or a triangulation that can

610
00:29:25,760 --> 00:29:27,840
Speaker 2: be a little bit accurate, if you can plug that

611
00:29:27,920 --> 00:29:31,480
Speaker 2: into that system, it will Using the facial recognition technology,

612
00:29:31,880 --> 00:29:34,280
Speaker 2: it does have the capacity to find them immediately and

613
00:29:34,280 --> 00:29:35,720
Speaker 2: then alert us where that person.

614
00:29:35,560 --> 00:29:38,880
Speaker 1: Is how are this then with people going on the

615
00:29:38,920 --> 00:29:43,400
Speaker 1: run in this people often ask me that you know,

616
00:29:43,680 --> 00:29:47,160
Speaker 1: and you've got the alleged shooting by Desmond Freeman that

617
00:29:47,920 --> 00:29:52,160
Speaker 1: it's been months that they're looking for. But the type

618
00:29:52,160 --> 00:29:54,920
Speaker 1: of technology we're talking about now is only applicable to

619
00:29:54,960 --> 00:29:57,920
Speaker 1: the environment, and if he's in the bush, that we

620
00:29:58,000 --> 00:29:59,959
Speaker 1: haven't got that advantage.

621
00:30:00,160 --> 00:30:01,640
Speaker 2: No, that's right, and that's why I think they're brought

622
00:30:01,680 --> 00:30:03,040
Speaker 2: in and I'm not that close to what I think

623
00:30:03,040 --> 00:30:05,640
Speaker 2: they're brought in thermal imaguring and different things into that area.

624
00:30:05,680 --> 00:30:07,760
Speaker 2: But look, I worked up there for so many years

625
00:30:07,840 --> 00:30:10,000
Speaker 2: and you were doing searches for people that were lost

626
00:30:10,240 --> 00:30:12,400
Speaker 2: and they wanted to be found and we couldn't find them.

627
00:30:12,840 --> 00:30:16,600
Speaker 1: To count it rough and yeah, desolate, it's.

628
00:30:16,280 --> 00:30:19,240
Speaker 2: Just so vast, so's it's a challenging time. But look,

629
00:30:19,280 --> 00:30:22,760
Speaker 2: I'm just so glad that they're continuing that search and

630
00:30:22,760 --> 00:30:24,760
Speaker 2: continue to fight to bring into justice.

631
00:30:24,840 --> 00:30:27,320
Speaker 1: Oh well, they're putting all the resources in. Then he

632
00:30:27,400 --> 00:30:30,120
Speaker 1: needs we need to find out what's happened and where

633
00:30:30,160 --> 00:30:33,520
Speaker 1: he is, where he's still with us or if he's

634
00:30:33,760 --> 00:30:34,200
Speaker 1: if he's not.

635
00:30:34,480 --> 00:30:36,640
Speaker 2: Yeah, but you raise a good point, and people out there,

636
00:30:36,640 --> 00:30:39,880
Speaker 2: would you know, that's my it's my right to aout

637
00:30:39,880 --> 00:30:42,280
Speaker 2: to walk around the city without being tracked by the government.

638
00:30:42,880 --> 00:30:44,840
Speaker 2: I guess the lens that we're I'm trying to put

639
00:30:44,840 --> 00:30:48,240
Speaker 2: on and hopefully we get to the point is the

640
00:30:48,280 --> 00:30:51,120
Speaker 2: systems won't be bothered by you. It's looking for the

641
00:30:51,120 --> 00:30:53,360
Speaker 2: person that's just done the drive by shooting, or committed

642
00:30:53,360 --> 00:30:55,960
Speaker 2: the murder, or committed the sexual assault. And of course

643
00:30:56,040 --> 00:30:59,120
Speaker 2: everyone agreed we want that person court. That's the piece

644
00:30:59,160 --> 00:31:01,840
Speaker 2: that needs to come in here, not tracking everyone single movements,

645
00:31:02,640 --> 00:31:07,480
Speaker 2: and that's the lens of auditing those systems to make

646
00:31:07,520 --> 00:31:10,120
Speaker 2: sure that they're being accessed appropriately. No different to any

647
00:31:10,120 --> 00:31:12,120
Speaker 2: of the systems we've gone through in policing, where you know,

648
00:31:12,120 --> 00:31:14,520
Speaker 2: if you access it for the wrong reason, high chance

649
00:31:14,560 --> 00:31:15,200
Speaker 2: you'll lose your job.

650
00:31:15,960 --> 00:31:18,680
Speaker 1: Are we in a situation like the way AI is

651
00:31:18,760 --> 00:31:24,880
Speaker 1: rapidly evolving? That really sickly people? The government, big brother

652
00:31:25,080 --> 00:31:27,240
Speaker 1: will know where each and every one of us are

653
00:31:27,360 --> 00:31:28,000
Speaker 1: at any time.

654
00:31:28,960 --> 00:31:31,880
Speaker 2: Well, if you're commuted a serious crime, yeah, yeah, so Look,

655
00:31:31,880 --> 00:31:34,280
Speaker 2: it's hard to The more urban we are, the more

656
00:31:34,840 --> 00:31:36,240
Speaker 2: you're going to you're going to go onto.

657
00:31:36,080 --> 00:31:40,959
Speaker 1: Counter I think from yeah, I look at my day today,

658
00:31:41,080 --> 00:31:43,640
Speaker 1: I come here in my phone would be pinging off towers.

659
00:31:43,960 --> 00:31:46,680
Speaker 1: I go and use a credit card at the store.

660
00:31:46,800 --> 00:31:50,360
Speaker 1: They know I'm there. My car goes through down the road.

661
00:31:50,640 --> 00:31:54,800
Speaker 1: There's images of that. It's pretty much where we're at now, isn't.

662
00:31:54,600 --> 00:31:57,040
Speaker 2: It's right, it's there now, and that's where a I

663
00:31:57,160 --> 00:31:59,760
Speaker 2: will expedite that and bring it all together. So like

664
00:31:59,800 --> 00:32:02,440
Speaker 2: a definition at the start was taking large sets of

665
00:32:02,520 --> 00:32:05,720
Speaker 2: data large it's information pulling it all together. There's already

666
00:32:05,760 --> 00:32:09,320
Speaker 2: AI out there that you know, the old case of

667
00:32:09,320 --> 00:32:11,760
Speaker 2: either an armed robber or something, you'll go to twenty

668
00:32:11,800 --> 00:32:14,440
Speaker 2: thirty different places and get all their CCTV. You collect

669
00:32:14,520 --> 00:32:16,680
Speaker 2: it all because you don't want it to get lost.

670
00:32:16,960 --> 00:32:20,480
Speaker 2: Some systems rewright after a week or a month, and

671
00:32:20,520 --> 00:32:21,880
Speaker 2: you don't want to be two months down the track

672
00:32:21,920 --> 00:32:23,680
Speaker 2: and go, oh gosh, they actually dumped the gun in

673
00:32:23,720 --> 00:32:25,480
Speaker 2: a being around the corner.

674
00:32:25,160 --> 00:32:29,200
Speaker 1: You want and timely, and it was time consuming going

675
00:32:29,240 --> 00:32:30,000
Speaker 1: and going through that.

676
00:32:30,160 --> 00:32:32,040
Speaker 2: So there is AI already of al that you can

677
00:32:32,120 --> 00:32:35,480
Speaker 2: get all of those thirty different businesses cameras that might

678
00:32:35,880 --> 00:32:38,840
Speaker 2: Fragument's sake have ten cameras each. There's three hundred cameras

679
00:32:38,880 --> 00:32:41,280
Speaker 2: you've got to go through well and all all of

680
00:32:41,280 --> 00:32:41,640
Speaker 2: that time.

681
00:32:41,840 --> 00:32:45,040
Speaker 1: I remember how time consuming that was. We'd grabbed great,

682
00:32:45,040 --> 00:32:48,760
Speaker 1: we've got footage from ten CCTV cameras, but it would

683
00:32:48,840 --> 00:32:50,560
Speaker 1: take weeks to go through.

684
00:32:50,760 --> 00:32:56,000
Speaker 2: And how do we give that job too or you know,

685
00:32:56,040 --> 00:32:57,720
Speaker 2: you had to go through it all yourself, And that's

686
00:32:57,880 --> 00:33:00,160
Speaker 2: that's the piece, that's the saving of times, so you

687
00:33:00,240 --> 00:33:02,120
Speaker 2: can prevent the next time robbery or of a next

688
00:33:02,120 --> 00:33:05,720
Speaker 2: sexual assault because you're straight onto that person's identity much

689
00:33:05,760 --> 00:33:07,080
Speaker 2: faster than you would have been if you're trying to

690
00:33:07,120 --> 00:33:08,040
Speaker 2: go through it all yourself.

691
00:33:08,240 --> 00:33:12,520
Speaker 1: The argument, and I've heard this argument when we talk fingerprints,

692
00:33:12,560 --> 00:33:17,080
Speaker 1: I hear in DNA when DNA evolved and that, and

693
00:33:17,160 --> 00:33:19,720
Speaker 1: people would go, you know, like there was a suggestion

694
00:33:19,840 --> 00:33:24,040
Speaker 1: that fingerprints and DNA should be taken from everyone because

695
00:33:24,440 --> 00:33:28,240
Speaker 1: people assume we've got fingerprints of everyone. We don't necessarily

696
00:33:28,280 --> 00:33:31,120
Speaker 1: have fingerprints of anyone. They've got to have come before

697
00:33:31,160 --> 00:33:35,960
Speaker 1: the system with DNA, Like why not get everyone's DNA

698
00:33:36,040 --> 00:33:38,560
Speaker 1: on the day that they're born. Why not get everyone's fingerprints?

699
00:33:38,680 --> 00:33:41,880
Speaker 1: Everything like that, And people would often say, well, I've

700
00:33:41,880 --> 00:33:44,360
Speaker 1: got nothing to hide, I don't care if people know.

701
00:33:45,120 --> 00:33:50,080
Speaker 1: I was of that view earlier. Maybe throughout my police career,

702
00:33:50,080 --> 00:33:52,760
Speaker 1: I didn't give it a lot of thought. Since I've

703
00:33:52,920 --> 00:33:56,560
Speaker 1: left the police, and certainly you know the battles I've

704
00:33:56,600 --> 00:34:00,280
Speaker 1: had with court system and taken on Big Brother, it

705
00:34:00,320 --> 00:34:03,479
Speaker 1: does sort of, it does scare me. And so what

706
00:34:03,760 --> 00:34:07,120
Speaker 1: I'm saying that public opinion will Who cares? If you

707
00:34:07,160 --> 00:34:09,560
Speaker 1: don't do nothing wrong, You've got nothing to hide. But

708
00:34:10,239 --> 00:34:13,080
Speaker 1: there is an aspect of an invasion of privacy that

709
00:34:13,120 --> 00:34:14,239
Speaker 1: I'm not not comfortable with.

710
00:34:14,560 --> 00:34:16,600
Speaker 2: I got to agree with you with that. I'm exactly

711
00:34:16,600 --> 00:34:18,879
Speaker 2: the saying through my policing career, if you'm not only wrong,

712
00:34:18,880 --> 00:34:20,359
Speaker 2: what have you got to worry about? Yeah, But now

713
00:34:20,360 --> 00:34:22,000
Speaker 2: on the other side of the fence, I think it's

714
00:34:22,080 --> 00:34:27,239
Speaker 2: really it's more important to me to have that security

715
00:34:27,320 --> 00:34:29,799
Speaker 2: that number one, you know, it's my information, it's my

716
00:34:29,920 --> 00:34:32,120
Speaker 2: DNA to want it out there and just say taking

717
00:34:32,120 --> 00:34:35,560
Speaker 2: on Big Brother, and we've seen errors in cases through DNA,

718
00:34:35,640 --> 00:34:39,440
Speaker 2: We've seen errors in criminal cases. So it's important to

719
00:34:39,480 --> 00:34:42,840
Speaker 2: have the integrity of these systems built right from the outset.

720
00:34:42,960 --> 00:34:45,400
Speaker 2: If we're looking at AI, how it's built right.

721
00:34:45,800 --> 00:34:48,399
Speaker 1: Well, it's interesting and that's been the theme of your

722
00:34:48,480 --> 00:34:51,440
Speaker 1: discussion is about the integrity and the ethics involved in it,

723
00:34:51,520 --> 00:34:53,839
Speaker 1: because it is something that could be abused. I think

724
00:34:54,719 --> 00:34:58,920
Speaker 1: a lot of us felt aggrieved by the during the

725
00:34:58,960 --> 00:35:02,319
Speaker 1: COVID times where everywhere you had the g you had

726
00:35:02,320 --> 00:35:05,359
Speaker 1: the scan in, and that that frustrated me. I didn't

727
00:35:05,480 --> 00:35:09,120
Speaker 1: like that because police or the government was saying, look,

728
00:35:09,440 --> 00:35:11,480
Speaker 1: will not be used for anything other than tracking the

729
00:35:11,560 --> 00:35:15,680
Speaker 1: COVID case, I'd say bullshit, because because I know if

730
00:35:15,680 --> 00:35:18,160
Speaker 1: there was a murder, yeah, I would get access to

731
00:35:18,200 --> 00:35:21,960
Speaker 1: that information. Where if I was looking for you during CAVID,

732
00:35:21,960 --> 00:35:24,560
Speaker 1: then you'd scanned into such and such a place, we'd

733
00:35:24,560 --> 00:35:25,400
Speaker 1: get that information.

734
00:35:25,520 --> 00:35:27,360
Speaker 2: Yeah. Well we had an example of that on the border.

735
00:35:27,480 --> 00:35:29,160
Speaker 2: So we're on the border with New South Wales which

736
00:35:29,200 --> 00:35:32,960
Speaker 2: is obviously closed, and we had a shooting in central Wodonga.

737
00:35:33,280 --> 00:35:35,080
Speaker 2: We got the car really quickly because it was not

738
00:35:35,160 --> 00:35:37,480
Speaker 2: much footage to go through. When we went, oh, we

739
00:35:37,560 --> 00:35:39,879
Speaker 2: know what that car is, We put that information out

740
00:35:39,920 --> 00:35:42,880
Speaker 2: to the checkpoint, bang got him ten minutes after the shooting.

741
00:35:43,560 --> 00:35:46,359
Speaker 2: Different to scanning in somewhere. But big Brother was there

742
00:35:46,440 --> 00:35:49,200
Speaker 2: the whole time, and I was very anxious about that

743
00:35:49,200 --> 00:35:51,640
Speaker 2: that whole time, and I didn't enjoy the job at

744
00:35:51,640 --> 00:35:53,120
Speaker 2: that time. I think a lot of cops went, you know,

745
00:35:53,120 --> 00:35:54,000
Speaker 2: I didn't sign up for.

746
00:35:53,920 --> 00:35:56,799
Speaker 1: This well down in Victoria and I saw it. I

747
00:35:57,080 --> 00:36:00,440
Speaker 1: just got out of the police before the lockdowns happened.

748
00:36:00,480 --> 00:36:03,160
Speaker 1: And yeah, I wouldn't have liked to be the police

749
00:36:03,200 --> 00:36:06,880
Speaker 1: officer in those times. I think we were forced to

750
00:36:07,000 --> 00:36:08,799
Speaker 1: enforce the law, which is our job and what we

751
00:36:08,880 --> 00:36:11,279
Speaker 1: sign up to, but I don't think our hearts would

752
00:36:11,280 --> 00:36:14,200
Speaker 1: have been in a lot of it. In the nine pm.

753
00:36:13,960 --> 00:36:18,440
Speaker 2: Curfews, curfews trying to stop kids from playing in parks

754
00:36:18,480 --> 00:36:21,000
Speaker 2: and oh my god, don't.

755
00:36:20,840 --> 00:36:22,279
Speaker 1: Go the end of the beach. All right, Well, we

756
00:36:22,320 --> 00:36:24,200
Speaker 1: could do a whole nother podcast with you and I

757
00:36:24,280 --> 00:36:28,400
Speaker 1: winding about that. But yeah, it was that just to me,

758
00:36:28,560 --> 00:36:30,319
Speaker 1: it showed how power corrupted it is.

759
00:36:30,600 --> 00:36:33,960
Speaker 2: It's a really important piece of the discussion on development

760
00:36:34,000 --> 00:36:36,440
Speaker 2: of AI and what does it mean. And that's again

761
00:36:36,680 --> 00:36:38,359
Speaker 2: and so to go on about that's why it needs

762
00:36:38,360 --> 00:36:40,520
Speaker 2: to be done here so we can control them. But

763
00:36:41,280 --> 00:36:43,240
Speaker 2: and that was part of my passion of getting involved

764
00:36:43,239 --> 00:36:44,839
Speaker 2: in the first place, because I could see a real

765
00:36:44,920 --> 00:36:46,560
Speaker 2: need for it. But I can actually see how this

766
00:36:46,600 --> 00:36:51,680
Speaker 2: can benefit saving so much time for investigators quickly identifying

767
00:36:51,680 --> 00:36:54,640
Speaker 2: offenders to stop that second offense, but also help in

768
00:36:54,640 --> 00:36:58,080
Speaker 2: the court process. You know, we've got horrendous weights and

769
00:36:58,120 --> 00:37:01,440
Speaker 2: delays through court the lawyer piece on the other side

770
00:37:01,760 --> 00:37:04,560
Speaker 2: for them to understand all the data information. There is

771
00:37:04,600 --> 00:37:06,239
Speaker 2: a real space for it, but it's got to be

772
00:37:06,280 --> 00:37:10,800
Speaker 2: developed ethically, morally, and I feel what better person to,

773
00:37:11,200 --> 00:37:13,040
Speaker 2: I guess, develop it than someone that's been through it

774
00:37:13,040 --> 00:37:14,680
Speaker 2: and lived it and knows what's needed.

775
00:37:15,719 --> 00:37:18,920
Speaker 1: Hey, guys, it's Gary jubilin here. Want they get more

776
00:37:18,920 --> 00:37:21,399
Speaker 1: out of VI Catch Killers, then you should head over

777
00:37:21,480 --> 00:37:24,799
Speaker 1: to our new video feed on Spotify where you can

778
00:37:24,880 --> 00:37:28,600
Speaker 1: watch every episode of VI Catch Killers. Just search for

779
00:37:28,680 --> 00:37:32,040
Speaker 1: I Catch Killers video in your Spotify app and start

780
00:37:32,120 --> 00:37:35,960
Speaker 1: watching today. Give me a sense of how it changes

781
00:37:36,000 --> 00:37:39,560
Speaker 1: the way with approach a criminal investigation. And you know

782
00:37:39,600 --> 00:37:41,640
Speaker 1: we're talking you have only left four years ago, so

783
00:37:41,680 --> 00:37:45,440
Speaker 1: it's relatively recent. How do you see the potential for

784
00:37:45,480 --> 00:37:47,680
Speaker 1: it to change criminal investigations.

785
00:37:48,000 --> 00:37:50,440
Speaker 2: Yeah, I'd probably give you a real basic case, so

786
00:37:51,160 --> 00:37:53,160
Speaker 2: you know, a drive by shooting or a murder if

787
00:37:53,200 --> 00:37:55,239
Speaker 2: you will, so to get away car. You get it

788
00:37:55,680 --> 00:37:58,960
Speaker 2: on footage, so you know the initial action is always

789
00:37:59,520 --> 00:38:03,360
Speaker 2: canvassing witnesses chemissing for CCTV, it's everywhere if it's urban.

790
00:38:03,760 --> 00:38:06,399
Speaker 2: So you do get your piece of CCTV that has

791
00:38:06,400 --> 00:38:09,480
Speaker 2: the car driving off at speed away from the shooting.

792
00:38:09,600 --> 00:38:12,880
Speaker 2: We've sent it on TV a million times and you

793
00:38:12,920 --> 00:38:15,399
Speaker 2: would have sat around the table in the office talking

794
00:38:15,440 --> 00:38:19,600
Speaker 2: about is that a twenty nineteen hold in Commodore vs VX?

795
00:38:20,400 --> 00:38:22,719
Speaker 2: What type of car is that? You need to identify

796
00:38:22,719 --> 00:38:26,520
Speaker 2: exactly what it is. AI will be able to do that,

797
00:38:26,880 --> 00:38:29,080
Speaker 2: and that's something we're looking at with Peregrim has been

798
00:38:29,080 --> 00:38:32,360
Speaker 2: able to build a data set of vehicles. It's a

799
00:38:32,400 --> 00:38:35,680
Speaker 2: known quantity. There's no it's not like a fingerprint where

800
00:38:35,680 --> 00:38:37,960
Speaker 2: everyone has one. There's only so many models of vehicles

801
00:38:37,960 --> 00:38:41,880
Speaker 2: in the world. So an AI program developed for law

802
00:38:41,960 --> 00:38:46,560
Speaker 2: enforcement would quickly identify immediately that is a twenty nineteen

803
00:38:46,600 --> 00:38:50,120
Speaker 2: hold in Commodore, certain model, the rims are changed or

804
00:38:50,160 --> 00:38:53,120
Speaker 2: whatever aspect or whatever unique piece of that car is

805
00:38:53,160 --> 00:38:54,280
Speaker 2: and it's one hundred percent accurate.

806
00:38:54,680 --> 00:38:56,840
Speaker 1: See that it self would be beneficial. And you just

807
00:38:57,360 --> 00:38:59,560
Speaker 1: brought me crashing back into my time in the told

808
00:38:59,600 --> 00:39:02,160
Speaker 1: Up squad when we're always tracking cars and we would

809
00:39:02,200 --> 00:39:05,080
Speaker 1: go to the manufacturer and this is what we've got

810
00:39:05,160 --> 00:39:08,640
Speaker 1: then take a long time to get that and it

811
00:39:08,680 --> 00:39:11,799
Speaker 1: was always opinion based. But you're saying with AI the

812
00:39:11,880 --> 00:39:14,400
Speaker 1: right system bank, this is a car you're looking for.

813
00:39:14,440 --> 00:39:16,800
Speaker 2: Well, it's a fixed data set, isn't it. So you

814
00:39:16,440 --> 00:39:18,799
Speaker 2: know if you're loading in every single car that's ever

815
00:39:18,800 --> 00:39:21,640
Speaker 2: been manufactured, it'll find it, and it'll find it quickly.

816
00:39:21,719 --> 00:39:24,680
Speaker 2: So we know the power of knowing that piece of

817
00:39:24,719 --> 00:39:28,480
Speaker 2: information investigation as early as possible is critical. And then

818
00:39:28,520 --> 00:39:31,400
Speaker 2: we talked before about if you did not know that

819
00:39:31,520 --> 00:39:33,920
Speaker 2: vehicle and you go to the media or the public

820
00:39:33,920 --> 00:39:36,719
Speaker 2: about can you help us find this car or we're

821
00:39:36,760 --> 00:39:38,680
Speaker 2: looking for this particular car, and if you get it wrong,

822
00:39:39,200 --> 00:39:40,080
Speaker 2: you can' undo that.

823
00:39:40,360 --> 00:39:43,279
Speaker 1: No, Well that was you had to hold back sometimes too,

824
00:39:43,320 --> 00:39:46,520
Speaker 1: because if you put out the wrong one, okay.

825
00:39:46,320 --> 00:39:48,880
Speaker 2: Well that's a real basic example of and that can

826
00:39:48,920 --> 00:39:51,399
Speaker 2: then change your tactics. So if you've quickly identified the car,

827
00:39:51,800 --> 00:39:53,799
Speaker 2: if you quickly identified what make a model it is,

828
00:39:54,160 --> 00:39:55,920
Speaker 2: you may then be able to look at the identity

829
00:39:55,920 --> 00:39:57,440
Speaker 2: of that vehicle, whether it's the red j O and

830
00:39:58,080 --> 00:40:00,760
Speaker 2: plug it in. But then you can track it across

831
00:40:01,200 --> 00:40:04,040
Speaker 2: where it went from there. So there's a lot that

832
00:40:04,160 --> 00:40:06,480
Speaker 2: can change in that simple example of just that one

833
00:40:06,480 --> 00:40:08,240
Speaker 2: piece of knowledge of knowing the make and model.

834
00:40:08,239 --> 00:40:10,400
Speaker 1: If you get away car, okay, I can see the

835
00:40:10,440 --> 00:40:14,440
Speaker 1: benefits there. And if we can get and the old adages,

836
00:40:14,560 --> 00:40:16,879
Speaker 1: get the police out on the streets where we need them.

837
00:40:16,880 --> 00:40:19,879
Speaker 1: And I see in Victoria, I heard them say that

838
00:40:20,760 --> 00:40:22,600
Speaker 1: they're bringing in a policy where they're going to have

839
00:40:22,680 --> 00:40:25,360
Speaker 1: more public servants doing work than police have done. The

840
00:40:25,400 --> 00:40:27,759
Speaker 1: free up the police because they're struggling with I think

841
00:40:27,800 --> 00:40:31,680
Speaker 1: that's a good idea. Maybe if we embrace AI in

842
00:40:31,760 --> 00:40:35,680
Speaker 1: law enforcement and don't quite often I found in law

843
00:40:35,800 --> 00:40:39,640
Speaker 1: enforcement and you would have seen this too. When technology

844
00:40:39,719 --> 00:40:43,239
Speaker 1: changes invariably we wait a little bit. Yes, then we're

845
00:40:43,239 --> 00:40:46,560
Speaker 1: reaching out to the experts outside the police. Can you

846
00:40:46,560 --> 00:40:48,560
Speaker 1: give us a hand? It'd be good if police got

847
00:40:48,640 --> 00:40:50,400
Speaker 1: on the front foot, law enforcement got them on the

848
00:40:50,400 --> 00:40:50,839
Speaker 1: front foot.

849
00:40:51,160 --> 00:40:54,160
Speaker 2: Yeah. Yeah, And that's I think the trigger point at

850
00:40:54,200 --> 00:40:57,840
Speaker 2: the moment is without telling government or police and what

851
00:40:57,880 --> 00:40:59,960
Speaker 2: they need to do. Now I lived on the other side,

852
00:41:00,480 --> 00:41:02,600
Speaker 2: you can't do it on your own one funding, it's

853
00:41:02,640 --> 00:41:05,360
Speaker 2: not cheap. And show me a government that's got millions

854
00:41:05,400 --> 00:41:07,920
Speaker 2: of dollars to invest in AI for their own system.

855
00:41:08,200 --> 00:41:11,439
Speaker 2: I've been at a few conferences lately around the frustrations

856
00:41:11,480 --> 00:41:13,759
Speaker 2: that are shared by very senior police saying when is

857
00:41:13,800 --> 00:41:16,920
Speaker 2: the corporate we going to help because it's needed, and

858
00:41:17,239 --> 00:41:20,360
Speaker 2: that's I guess where we come in. And it's certainly

859
00:41:20,400 --> 00:41:22,960
Speaker 2: not cheap. You know, if there's investors there that want

860
00:41:22,960 --> 00:41:24,400
Speaker 2: to come on board and kive me your hand, that'd

861
00:41:24,400 --> 00:41:26,200
Speaker 2: be great, but you've got to align yourself with the

862
00:41:26,280 --> 00:41:28,879
Speaker 2: right people. This isn't about profit. It's about building something

863
00:41:28,920 --> 00:41:30,840
Speaker 2: that will work for our community, to make it safer

864
00:41:31,320 --> 00:41:35,040
Speaker 2: and as you say, make the investigators time more out

865
00:41:35,080 --> 00:41:37,760
Speaker 2: on the road rather than behind a computer putting briefs

866
00:41:37,800 --> 00:41:41,520
Speaker 2: of evidence together, going through CCTV, trying to identify a vehicle,

867
00:41:41,840 --> 00:41:44,560
Speaker 2: or going through phone records, even those things, you know,

868
00:41:44,719 --> 00:41:47,040
Speaker 2: hours and hours and hours at work to work through that.

869
00:41:47,080 --> 00:41:49,520
Speaker 2: People think we have that already, the amount of conversations

870
00:41:49,520 --> 00:41:53,239
Speaker 2: I have with corpus that don't the cops have this? Sorry, No,

871
00:41:53,520 --> 00:41:57,240
Speaker 2: it's you know, it's funded through state governments and federal governments.

872
00:41:57,360 --> 00:42:00,480
Speaker 2: It's a real challenge. So there are some partnership out there.

873
00:42:00,480 --> 00:42:03,319
Speaker 2: I know monash Uni and Australian Federal Belief have an

874
00:42:03,320 --> 00:42:09,040
Speaker 2: amazing program through the ALEX Labin Artificial intelligence for law

875
00:42:09,120 --> 00:42:11,520
Speaker 2: enforcement community safety. They do an amazing amount of work

876
00:42:11,520 --> 00:42:15,160
Speaker 2: around the trail exploitation and trailed sexual material online. They're

877
00:42:15,280 --> 00:42:17,879
Speaker 2: using AI to help with that, which is fantastic because

878
00:42:17,880 --> 00:42:20,520
Speaker 2: it protects our kids. But there's more pieces if you're done,

879
00:42:20,719 --> 00:42:22,440
Speaker 2: and that sort of circles all the way around to

880
00:42:23,480 --> 00:42:25,399
Speaker 2: AI can be anything. But you've got to find out

881
00:42:25,400 --> 00:42:26,839
Speaker 2: what is the problem you want to solve. And it's

882
00:42:26,880 --> 00:42:28,960
Speaker 2: the same in law enforcement. You've got to pick what's

883
00:42:29,000 --> 00:42:31,680
Speaker 2: going to be the most effective and efficient way to

884
00:42:31,719 --> 00:42:34,759
Speaker 2: probably save time, which is cost of cops out on

885
00:42:34,800 --> 00:42:38,160
Speaker 2: the street, and understand why they're employing public servants because

886
00:42:38,400 --> 00:42:40,440
Speaker 2: the endgame is you want to have more visible.

887
00:42:40,120 --> 00:42:42,960
Speaker 1: Presence and at the risk of losing listeners because it's

888
00:42:43,000 --> 00:42:45,120
Speaker 1: so boring and I know you would have found it boring.

889
00:42:45,160 --> 00:42:48,840
Speaker 1: But the admin side of policing, even to the point

890
00:42:48,880 --> 00:42:52,160
Speaker 1: of rostering, how much time that would take up. Now,

891
00:42:52,280 --> 00:42:55,239
Speaker 1: AI like chat GPT could work out the roster so

892
00:42:55,880 --> 00:43:01,399
Speaker 1: simply so we could three out administrative things like that.

893
00:43:01,440 --> 00:43:04,439
Speaker 2: For sure, And and the cost in labor alone huge.

894
00:43:04,520 --> 00:43:06,680
Speaker 2: There's there's you know, I'd hate to think how many

895
00:43:06,680 --> 00:43:08,080
Speaker 2: cops are back out on the street.

896
00:43:08,400 --> 00:43:12,040
Speaker 1: Well, handling of exhibits, tracking, and I know and I

897
00:43:12,080 --> 00:43:14,600
Speaker 1: assume Victoria would have been the same that New South Wales.

898
00:43:14,840 --> 00:43:17,560
Speaker 1: We upskilled on that because gone were the days of

899
00:43:17,560 --> 00:43:21,480
Speaker 1: the old exhibit book And where is that missing exhibit type? Oh,

900
00:43:21,560 --> 00:43:24,440
Speaker 1: that's right, it was in Grahams that's right.

901
00:43:25,520 --> 00:43:25,759
Speaker 2: Yeah.

902
00:43:26,160 --> 00:43:29,440
Speaker 1: So all that should in fact free up police to

903
00:43:29,480 --> 00:43:32,840
Speaker 1: do what police should be doing, and that's enforcing the

904
00:43:32,880 --> 00:43:35,720
Speaker 1: law and investigating crimes when they'd occurred.

905
00:43:35,880 --> 00:43:38,719
Speaker 2: Yeah, exactly, And that's that's the point. I guess that's

906
00:43:38,760 --> 00:43:41,560
Speaker 2: where we are at, this rural juncture of do we

907
00:43:41,680 --> 00:43:43,560
Speaker 2: lean into it, do we trust it? And you've got

908
00:43:43,560 --> 00:43:46,560
Speaker 2: to have the confidence and partner with the corporate to

909
00:43:46,800 --> 00:43:49,400
Speaker 2: trust to build that. And there's a space that law

910
00:43:49,480 --> 00:43:51,719
Speaker 2: enforcement are heavily involved in that for sure, but you've

911
00:43:51,719 --> 00:43:53,920
Speaker 2: got to be able to fund it. And you know

912
00:43:53,960 --> 00:43:54,680
Speaker 2: that's the big piece.

913
00:43:54,680 --> 00:43:57,040
Speaker 1: It's not cheap and you need those checks and balances.

914
00:43:57,080 --> 00:43:59,680
Speaker 1: And like when DNA came in, and I think it

915
00:43:59,760 --> 00:44:02,799
Speaker 1: was early nineties and the first case, it was one

916
00:44:02,800 --> 00:44:06,640
Speaker 1: of the early cases that DNA was used. It confused

917
00:44:06,640 --> 00:44:09,759
Speaker 1: hell Adam the first start, it was a horrific murder

918
00:44:09,800 --> 00:44:12,200
Speaker 1: of an elderly lady in her house and she'd been

919
00:44:12,280 --> 00:44:15,239
Speaker 1: sexually assaulted. And we had a good suspect, and we

920
00:44:15,360 --> 00:44:18,640
Speaker 1: got the suspects DNA and it didn't match, and that

921
00:44:19,440 --> 00:44:23,440
Speaker 1: everything else was pointing this is a person. And eventually

922
00:44:23,719 --> 00:44:26,520
Speaker 1: we got on to another person the DNA and there

923
00:44:26,600 --> 00:44:29,799
Speaker 1: was no admissions from him initially, but the DNA told

924
00:44:29,880 --> 00:44:31,880
Speaker 1: us this is a person you're looking for because seamen

925
00:44:32,120 --> 00:44:35,440
Speaker 1: was left at the crime scene and it blew me

926
00:44:35,480 --> 00:44:39,080
Speaker 1: away in that. Okay, we can charge a murder a

927
00:44:39,160 --> 00:44:43,239
Speaker 1: person with murder based on this information. But that's how

928
00:44:43,400 --> 00:44:44,760
Speaker 1: things evolve, isn't it correct?

929
00:44:44,920 --> 00:44:47,160
Speaker 2: And that's what juries tend to expect. Now to where's

930
00:44:47,160 --> 00:44:50,839
Speaker 2: the forensic evidence, where's the CCTV? How did we ever

931
00:44:50,840 --> 00:44:52,960
Speaker 2: convict anyone before we had all this? We look now

932
00:44:53,040 --> 00:44:55,560
Speaker 2: in twenty twenty five. If the case doesn't have DNA,

933
00:44:55,640 --> 00:44:59,040
Speaker 2: it doesn't have a fingerprint, doesn't have an eyewitness identification.

934
00:44:59,000 --> 00:45:03,200
Speaker 1: And if it's at scientifically based evidence, it's irrefutable. It

935
00:45:03,200 --> 00:45:06,799
Speaker 1: carries a lot more weight with the jury. But we've

936
00:45:06,800 --> 00:45:10,320
Speaker 1: got to be careful because is it Queensland. The laboratories

937
00:45:10,440 --> 00:45:12,760
Speaker 1: it was having a lot of problems and it didn't

938
00:45:12,800 --> 00:45:15,919
Speaker 1: stand up to the scrutiny. That's right, and that could

939
00:45:15,960 --> 00:45:18,279
Speaker 1: be the case where we fall into the AI. Well

940
00:45:18,520 --> 00:45:20,920
Speaker 1: if we don't make sure we have the proper standards

941
00:45:20,920 --> 00:45:21,719
Speaker 1: and regulations.

942
00:45:21,800 --> 00:45:25,000
Speaker 2: That's right, and that's why it's so critical to build purpose,

943
00:45:25,080 --> 00:45:26,160
Speaker 2: purpose built.

944
00:45:27,400 --> 00:45:30,879
Speaker 1: Data analysis and evidence gathering. A lot of the work

945
00:45:30,920 --> 00:45:34,520
Speaker 1: on the complex investigations, particularly when I was in homicide

946
00:45:34,520 --> 00:45:38,880
Speaker 1: and you worked homicide, you are overwhelmed with information that

947
00:45:39,320 --> 00:45:42,799
Speaker 1: comes in, and that is the real struggle. You have

948
00:45:43,040 --> 00:45:46,040
Speaker 1: to just sift through what's important and what's not. You

949
00:45:46,080 --> 00:45:49,239
Speaker 1: get a good analyst and an analyst and the way

950
00:45:49,280 --> 00:45:52,000
Speaker 1: that what I considered the good analyst in my time

951
00:45:52,160 --> 00:45:55,799
Speaker 1: was someone that could retain information, could reach statements and

952
00:45:55,880 --> 00:45:57,879
Speaker 1: pick up little pieces of there was a red car

953
00:45:57,920 --> 00:46:01,399
Speaker 1: scene in this witness's statement. Someone else mentioned the red

954
00:46:01,440 --> 00:46:05,640
Speaker 1: car in that street two weeks ago, These little things AI.

955
00:46:05,960 --> 00:46:08,520
Speaker 1: I could see AI just freeing that up exactly.

956
00:46:08,920 --> 00:46:13,160
Speaker 2: It's solving problems. It's understanding the data and understanding information.

957
00:46:13,760 --> 00:46:16,120
Speaker 2: And if you're plugging all that information into your case

958
00:46:16,160 --> 00:46:18,200
Speaker 2: and into your system, and you want to know whereas

959
00:46:18,200 --> 00:46:20,520
Speaker 2: a red car mentioned every single time, and every witness

960
00:46:20,560 --> 00:46:23,719
Speaker 2: statement or every bit of CCTV footage. It won't only

961
00:46:23,800 --> 00:46:25,799
Speaker 2: just tell you where it is, it can timeline it

962
00:46:25,800 --> 00:46:28,000
Speaker 2: for you, so you know, trying to put the chain

963
00:46:28,040 --> 00:46:30,920
Speaker 2: of events and chain of circumstances of your case together

964
00:46:31,680 --> 00:46:33,919
Speaker 2: when you're definitely tracking someone that's been over a matter

965
00:46:33,960 --> 00:46:35,840
Speaker 2: of weeks in the lead up to something, or post

966
00:46:35,920 --> 00:46:39,160
Speaker 2: conviction or post offense as well, it'll timeline it all

967
00:46:39,200 --> 00:46:41,120
Speaker 2: that for you, which it will do it in seconds,

968
00:46:41,360 --> 00:46:43,399
Speaker 2: but then you've got to validate and then you've got

969
00:46:43,400 --> 00:46:46,080
Speaker 2: to check it. But it's removing those hours and hours

970
00:46:46,120 --> 00:46:49,120
Speaker 2: and hours. And we're not just talking four or five hours.

971
00:46:49,200 --> 00:46:51,680
Speaker 2: I think we've said before. You know, it's it's weeks,

972
00:46:51,840 --> 00:46:53,440
Speaker 2: if not months, of putting a brief together.

973
00:46:53,840 --> 00:46:57,080
Speaker 1: I still remember and that it was a hard time

974
00:46:57,320 --> 00:47:00,680
Speaker 1: in policing. When I say hard, it was worthwhile. But

975
00:47:00,800 --> 00:47:04,120
Speaker 1: it was sitting in an office for about four months

976
00:47:04,120 --> 00:47:07,440
Speaker 1: on my own, going through a broof of evidence, reviewing

977
00:47:07,760 --> 00:47:11,720
Speaker 1: an old homicide three kids. It was a serial killer,

978
00:47:11,880 --> 00:47:15,359
Speaker 1: it was a bowerble one and reading statements that were

979
00:47:15,400 --> 00:47:17,920
Speaker 1: taken in the nineties that weren't entered on computers, so

980
00:47:17,960 --> 00:47:20,040
Speaker 1: you couldn't you couldn't use the computers to find the

981
00:47:20,080 --> 00:47:24,040
Speaker 1: information and literally going through highlighting this and then have

982
00:47:24,600 --> 00:47:27,680
Speaker 1: to keep my head around the whole investigation so I'm

983
00:47:27,680 --> 00:47:30,840
Speaker 1: not missing anything. We didn't have an analyst attached to

984
00:47:30,840 --> 00:47:33,240
Speaker 1: the investigation. In fact, I was the only one working

985
00:47:33,280 --> 00:47:35,520
Speaker 1: on it. So it was literally sitting in a room

986
00:47:35,560 --> 00:47:38,359
Speaker 1: for three to four months making notes and making sure

987
00:47:38,400 --> 00:47:41,160
Speaker 1: I didn't miss any information. And I look back at

988
00:47:41,160 --> 00:47:43,360
Speaker 1: something like that now, and that was it was my

989
00:47:43,880 --> 00:47:45,480
Speaker 1: When I say mind, I mean it was just.

990
00:47:45,480 --> 00:47:47,959
Speaker 2: Hard work hard. That's a hard slog.

991
00:47:48,040 --> 00:47:51,759
Speaker 1: And that could be literally done in a matter of minutes.

992
00:47:51,480 --> 00:47:54,000
Speaker 2: Correct one hundred percent can and actually then trying to

993
00:47:54,000 --> 00:47:56,440
Speaker 2: identify where your gaps are or where you need to

994
00:47:56,440 --> 00:47:58,960
Speaker 2: be if you can train it to think like an investigator.

995
00:47:59,120 --> 00:48:01,399
Speaker 2: And that's probably I'm most worry about, is we don't

996
00:48:01,440 --> 00:48:04,239
Speaker 2: then get lazy investigators. We're not twenty years down the

997
00:48:04,239 --> 00:48:07,439
Speaker 2: track going. These teams can't think for themselves, they don't

998
00:48:07,480 --> 00:48:09,960
Speaker 2: think outside the box. You've still got to have that skill.

999
00:48:10,280 --> 00:48:14,000
Speaker 1: You bought up something that this is my silly mind

1000
00:48:14,040 --> 00:48:17,759
Speaker 1: ticking over. I learned how to do AFFI David's. I

1001
00:48:17,800 --> 00:48:19,800
Speaker 1: learned how to type up a fact sheet. I learned

1002
00:48:19,800 --> 00:48:23,520
Speaker 1: how to take a witness statement. That was the skills

1003
00:48:23,800 --> 00:48:27,000
Speaker 1: I believe I had as a detechnique. They were hard

1004
00:48:27,040 --> 00:48:29,279
Speaker 1: earned skills. It didn't come natural. It was trial and

1005
00:48:29,360 --> 00:48:33,360
Speaker 1: error and learning and experience and learning every day. Now

1006
00:48:33,840 --> 00:48:36,720
Speaker 1: to do a set of facts, you could, in fact,

1007
00:48:37,520 --> 00:48:41,560
Speaker 1: just put in brief information and ask the computer system

1008
00:48:41,640 --> 00:48:44,680
Speaker 1: AI to present a set of facts for the local court,

1009
00:48:45,000 --> 00:48:46,680
Speaker 1: or a set of facts for the district court, or

1010
00:48:46,680 --> 00:48:49,480
Speaker 1: this is going to the Supreme Court, a comprehensive set

1011
00:48:49,480 --> 00:48:52,560
Speaker 1: of facts with reference to where the material was from.

1012
00:48:53,000 --> 00:48:54,879
Speaker 2: Just a blame pro a, Thanks Gerry.

1013
00:48:55,320 --> 00:48:58,160
Speaker 1: Sorry, I didn't I think of that business. But that

1014
00:48:58,480 --> 00:49:04,600
Speaker 1: is so difficult to do and learn. Now, if I

1015
00:49:04,719 --> 00:49:08,880
Speaker 1: had read set of facts that came from AI, I could,

1016
00:49:08,920 --> 00:49:11,960
Speaker 1: because of my experience, look and go no, that's not right,

1017
00:49:12,040 --> 00:49:14,880
Speaker 1: we don't need that. But if people don't learn that

1018
00:49:15,000 --> 00:49:18,000
Speaker 1: skill to start with, what you've just said is a

1019
00:49:18,120 --> 00:49:21,080
Speaker 1: con And that's not just we're talking law enforcement here,

1020
00:49:21,080 --> 00:49:23,719
Speaker 1: we're talking detectives. Because it's a world well winner. But

1021
00:49:23,760 --> 00:49:24,920
Speaker 1: it's across the board, isn't it.

1022
00:49:25,320 --> 00:49:29,319
Speaker 2: Embarrasters, lawyers, you know, it's everyone across the board. But

1023
00:49:30,400 --> 00:49:34,600
Speaker 2: I feel the benefits far our way, the negatives and

1024
00:49:34,680 --> 00:49:37,120
Speaker 2: I think that's an important piece of moving through to

1025
00:49:37,200 --> 00:49:39,759
Speaker 2: that next stage of embracing it. But make sure we

1026
00:49:39,760 --> 00:49:42,640
Speaker 2: don't lose sight of how we then skill our people

1027
00:49:42,680 --> 00:49:45,200
Speaker 2: to fact check properly to make sure that it's accurate,

1028
00:49:45,200 --> 00:49:48,560
Speaker 2: because it will cross reference. So you get one hundred

1029
00:49:48,600 --> 00:49:51,359
Speaker 2: statements from your homicide brief or trying to statue homicide brief,

1030
00:49:51,400 --> 00:49:54,160
Speaker 2: give me those facts and circumstances for the court. It'll

1031
00:49:54,160 --> 00:49:57,440
Speaker 2: cross reference everything for you and your exhibits and timeline

1032
00:49:57,440 --> 00:49:59,279
Speaker 2: it for you and put in the language that the

1033
00:49:59,320 --> 00:50:02,520
Speaker 2: courts want, that the barristers want. Defense can better understand

1034
00:50:02,520 --> 00:50:05,360
Speaker 2: the case and your prosecutors can better understand it. So

1035
00:50:05,360 --> 00:50:08,719
Speaker 2: it should be in theory expediting all of those concerns

1036
00:50:08,719 --> 00:50:10,960
Speaker 2: and your case conferences are very clear as to what

1037
00:50:11,040 --> 00:50:13,880
Speaker 2: other points and issuing here and anyone can agree, rather

1038
00:50:13,920 --> 00:50:18,320
Speaker 2: than going through volumes and volumes of all wielding trolley's

1039
00:50:18,360 --> 00:50:21,640
Speaker 2: worth of evidence into the court. It definitely has its place,

1040
00:50:22,280 --> 00:50:24,879
Speaker 2: but we need to make sure that people still keep

1041
00:50:25,480 --> 00:50:28,799
Speaker 2: their skills in, as you said, understanding what the courts want,

1042
00:50:29,920 --> 00:50:32,319
Speaker 2: what the investigators need, and what the law is. Well.

1043
00:50:32,840 --> 00:50:35,520
Speaker 1: To give an example, I'm just thinking when you were

1044
00:50:35,560 --> 00:50:40,040
Speaker 1: saying that it wasn't unusual to get a twenty page

1045
00:50:40,080 --> 00:50:42,440
Speaker 1: statement in a homicide investigation.

1046
00:50:42,480 --> 00:50:43,200
Speaker 2: That sounds a lot.

1047
00:50:43,239 --> 00:50:46,600
Speaker 1: But if you're going nothing down in the finite detail,

1048
00:50:47,160 --> 00:50:50,520
Speaker 1: or you do a URISP interview and an electronic interview,

1049
00:50:50,520 --> 00:50:53,360
Speaker 1: that might take three or four hours. So there's a

1050
00:50:53,360 --> 00:50:56,759
Speaker 1: lot of information there, a lot of work. And when

1051
00:50:56,800 --> 00:50:59,560
Speaker 1: I was leading the investigations, or when I was working

1052
00:50:59,560 --> 00:51:02,160
Speaker 1: on the I was doing the summary of it. But

1053
00:51:02,200 --> 00:51:04,640
Speaker 1: when I was leading the investigations, I was getting people

1054
00:51:04,680 --> 00:51:07,640
Speaker 1: to summarize this fift It might be a fifty page transcrip,

1055
00:51:08,000 --> 00:51:13,400
Speaker 1: summarize this in very condensed version, highlighting the key points,

1056
00:51:13,400 --> 00:51:15,560
Speaker 1: all the areas that I might need, all the person

1057
00:51:15,640 --> 00:51:19,360
Speaker 1: running the investigation might need to pull from that statement

1058
00:51:20,080 --> 00:51:22,719
Speaker 1: or interview. That's done, is it?

1059
00:51:23,160 --> 00:51:25,040
Speaker 2: Yeah? And I can see a space where we even

1060
00:51:25,960 --> 00:51:29,440
Speaker 2: not now to looking further, where your AI is actually

1061
00:51:29,440 --> 00:51:31,200
Speaker 2: there with you in the interview room and you've got

1062
00:51:31,239 --> 00:51:33,400
Speaker 2: all your data sets there with you. As you're asking

1063
00:51:33,400 --> 00:51:36,399
Speaker 2: the questions of the accused, I can tell you, hey,

1064
00:51:36,600 --> 00:51:37,799
Speaker 2: you're missing this piece.

1065
00:51:37,680 --> 00:51:40,560
Speaker 1: And it could do it live. And I think the

1066
00:51:40,680 --> 00:51:43,239
Speaker 1: technology is even there to do that now, isn't it.

1067
00:51:43,600 --> 00:51:45,960
Speaker 2: Yeah? It's just again building it so that it's secure,

1068
00:51:46,000 --> 00:51:49,759
Speaker 2: its safe. The integrity piece is part of it. But

1069
00:51:49,880 --> 00:51:51,640
Speaker 2: you don't want to walk out of an interview room

1070
00:51:51,640 --> 00:51:53,400
Speaker 2: and go I really forgot to ask that question.

1071
00:51:53,640 --> 00:51:56,399
Speaker 1: Well, the skill of a great detective was to sit down.

1072
00:51:56,760 --> 00:51:58,840
Speaker 1: I always thought this was one of the most valuable

1073
00:51:58,840 --> 00:52:01,960
Speaker 1: skills that detective had, was the ability to extract information

1074
00:52:02,040 --> 00:52:06,200
Speaker 1: from a witness. And if you had AI sitting there

1075
00:52:06,360 --> 00:52:09,880
Speaker 1: and you're my witness, I'm getting the statement, and sometimes

1076
00:52:09,880 --> 00:52:12,920
Speaker 1: you've been up for twenty four hours, Yeah you're tired

1077
00:52:13,000 --> 00:52:15,600
Speaker 1: and getting the statement, and then you come back and

1078
00:52:15,640 --> 00:52:16,359
Speaker 1: you've I.

1079
00:52:16,360 --> 00:52:17,359
Speaker 2: Forgot that it's wrong.

1080
00:52:17,400 --> 00:52:19,680
Speaker 1: You could have AI going you haven't covered off on

1081
00:52:20,120 --> 00:52:22,560
Speaker 1: when he last spoke to the victim. There's something as

1082
00:52:22,600 --> 00:52:23,160
Speaker 1: simple as that.

1083
00:52:23,200 --> 00:52:26,000
Speaker 2: It's right, yeah, yeah, exactly right. It has a place

1084
00:52:26,000 --> 00:52:28,400
Speaker 2: for it, and you don't want to have to be

1085
00:52:28,440 --> 00:52:31,040
Speaker 2: going back to reinterview or take another statement. I want

1086
00:52:31,080 --> 00:52:33,600
Speaker 2: to retraumatize the victims by going back, because every time

1087
00:52:33,600 --> 00:52:36,880
Speaker 2: you turn up on someone's doorstep, I'm back again. What

1088
00:52:36,920 --> 00:52:39,000
Speaker 2: do they want now? The anxiety everything, So you're there

1089
00:52:39,040 --> 00:52:42,080
Speaker 2: once you're there thorough and the AI is looking at

1090
00:52:42,080 --> 00:52:44,760
Speaker 2: your entire case, And as you said, a very skilled

1091
00:52:44,800 --> 00:52:47,840
Speaker 2: investigator knew their whole case, knew everything in and out.

1092
00:52:48,080 --> 00:52:50,520
Speaker 2: But nowadays, how many are they working on it? Once

1093
00:52:51,440 --> 00:52:53,759
Speaker 2: there's a human element that can forget that, but that's

1094
00:52:53,760 --> 00:52:57,799
Speaker 2: where AI has its real appetite to assist an investigator.

1095
00:52:57,960 --> 00:52:59,880
Speaker 2: It's another tool, and I think if we look at

1096
00:52:59,880 --> 00:53:02,359
Speaker 2: it has another tool, it's going to be a great

1097
00:53:02,400 --> 00:53:03,680
Speaker 2: benefit to community.

1098
00:53:03,960 --> 00:53:07,239
Speaker 1: Do you think the law enforcement will embrace it?

1099
00:53:08,120 --> 00:53:11,160
Speaker 2: I hope so, I really hope so. I think it'll

1100
00:53:11,239 --> 00:53:15,560
Speaker 2: free up time. But essentially the purpose is to identify

1101
00:53:15,840 --> 00:53:18,600
Speaker 2: offenders quicker than what we have been in the past.

1102
00:53:18,840 --> 00:53:21,320
Speaker 2: You know, who is this person? Do we use facial

1103
00:53:21,360 --> 00:53:25,400
Speaker 2: recognition technology? Do we use data sets of phone records

1104
00:53:25,480 --> 00:53:28,560
Speaker 2: or statement taking to help us better understand evidence and

1105
00:53:28,600 --> 00:53:29,000
Speaker 2: the case?

1106
00:53:29,560 --> 00:53:32,880
Speaker 1: Well, to me, what you're describing and what AI brings

1107
00:53:32,880 --> 00:53:35,279
Speaker 1: to it, it'd be like if I hand picked the

1108
00:53:35,320 --> 00:53:38,879
Speaker 1: world's best criminal investigation team and they're at my disposal.

1109
00:53:39,080 --> 00:53:40,319
Speaker 2: That's the best way to look at it.

1110
00:53:40,400 --> 00:53:43,040
Speaker 1: Yeah, yeah, Like I'm thinking with all that, some of

1111
00:53:43,080 --> 00:53:45,360
Speaker 1: the investigations and even we were having to chat the

1112
00:53:45,400 --> 00:53:48,200
Speaker 1: other other day, and I think we're both carried the

1113
00:53:48,239 --> 00:53:51,600
Speaker 1: trauma from it those late nights when a murders happened,

1114
00:53:51,719 --> 00:53:54,960
Speaker 1: or are serious crimes happen and you need a listening

1115
00:53:54,960 --> 00:53:56,960
Speaker 1: device or something put in place, and you're sitting there

1116
00:53:57,000 --> 00:53:59,240
Speaker 1: at two o'clock in the morning typing up and after David,

1117
00:53:59,239 --> 00:54:01,600
Speaker 1: that's going to be read by a Supreme Court judge,

1118
00:54:01,960 --> 00:54:04,160
Speaker 1: and how exhausting that was, and get it in the

1119
00:54:04,239 --> 00:54:07,960
Speaker 1: terminology that's appropriate, and you could actually blow the application

1120
00:54:08,040 --> 00:54:10,400
Speaker 1: if the judge gets cranky because you haven't laid it

1121
00:54:10,400 --> 00:54:14,880
Speaker 1: out properly. That could be done in a tenth.

1122
00:54:14,640 --> 00:54:18,160
Speaker 2: Of the time easy, and then some literally seconds for

1123
00:54:18,239 --> 00:54:21,040
Speaker 2: it to process all of the evidence that's in the

1124
00:54:21,080 --> 00:54:23,800
Speaker 2: system already and put out an affidavit in the right format.

1125
00:54:23,880 --> 00:54:25,239
Speaker 2: Then you just got to fact check it. You can

1126
00:54:25,320 --> 00:54:28,400
Speaker 2: have someone awake enough to make sure that it's fact checked.

1127
00:54:28,520 --> 00:54:31,640
Speaker 1: You keep coming back to the fact check. That's going

1128
00:54:31,719 --> 00:54:33,960
Speaker 1: to be the failings, isn't it, Because I would imagine

1129
00:54:34,200 --> 00:54:37,600
Speaker 1: one time something slips through the system and everyone goes, well,

1130
00:54:37,600 --> 00:54:40,000
Speaker 1: this is why we can't use AI. There's going to

1131
00:54:40,040 --> 00:54:42,200
Speaker 1: be a reluctance and police will hide behind that or

1132
00:54:42,280 --> 00:54:44,959
Speaker 1: law enforcement will hide well, you can't trust that because

1133
00:54:45,000 --> 00:54:48,080
Speaker 1: someone submitted the facts and had the wrong name or

1134
00:54:48,320 --> 00:54:50,880
Speaker 1: something as stupid as that. That's what we're going to

1135
00:54:50,880 --> 00:54:55,400
Speaker 1: make sure. So you need someone actually still putting a

1136
00:54:55,480 --> 00:54:57,239
Speaker 1: signature to it. You can't just go, oh, well that

1137
00:54:57,320 --> 00:54:58,640
Speaker 1: was a computer. It's not our fault.

1138
00:54:58,760 --> 00:55:00,279
Speaker 2: Yeah, And that's what we've got to work with law

1139
00:55:00,360 --> 00:55:03,279
Speaker 2: enforcement agencies. Develop the policies and procedures that sit with

1140
00:55:03,400 --> 00:55:06,200
Speaker 2: the AI. Don't compete against it. Let's work with it

1141
00:55:06,480 --> 00:55:08,360
Speaker 2: and have your policies and procedures that talk to it.

1142
00:55:08,520 --> 00:55:10,440
Speaker 2: This is what I expect from you. So your supervisor

1143
00:55:10,400 --> 00:55:12,279
Speaker 2: as a fact checking, we're doing that now.

1144
00:55:12,560 --> 00:55:14,840
Speaker 1: They so we had to, like I'd have a junior

1145
00:55:14,880 --> 00:55:18,439
Speaker 1: officer prepare an affidavid and I would have to sign

1146
00:55:18,480 --> 00:55:20,680
Speaker 1: it off before it was forwarded. And that was me

1147
00:55:21,080 --> 00:55:23,560
Speaker 1: fact checking that the person knew what they were doing

1148
00:55:23,600 --> 00:55:25,600
Speaker 1: and it was truthful and accurate.

1149
00:55:25,680 --> 00:55:27,719
Speaker 2: That's right. You just haven't got the poor young constable

1150
00:55:27,760 --> 00:55:29,520
Speaker 2: doing four or five hours on an affidavit.

1151
00:55:31,400 --> 00:55:34,279
Speaker 1: We'll take a break here. When we come back, we'll

1152
00:55:34,320 --> 00:55:37,319
Speaker 1: break it down and talk about some investigations on how

1153
00:55:37,320 --> 00:55:39,680
Speaker 1: that could actually help. I'd like to look at the

1154
00:55:40,200 --> 00:55:43,319
Speaker 1: unsolved homicide, the cold cases too. On my mind, sort

1155
00:55:43,320 --> 00:55:46,680
Speaker 1: of the ticking ovor how I could play a part

1156
00:55:46,719 --> 00:55:49,320
Speaker 1: in that. But it is a brave new world.

1157
00:55:49,160 --> 00:55:52,400
Speaker 2: Isn't it. It certainly is Gary, And I think.

1158
00:55:52,280 --> 00:55:54,000
Speaker 1: I might be able to do myself out of a job,

1159
00:55:54,040 --> 00:55:57,520
Speaker 1: would I like, as in hosting a podcast like nothing's

1160
00:55:58,000 --> 00:55:59,040
Speaker 1: off the table, is it? No?

1161
00:55:59,160 --> 00:56:01,719
Speaker 2: That's right. That's where everyone's a bit anxious and a

1162
00:56:01,719 --> 00:56:04,440
Speaker 2: bit nervous about what does that future look like? Replacing

1163
00:56:04,800 --> 00:56:05,600
Speaker 2: placing jobs.

1164
00:56:05,640 --> 00:56:08,839
Speaker 1: But well, I want to talk talk about court too,

1165
00:56:08,880 --> 00:56:11,000
Speaker 1: because I think that it's going to be a big

1166
00:56:11,080 --> 00:56:14,400
Speaker 1: change in court, not just the role of solicitors and

1167
00:56:14,680 --> 00:56:17,800
Speaker 1: whether it be defense or prosecution, but magistrates and judges

1168
00:56:17,840 --> 00:56:21,440
Speaker 1: and yeah, interesting, Okay, we'll be back back shortly.