1 00:00:00,240 --> 00:00:04,680 Speaker 1: From UFOs to psychic powers and government conspiracies. History is 2 00:00:04,760 --> 00:00:09,080 Speaker 1: riddled with unexplained events. You can turn back now or 3 00:00:09,200 --> 00:00:12,079 Speaker 1: learn the stuff they don't want you to know. A 4 00:00:12,200 --> 00:00:25,520 Speaker 1: production of I Heart Radio. Hello, welcome back to the show. 5 00:00:25,600 --> 00:00:28,120 Speaker 1: My name is Matt, my name is Nol. They called 6 00:00:28,120 --> 00:00:30,960 Speaker 1: me Ben. We are joined today with our guest super 7 00:00:30,960 --> 00:00:35,440 Speaker 1: producer Seth Nicholas Johnson. Most importantly, you are you. You 8 00:00:35,520 --> 00:00:38,680 Speaker 1: are here, and that makes this the stuff they don't 9 00:00:38,960 --> 00:00:43,319 Speaker 1: want you to know. This is a global episode, it's 10 00:00:43,320 --> 00:00:46,519 Speaker 1: an international episode, and we do hope you enjoy it. 11 00:00:47,240 --> 00:00:50,880 Speaker 1: Let's just let's put all the cards on the table 12 00:00:50,960 --> 00:00:55,080 Speaker 1: real quick here. China is often vilified in Western media, 13 00:00:55,320 --> 00:00:58,840 Speaker 1: Europe and the US and Australia, of course, and often 14 00:00:58,960 --> 00:01:02,959 Speaker 1: for good reason. But if you look at it, if 15 00:01:03,000 --> 00:01:05,119 Speaker 1: you look at the day to day lives and most people, 16 00:01:05,880 --> 00:01:07,840 Speaker 1: we all kind of want the same things right in. 17 00:01:07,959 --> 00:01:10,720 Speaker 1: China is a country filled with people just like any other. 18 00:01:11,000 --> 00:01:13,480 Speaker 1: So most of the day to day stuff that happens 19 00:01:13,480 --> 00:01:16,880 Speaker 1: in China never makes it to the domestic news, much 20 00:01:16,959 --> 00:01:19,959 Speaker 1: less the international news. Just like here in the States. 21 00:01:19,959 --> 00:01:22,360 Speaker 1: You know, you don't see a big CNN headline that 22 00:01:22,560 --> 00:01:25,080 Speaker 1: is like so and so got a parking ticket. So 23 00:01:25,200 --> 00:01:27,480 Speaker 1: someone in China might get a parking ticket. They might 24 00:01:27,520 --> 00:01:29,320 Speaker 1: have to go get married or divorced and do the 25 00:01:29,360 --> 00:01:33,440 Speaker 1: paperwork for one reason or another. They might also, unfortunately 26 00:01:33,520 --> 00:01:37,319 Speaker 1: find themselves in court. And that is where today's episode begins. Guys, 27 00:01:37,360 --> 00:01:40,240 Speaker 1: I don't want to spoil too much just yet, but 28 00:01:40,280 --> 00:01:43,120 Speaker 1: I'm thinking we can just nickname this episode Law and 29 00:01:43,240 --> 00:01:48,280 Speaker 1: Order China. Addition, um, can I be Sam Waters? Um? 30 00:01:51,040 --> 00:01:53,120 Speaker 1: Wasn't he? He was? He was O G Law and 31 00:01:53,240 --> 00:01:57,400 Speaker 1: Order right like like original series. I don't know, God 32 00:01:57,440 --> 00:01:59,400 Speaker 1: knows how many of those there are. Dick Wolf is 33 00:01:59,440 --> 00:02:02,520 Speaker 1: a madman, you said on our recent episode of Ridiculous History, Ben, 34 00:02:02,560 --> 00:02:05,080 Speaker 1: something must have happened then, I wonder. Yeah, I was 35 00:02:05,120 --> 00:02:07,320 Speaker 1: just about to say that here too. So it's like, 36 00:02:07,600 --> 00:02:10,480 Speaker 1: if you look at Law and Order spinoffs, you know, uh, 37 00:02:10,520 --> 00:02:13,640 Speaker 1: you will see the same episodes kind of occur with 38 00:02:13,800 --> 00:02:18,119 Speaker 1: just different people. Mad lived in prom is a big one. 39 00:02:18,440 --> 00:02:21,359 Speaker 1: What happened at your problem? Dick Wolf? Are you okay? 40 00:02:21,560 --> 00:02:25,840 Speaker 1: But but in this uh? I would also before you 41 00:02:25,880 --> 00:02:29,520 Speaker 1: commit to Sam Waterson, noal, I would point out that 42 00:02:29,760 --> 00:02:33,400 Speaker 1: Jeff gold Bloom is in Law and Order criminal intent 43 00:02:33,840 --> 00:02:37,080 Speaker 1: for some reason. So that might be that might affect 44 00:02:37,080 --> 00:02:40,520 Speaker 1: your decision. But I just like the name Sam Waterson. 45 00:02:40,600 --> 00:02:42,240 Speaker 1: I don't know why, it just does it for me, 46 00:02:42,960 --> 00:02:47,400 Speaker 1: son of Son of the water, UH, and speaking water. 47 00:02:47,520 --> 00:02:52,639 Speaker 1: Let's travel across the Pacific to China. Here are the facts. Like, 48 00:02:52,760 --> 00:02:55,760 Speaker 1: if you are listening and you're from the U S 49 00:02:55,880 --> 00:02:58,880 Speaker 1: or you live in the US, now you're already on 50 00:02:58,960 --> 00:03:02,600 Speaker 1: some level of where of how that country's judicial system works. 51 00:03:02,919 --> 00:03:06,840 Speaker 1: The three branches of US government are in theory separate, 52 00:03:07,240 --> 00:03:11,120 Speaker 1: meaning that the courts are again in theory, supposed to 53 00:03:11,160 --> 00:03:15,360 Speaker 1: be beyond the reach of political theatrics and political parties. 54 00:03:15,400 --> 00:03:19,840 Speaker 1: And again we can't say that's theoretical often enough because, 55 00:03:19,960 --> 00:03:24,240 Speaker 1: especially in recent years, UH, the courts have been highly 56 00:03:24,280 --> 00:03:28,480 Speaker 1: politicized and the court of public opinion has to UH 57 00:03:28,560 --> 00:03:32,680 Speaker 1: in an arguable degree, affected judicial decisions. So what are 58 00:03:32,760 --> 00:03:36,960 Speaker 1: the similarities and differences between the judicial system in China 59 00:03:37,120 --> 00:03:40,200 Speaker 1: and the judicial system in the US? Honnestly didn't really 60 00:03:40,200 --> 00:03:43,040 Speaker 1: realize the distinction between these and quite as cut and 61 00:03:43,120 --> 00:03:46,120 Speaker 1: dry away as we're going to lay out here, um ben, 62 00:03:46,200 --> 00:03:49,200 Speaker 1: But you do a really fantastic job of spelling out 63 00:03:49,200 --> 00:03:51,920 Speaker 1: exactly the difference. There is civil law and then there 64 00:03:52,000 --> 00:03:54,560 Speaker 1: is common law. And I guess the term common law 65 00:03:54,600 --> 00:03:57,120 Speaker 1: to mean helly's associated with like a common law wife, 66 00:03:57,440 --> 00:03:59,760 Speaker 1: you know, a common law partner, like someone that by 67 00:03:59,760 --> 00:04:01,760 Speaker 1: like or you have just doing something for long enough, 68 00:04:01,760 --> 00:04:04,520 Speaker 1: it kind of becomes the thing. Um. But no, it's 69 00:04:04,560 --> 00:04:07,920 Speaker 1: more than that. Common law is essentially law that is 70 00:04:07,960 --> 00:04:11,320 Speaker 1: based on case law. Case law that is of course 71 00:04:11,360 --> 00:04:16,440 Speaker 1: published judicial opinions is prioritized, which means that someone who 72 00:04:16,520 --> 00:04:19,920 Speaker 1: is presiding over a case, whether it be a judge 73 00:04:20,000 --> 00:04:22,480 Speaker 1: or some sort of tribunal or what have you, um, 74 00:04:22,560 --> 00:04:26,760 Speaker 1: would prioritize you know, uh, the cases of the past 75 00:04:27,080 --> 00:04:30,159 Speaker 1: to determine how to act in a particular case that 76 00:04:30,279 --> 00:04:32,279 Speaker 1: is in front of them um at that moment. The 77 00:04:32,320 --> 00:04:34,880 Speaker 1: idea of legal precedent would be a good way of 78 00:04:34,880 --> 00:04:37,479 Speaker 1: thinking of it. Um. And some countries that use this 79 00:04:37,560 --> 00:04:40,799 Speaker 1: type of law include places like the United States, England, India, 80 00:04:40,880 --> 00:04:44,200 Speaker 1: and Canada. Yeah, and then the difference here is that 81 00:04:44,240 --> 00:04:47,000 Speaker 1: in China you're you're dealing with civil law. It's a 82 00:04:47,000 --> 00:04:49,400 Speaker 1: civil law system and it is a little bit different. 83 00:04:49,720 --> 00:04:52,960 Speaker 1: It's ah just to comment on the common law system, 84 00:04:53,520 --> 00:04:56,200 Speaker 1: I I think and what I remember being told is 85 00:04:56,240 --> 00:04:58,200 Speaker 1: that the reason for having that is so that the 86 00:04:58,279 --> 00:05:03,240 Speaker 1: law can evolve, society of the law evolves. Uh. Within 87 00:05:03,320 --> 00:05:08,320 Speaker 1: civil law countries like China, Uh, there are there are 88 00:05:08,440 --> 00:05:13,080 Speaker 1: just statutes. There are civil like rules that exist, and 89 00:05:13,560 --> 00:05:16,960 Speaker 1: what those rules state on paper is what goes right, 90 00:05:17,400 --> 00:05:20,800 Speaker 1: Uh this, If you do what this law states not 91 00:05:20,880 --> 00:05:23,839 Speaker 1: to do, then you are in trouble and you've broken 92 00:05:24,000 --> 00:05:27,440 Speaker 1: that statute. And I mean, conceivably doesn't that simplify things 93 00:05:27,480 --> 00:05:31,200 Speaker 1: a little bit? Uh? It definitely becomes a little bit 94 00:05:31,200 --> 00:05:34,000 Speaker 1: more didactic and what what's the word like Hammurabian? Like 95 00:05:34,080 --> 00:05:36,719 Speaker 1: the idea of this like code that if you break 96 00:05:36,760 --> 00:05:39,039 Speaker 1: the code then you get the punishment. Then it's all 97 00:05:39,080 --> 00:05:41,480 Speaker 1: written now. Um, So it certainly keeps you from having 98 00:05:41,480 --> 00:05:43,720 Speaker 1: to jump through all these hoops and figure out, well, no, 99 00:05:43,839 --> 00:05:45,760 Speaker 1: in this case, they did this, and that means that 100 00:05:45,800 --> 00:05:48,200 Speaker 1: we should apply this you know, precedent to this, and 101 00:05:48,279 --> 00:05:50,080 Speaker 1: oh now it's actually changed a little bit, so we 102 00:05:50,120 --> 00:05:52,440 Speaker 1: have to like retool things. I mean, you could definitely 103 00:05:52,520 --> 00:05:54,560 Speaker 1: argue that civil law is the more straightforward way of 104 00:05:54,600 --> 00:05:57,840 Speaker 1: doing it, but also leads you to more um, kind 105 00:05:57,839 --> 00:06:02,479 Speaker 1: of draconian measures. Right, Yeah, it's non negotiable is the 106 00:06:02,520 --> 00:06:05,640 Speaker 1: big difference. And one of the big things to keep 107 00:06:05,680 --> 00:06:07,320 Speaker 1: in mind here is that you know, you've got the 108 00:06:07,400 --> 00:06:10,279 Speaker 1: law that or the you know, the civil codes. Once 109 00:06:10,320 --> 00:06:13,760 Speaker 1: those are broken, the way it's kind of enacted is 110 00:06:13,760 --> 00:06:16,080 Speaker 1: a little different too, right Ben, And maybe correct me 111 00:06:16,120 --> 00:06:19,320 Speaker 1: if I'm anything, if anything is inaccurate here. But the 112 00:06:19,440 --> 00:06:22,919 Speaker 1: judge then just collects all the data on that case, 113 00:06:23,400 --> 00:06:27,080 Speaker 1: everything that is known, all the facts, and then applies 114 00:06:27,200 --> 00:06:31,039 Speaker 1: basically the punishments that are also within that code to 115 00:06:31,360 --> 00:06:34,160 Speaker 1: the person that broke the code. Yeah, yeah, just so. 116 00:06:34,640 --> 00:06:38,080 Speaker 1: And there are other nuts and bolts aspects of this, 117 00:06:38,200 --> 00:06:42,600 Speaker 1: like the role of lawyer, uh, the role of the judge, 118 00:06:42,760 --> 00:06:46,239 Speaker 1: you know, fact finder versus decree uh. And this also 119 00:06:46,320 --> 00:06:49,120 Speaker 1: plays out in the way a jury trial would work. 120 00:06:49,400 --> 00:06:51,760 Speaker 1: But it's important to say China is not unique in 121 00:06:51,800 --> 00:06:56,200 Speaker 1: the idea of civil law. Countries like Japan, Germany, France, Spain, 122 00:06:56,240 --> 00:06:59,320 Speaker 1: a lot of Continental Europe uses this approach, and a 123 00:06:59,320 --> 00:07:01,919 Speaker 1: lot of country across the world use a mix of 124 00:07:02,000 --> 00:07:05,919 Speaker 1: features from both civil and common law. And then in 125 00:07:05,960 --> 00:07:08,120 Speaker 1: the case of China, some of this is also based 126 00:07:08,160 --> 00:07:13,040 Speaker 1: on pre existing Chinese judicial approaches from the past. Well, 127 00:07:13,160 --> 00:07:15,920 Speaker 1: certainly there must be ways to change those statutes or 128 00:07:16,080 --> 00:07:18,600 Speaker 1: those codes if if the need arises, but it would 129 00:07:18,600 --> 00:07:20,840 Speaker 1: probably just take a little bit more doing right. Well 130 00:07:20,960 --> 00:07:25,880 Speaker 1: sure be jimping, Yeah, exactly, fair enough enough, and it's 131 00:07:25,880 --> 00:07:28,480 Speaker 1: really quickly. I think I coined a new term Hammurabi, 132 00:07:28,560 --> 00:07:30,440 Speaker 1: and I mean everybody's when I met by that, I think. 133 00:07:30,480 --> 00:07:32,960 Speaker 1: But the code of Hammurabi was, like I think the 134 00:07:33,160 --> 00:07:36,480 Speaker 1: if not one of the earliest codified sets of laws 135 00:07:36,520 --> 00:07:39,480 Speaker 1: that would apply, you know, to a society. Yeah, and 136 00:07:39,520 --> 00:07:43,160 Speaker 1: the quote of Hammurabi um may not have aged well 137 00:07:43,240 --> 00:07:46,560 Speaker 1: because it will seem a little bit absolutist and draconian, 138 00:07:46,640 --> 00:07:51,560 Speaker 1: but it is, uh. It is the first comprehensive written 139 00:07:51,600 --> 00:07:55,240 Speaker 1: record of if you do X, we will do why, 140 00:07:55,400 --> 00:07:59,720 Speaker 1: and you know people need uh, the civilizations, I would say, 141 00:08:00,040 --> 00:08:03,280 Speaker 1: eat that kind of guidance, but we have to so 142 00:08:03,320 --> 00:08:06,600 Speaker 1: we've laid out, you know, this sort of I would say, 143 00:08:06,640 --> 00:08:13,400 Speaker 1: not even philosophical difference, but structural difference, and neither. They 144 00:08:13,400 --> 00:08:16,760 Speaker 1: both have advantages and disadvantages, but it's not as if 145 00:08:16,840 --> 00:08:20,640 Speaker 1: one is evil and one is noble. They're just different approaches. 146 00:08:21,080 --> 00:08:24,440 Speaker 1: Like you know, ca CD and a burrito have the 147 00:08:24,480 --> 00:08:29,880 Speaker 1: same ingredients anyway, the perhaps the more important difference between 148 00:08:30,000 --> 00:08:35,160 Speaker 1: Chinese and us UH judiciary systems. Here is a functional 149 00:08:35,240 --> 00:08:39,280 Speaker 1: difference the judicial branch in China, and I hope this 150 00:08:39,320 --> 00:08:44,160 Speaker 1: isn't a hot take to say this. It isn't really independent, 151 00:08:44,240 --> 00:08:47,720 Speaker 1: not really, because it's an extension of the government, has 152 00:08:47,920 --> 00:08:53,280 Speaker 1: very little separation, meaning that it is part of the 153 00:08:53,400 --> 00:08:57,720 Speaker 1: ruling political party, the Communist Party of China, that is 154 00:08:57,760 --> 00:09:00,800 Speaker 1: in charge of the PRC or Pupil's public of China. 155 00:09:01,040 --> 00:09:03,440 Speaker 1: This might not seem like a big, big difference for 156 00:09:03,520 --> 00:09:09,560 Speaker 1: little transgressions. Somebody's littering, uh, somebody has like stolen some 157 00:09:09,880 --> 00:09:13,040 Speaker 1: DVDs if there are still DVDs around. Those things are 158 00:09:13,080 --> 00:09:16,199 Speaker 1: kind of a political depending on the content of the DVD, 159 00:09:16,840 --> 00:09:20,400 Speaker 1: and the Chinese system in that case can function like 160 00:09:20,440 --> 00:09:23,840 Speaker 1: an independent branch of politics because no one's talking about 161 00:09:23,880 --> 00:09:27,200 Speaker 1: something like Tianamen Square, no one's talking about the weakers. 162 00:09:27,200 --> 00:09:30,080 Speaker 1: But as you can imagine, this difference comes to the 163 00:09:30,120 --> 00:09:35,120 Speaker 1: forefront in a huge way whenever a case has anything 164 00:09:35,240 --> 00:09:37,920 Speaker 1: that might be seen as a political element, and often 165 00:09:37,960 --> 00:09:40,720 Speaker 1: the way this is reported in the West, UH, those 166 00:09:40,760 --> 00:09:44,560 Speaker 1: political elements may seem really small and innocuous, but they 167 00:09:44,559 --> 00:09:49,640 Speaker 1: are a big, big deal domestically. And yeah, this isn't 168 00:09:49,640 --> 00:09:54,480 Speaker 1: not meant to disparage the Chinese government unduly. Those two 169 00:09:54,520 --> 00:09:58,880 Speaker 1: things are simple structural facts. Of course, a lack of 170 00:09:58,920 --> 00:10:02,080 Speaker 1: independence can clear early lead to corruption. It can open 171 00:10:02,120 --> 00:10:05,319 Speaker 1: the door for conspiracy and even tyranny. But we can't 172 00:10:05,320 --> 00:10:07,520 Speaker 1: forget that, you know. Like I said earlier, the U 173 00:10:07,600 --> 00:10:10,640 Speaker 1: S courts over the past several years and decades especially 174 00:10:10,840 --> 00:10:15,600 Speaker 1: have found themselves embroiled in the world of politics despite 175 00:10:15,640 --> 00:10:19,920 Speaker 1: that theoretical just the facts approach, you know. I like 176 00:10:20,000 --> 00:10:22,280 Speaker 1: to think about it is we can't throw stones from 177 00:10:22,280 --> 00:10:27,720 Speaker 1: our own glass courthouse from or within, I guess, because 178 00:10:27,760 --> 00:10:29,760 Speaker 1: it all comes crashing now. I don't know. At least 179 00:10:29,840 --> 00:10:33,360 Speaker 1: the China sort of acknowledges that that's how it is. 180 00:10:33,559 --> 00:10:36,440 Speaker 1: For the most part, there's no illusions of separation. I 181 00:10:36,480 --> 00:10:39,560 Speaker 1: think here we're inching more. Not to be too hot 182 00:10:39,600 --> 00:10:41,320 Speaker 1: of a take on it, but I think we all 183 00:10:41,400 --> 00:10:43,439 Speaker 1: kind of see it. We're inching more and more towards 184 00:10:43,440 --> 00:10:47,480 Speaker 1: something that feels like there is very little divide, you know. 185 00:10:47,559 --> 00:10:50,120 Speaker 1: And I think there's a lot of like legal apologists 186 00:10:50,120 --> 00:10:51,960 Speaker 1: and kind of purists that maybe say, no, no, no, 187 00:10:52,280 --> 00:10:53,959 Speaker 1: it's there it's still there. It's like part of the 188 00:10:54,200 --> 00:10:57,600 Speaker 1: tradition and it must remain. But you know, functionally, it's sure. 189 00:10:57,880 --> 00:11:00,400 Speaker 1: Sometimes it doesn't seem like that divide the exists the 190 00:11:00,400 --> 00:11:03,600 Speaker 1: way it might. And we really see politics entering into 191 00:11:03,679 --> 00:11:07,240 Speaker 1: the judicial branch as you go higher in the circuit 192 00:11:07,320 --> 00:11:10,840 Speaker 1: system until you get to that old Supreme Court. And 193 00:11:10,960 --> 00:11:12,480 Speaker 1: you know that, I would say the politics kind of 194 00:11:12,520 --> 00:11:15,640 Speaker 1: start at the top level and then go down trick down. 195 00:11:16,160 --> 00:11:24,800 Speaker 1: It's tricked trickled down politics. Well, yeah, yeah, and you know, 196 00:11:24,920 --> 00:11:27,360 Speaker 1: we we could spend a lot of time here just 197 00:11:27,400 --> 00:11:31,680 Speaker 1: talking about the philosophical differences between you know, China and 198 00:11:31,679 --> 00:11:33,880 Speaker 1: the United States when it comes to government. When we 199 00:11:33,960 --> 00:11:37,480 Speaker 1: say there's one party in China, it is true that 200 00:11:37,520 --> 00:11:39,920 Speaker 1: there are two parties in the US, and that is 201 00:11:39,920 --> 00:11:44,440 Speaker 1: a difference. But if you know, you try and you 202 00:11:44,520 --> 00:11:48,280 Speaker 1: try and delve too deeply into that you know, highly 203 00:11:48,320 --> 00:11:52,320 Speaker 1: connected world of donors and movers and shakers, it's it 204 00:11:52,360 --> 00:11:55,679 Speaker 1: gets a little muddy. Yeah, I mean you're making you're 205 00:11:55,720 --> 00:11:59,920 Speaker 1: making a solid point here because, uh, there's something else 206 00:12:00,240 --> 00:12:02,360 Speaker 1: that I think needs to be pointed out more often 207 00:12:02,400 --> 00:12:07,120 Speaker 1: about the American judicial system. If a judge is elected, 208 00:12:07,880 --> 00:12:11,960 Speaker 1: then are they not inherently in some way involved in 209 00:12:12,040 --> 00:12:15,720 Speaker 1: the world of politics. I mean, that's just an inescapable fact. 210 00:12:15,840 --> 00:12:18,960 Speaker 1: But for our purposes today, those are the two big 211 00:12:19,000 --> 00:12:22,280 Speaker 1: differences to keep in mind. One, China's judicial system is 212 00:12:22,280 --> 00:12:26,320 Speaker 1: based in civil law, and it is too, in practice 213 00:12:26,360 --> 00:12:31,960 Speaker 1: inherently politicize. Big question why these differences matter? Why? Well, 214 00:12:32,000 --> 00:12:34,520 Speaker 1: first and foremost they matter a lot of the people 215 00:12:34,520 --> 00:12:38,760 Speaker 1: on trial. That's that's a rough one. Uh. And Second, 216 00:12:39,240 --> 00:12:43,800 Speaker 1: these differences accelerate because we live in a world where 217 00:12:43,800 --> 00:12:47,080 Speaker 1: technology is changing the game, changing all games, I would 218 00:12:47,160 --> 00:12:50,120 Speaker 1: argue in a very big way, a profound way. If 219 00:12:50,120 --> 00:12:53,000 Speaker 1: you have ever been involved with any aspect of your 220 00:12:53,000 --> 00:12:56,880 Speaker 1: own country's legal system, then you are doubtlessly familiar with 221 00:12:56,960 --> 00:13:00,760 Speaker 1: the slow grind of justice. Cases can eight years to 222 00:13:00,800 --> 00:13:03,760 Speaker 1: reach a conclusion. The courts, at least here in the US, 223 00:13:03,880 --> 00:13:07,480 Speaker 1: are regularly backed up. They're overloaded with cases, so much 224 00:13:07,559 --> 00:13:12,480 Speaker 1: so that it is a ubiquitous trope of most fiction 225 00:13:12,760 --> 00:13:16,160 Speaker 1: to see the overloaded public defenders saying, you know you're 226 00:13:16,240 --> 00:13:19,040 Speaker 1: my one hundred and forty nine case today. I would 227 00:13:19,040 --> 00:13:21,080 Speaker 1: do better if I was on cocaine, but I can't 228 00:13:21,080 --> 00:13:25,679 Speaker 1: afford it on this salary. Etcetera, etcetera. The courts are 229 00:13:26,080 --> 00:13:28,760 Speaker 1: like before the rise of COVID, courts were backed up, 230 00:13:28,800 --> 00:13:31,000 Speaker 1: And this is pretty common in a lot of countries, 231 00:13:31,880 --> 00:13:34,400 Speaker 1: and in the US is pretty understandable because we're talking 232 00:13:34,400 --> 00:13:39,240 Speaker 1: about a country with almost three hundred and thirty million people. Now, 233 00:13:39,280 --> 00:13:43,000 Speaker 1: imagine how convoluted all these variables become when we're in 234 00:13:43,000 --> 00:13:46,800 Speaker 1: a country with one point three billion people, like the 235 00:13:46,840 --> 00:13:50,040 Speaker 1: population of China. Uh, there's a big difference between million 236 00:13:50,040 --> 00:13:51,640 Speaker 1: and billion. Well, I have to waste a lot of 237 00:13:51,640 --> 00:13:54,480 Speaker 1: time on it, but the difference is almost certainly bigger 238 00:13:54,480 --> 00:13:58,200 Speaker 1: than most people imagine. And we are living in this 239 00:13:58,280 --> 00:14:03,080 Speaker 1: age of massive tech logical innovation. When these two factors meet, 240 00:14:03,160 --> 00:14:07,000 Speaker 1: that need for more efficiency and this explosive level of 241 00:14:07,080 --> 00:14:11,760 Speaker 1: this meteoric rise in technological growth, When those two combine, 242 00:14:12,160 --> 00:14:16,320 Speaker 1: we see the makings of something incredibly dangerous. Today's question 243 00:14:17,640 --> 00:14:20,840 Speaker 1: is a question that China asked recently. They said, what 244 00:14:20,880 --> 00:14:24,600 Speaker 1: if we can make our justice system more efficient? What 245 00:14:24,680 --> 00:14:29,200 Speaker 1: if we can do it by taking the human element out, 246 00:14:29,760 --> 00:14:32,880 Speaker 1: taking some of the human element out of the human 247 00:14:32,920 --> 00:14:36,560 Speaker 1: pursuit of justice. It's a crazy question, and it's one 248 00:14:36,600 --> 00:14:39,320 Speaker 1: that gets us to some scary places. What are we 249 00:14:39,400 --> 00:14:42,360 Speaker 1: talking about. We'll tell you after a word from our sponsor. 250 00:14:48,960 --> 00:14:54,680 Speaker 1: Here's where it gets crazy. So Matt Noel Seth fellow 251 00:14:54,680 --> 00:14:58,040 Speaker 1: conspiracy realists, this is one of those, Thank you. This 252 00:14:58,080 --> 00:15:01,360 Speaker 1: is one of those episodes that, in the course of 253 00:15:01,440 --> 00:15:04,400 Speaker 1: research and the course of digging in, always makes me 254 00:15:04,480 --> 00:15:09,760 Speaker 1: wonder what's gonna go down when we reach customs in China? 255 00:15:10,080 --> 00:15:11,920 Speaker 1: You know what I mean. I don't think they care 256 00:15:12,080 --> 00:15:14,880 Speaker 1: because we're not you know, We're not like big political figures, 257 00:15:14,880 --> 00:15:17,520 Speaker 1: were not like what's his name, John Cena having to 258 00:15:17,560 --> 00:15:23,880 Speaker 1: apologize for calling Taiwana country but are headed no comment 259 00:15:24,080 --> 00:15:29,160 Speaker 1: but the the right but the interesting well not getting 260 00:15:29,160 --> 00:15:31,400 Speaker 1: in legally, I guess. But the interesting part of this 261 00:15:32,040 --> 00:15:36,280 Speaker 1: is that what we're about to talk about will inevitably, 262 00:15:36,320 --> 00:15:39,640 Speaker 1: I would argue, apply to things outside of the courtroom. 263 00:15:39,720 --> 00:15:43,960 Speaker 1: And shortly you'll see why. Right now, Uh, we we 264 00:15:44,040 --> 00:15:46,800 Speaker 1: gotta set the stage. You may have heard us briefly 265 00:15:46,960 --> 00:15:52,280 Speaker 1: mentioned this development in a previous Strange News segment, and 266 00:15:52,320 --> 00:15:55,280 Speaker 1: it's it's such a big story that we pretty much said, 267 00:15:56,080 --> 00:15:58,360 Speaker 1: hang on, we're gonna pause. This is an episode and 268 00:15:58,360 --> 00:16:00,520 Speaker 1: we're gonna come back to it. And that's what we're doing. 269 00:16:00,560 --> 00:16:03,640 Speaker 1: Because the rumors are true. The government of China has 270 00:16:03,760 --> 00:16:10,040 Speaker 1: indeed created an artificial intelligence program for its judicial system. 271 00:16:10,080 --> 00:16:13,040 Speaker 1: It's a machine that you're going to be hearing called 272 00:16:13,280 --> 00:16:16,600 Speaker 1: System two oh six or two oh six system, but 273 00:16:17,520 --> 00:16:21,160 Speaker 1: it's that's not entirely true. The new thing is based 274 00:16:21,280 --> 00:16:24,400 Speaker 1: on System two O six, which was a pre existing 275 00:16:24,760 --> 00:16:28,320 Speaker 1: AI tool. Is the name System two o six grade 276 00:16:28,560 --> 00:16:31,120 Speaker 1: and you know, not not really my opinion. I think 277 00:16:31,120 --> 00:16:34,560 Speaker 1: they're cooler names out there. But um, I guess they 278 00:16:34,560 --> 00:16:38,680 Speaker 1: didn't read our email, so place don't set yourself sure 279 00:16:38,680 --> 00:16:40,560 Speaker 1: you had something. We need to at least put these 280 00:16:40,560 --> 00:16:43,840 Speaker 1: out in the world because they're pretty epically great. I 281 00:16:43,880 --> 00:16:46,400 Speaker 1: don't know. I don't think Judge A Tron nine thousand 282 00:16:46,440 --> 00:16:49,160 Speaker 1: is something that people want to hear when they're getting 283 00:16:49,400 --> 00:16:54,240 Speaker 1: prison time. No one wants to be locked up by robobusted. Well, 284 00:16:54,280 --> 00:16:56,920 Speaker 1: I mean, how about we just call it Mr Roboto 285 00:16:57,160 --> 00:17:00,280 Speaker 1: call it a day. Yeah, you know, it's to have 286 00:17:00,320 --> 00:17:02,960 Speaker 1: any illegal legal puns in it, and it's just something 287 00:17:02,960 --> 00:17:05,960 Speaker 1: that makes it feel like it's your friendly neighborhood robot 288 00:17:06,080 --> 00:17:08,119 Speaker 1: that's just gonna send you to prison or put you 289 00:17:08,160 --> 00:17:12,040 Speaker 1: to death. Oh, like the government minders that would regularly 290 00:17:12,080 --> 00:17:15,040 Speaker 1: pop up if you are browsing the web on a 291 00:17:15,160 --> 00:17:18,560 Speaker 1: in a Chinese internet cafe. He's like these little car cute, 292 00:17:18,640 --> 00:17:21,280 Speaker 1: very cute cartoon police characters pop up and say, hey, 293 00:17:21,320 --> 00:17:22,840 Speaker 1: just you know, we're keeping an eye on you, make 294 00:17:22,880 --> 00:17:26,119 Speaker 1: sure everything is safe. It's like a clippy or something 295 00:17:26,160 --> 00:17:29,359 Speaker 1: like that, but little and one of my it's funny, 296 00:17:29,440 --> 00:17:33,040 Speaker 1: this slight tangent. One of the things. Um one of 297 00:17:33,080 --> 00:17:38,560 Speaker 1: my old professors, a dear friend pointed out, he's a 298 00:17:38,600 --> 00:17:42,520 Speaker 1: former Chinese national until he left for the US. He said, 299 00:17:42,520 --> 00:17:47,000 Speaker 1: the weirdest thing about those cartoons was that if you 300 00:17:47,040 --> 00:17:49,640 Speaker 1: looked closely, they had blue eyes. I don't know why 301 00:17:49,680 --> 00:17:55,320 Speaker 1: that always astounded him, but anyhow, Yes, the internet access 302 00:17:55,320 --> 00:17:57,880 Speaker 1: game in China is is very different, and they are 303 00:17:58,040 --> 00:18:00,840 Speaker 1: leading the four. They are leading the arge on Ai. 304 00:18:00,960 --> 00:18:05,280 Speaker 1: I would point out our earlier episode where the various 305 00:18:05,320 --> 00:18:09,480 Speaker 1: members of the Pentagon and the Western Defense Establishment said 306 00:18:09,520 --> 00:18:12,080 Speaker 1: that the US was just not ready at this point, 307 00:18:12,119 --> 00:18:16,440 Speaker 1: severely outclassed, which is unfortunately true from all indications, which 308 00:18:16,440 --> 00:18:18,280 Speaker 1: isn't something that we're used to write. We're used to 309 00:18:18,320 --> 00:18:20,199 Speaker 1: being like on the bleeding edge of like all this 310 00:18:20,320 --> 00:18:23,400 Speaker 1: kind of stuff. So it's for them to admit that openly. 311 00:18:23,520 --> 00:18:26,080 Speaker 1: Has gotta gotta give them props for the for the 312 00:18:26,160 --> 00:18:29,480 Speaker 1: humility on that one. Um. But I mean they're when 313 00:18:29,520 --> 00:18:31,560 Speaker 1: when we say that they're leading in AI, it's like 314 00:18:31,600 --> 00:18:34,920 Speaker 1: across the board, they're using it in all aspects of life. 315 00:18:34,960 --> 00:18:37,399 Speaker 1: Like we talked about sesame credit and things like that. 316 00:18:37,440 --> 00:18:39,040 Speaker 1: I mean, maybe that's not quite the same, but at 317 00:18:39,040 --> 00:18:43,159 Speaker 1: the very least, they're using technology with algorithmic and AI 318 00:18:43,240 --> 00:18:47,440 Speaker 1: components in tons of ways to either streamlined processes or 319 00:18:47,480 --> 00:18:49,000 Speaker 1: what have you. And this one just made me feels 320 00:18:49,000 --> 00:18:53,879 Speaker 1: a little bit more less benign. I don't know, Yeah, 321 00:18:54,080 --> 00:18:56,880 Speaker 1: I don't know. Man. Look I think this and sesame 322 00:18:56,960 --> 00:19:01,840 Speaker 1: credit are gonna be having some fun times together when 323 00:19:01,880 --> 00:19:04,760 Speaker 1: they get fully integrated. Now. Wasn't implying that sestimate credit 324 00:19:04,800 --> 00:19:06,359 Speaker 1: is benign now? And so you're right, these are like 325 00:19:06,480 --> 00:19:08,840 Speaker 1: that's a match date in heaven. But you know, there 326 00:19:08,920 --> 00:19:12,000 Speaker 1: certainly are probably more but now ways that China's using AI, 327 00:19:12,119 --> 00:19:15,960 Speaker 1: but these are two very spooky ones. Also, sesame credit 328 00:19:16,200 --> 00:19:18,800 Speaker 1: is one of the things that system two oh six 329 00:19:18,880 --> 00:19:22,560 Speaker 1: takes into consideration. Just if anyone that doesn't remember tessimme 330 00:19:22,600 --> 00:19:27,080 Speaker 1: credit is like essentially a ranking of social status based 331 00:19:27,119 --> 00:19:30,200 Speaker 1: on things that you do, and you're on your record, right, 332 00:19:30,280 --> 00:19:34,119 Speaker 1: and it gives you higher or lower tier access to goods, 333 00:19:34,240 --> 00:19:38,119 Speaker 1: to services, essentially into governments, you know, things to travel. 334 00:19:38,800 --> 00:19:43,200 Speaker 1: And it's also ah, it's it's not just individual's actions, 335 00:19:43,200 --> 00:19:46,439 Speaker 1: it's their associations. So if you went to high school 336 00:19:46,880 --> 00:19:49,960 Speaker 1: with someone who is considered of a lower credit rating, 337 00:19:50,040 --> 00:19:52,560 Speaker 1: than that can affect you too, even if you don't 338 00:19:52,560 --> 00:19:56,760 Speaker 1: really kick it with them. Um, but yeah, they're bus 339 00:19:56,840 --> 00:20:00,320 Speaker 1: in the car while they're driving it, aren't we all? 340 00:20:00,960 --> 00:20:03,800 Speaker 1: Uh m, all right, So I'm gonna I really quickly. 341 00:20:03,840 --> 00:20:06,600 Speaker 1: I just want to mention the newspaper article, if that's okay, Ben, 342 00:20:06,640 --> 00:20:10,600 Speaker 1: that I think we were initially alerted to this story from. 343 00:20:10,680 --> 00:20:14,960 Speaker 1: It's in the South China Morning Post. The title is 344 00:20:15,080 --> 00:20:18,720 Speaker 1: Chinese scientists develop ai prosecutor that can press its own charges. 345 00:20:18,760 --> 00:20:21,600 Speaker 1: It's written by Stephen Chen and it was published on 346 00:20:21,600 --> 00:20:26,399 Speaker 1: the December. Uh. You may not be able to find it, 347 00:20:26,520 --> 00:20:28,000 Speaker 1: or if you do find it, you may not be 348 00:20:28,040 --> 00:20:31,119 Speaker 1: able to access without you know, paying some money to 349 00:20:31,520 --> 00:20:35,160 Speaker 1: access that. You can also find it in asia one 350 00:20:35,240 --> 00:20:38,480 Speaker 1: dot com. That's where we located it and both of 351 00:20:38,520 --> 00:20:41,520 Speaker 1: those both of those reports and most of the reports 352 00:20:41,560 --> 00:20:45,240 Speaker 1: are quoting South China Morning Post. You can find some 353 00:20:45,320 --> 00:20:49,600 Speaker 1: other domestic Chinese media that has been translated to English 354 00:20:49,720 --> 00:20:52,760 Speaker 1: if you don't read Chinese. Uh. There was a paper 355 00:20:52,920 --> 00:20:59,080 Speaker 1: published this past December December in a journal called Management Review, 356 00:20:59,480 --> 00:21:03,439 Speaker 1: which is peer reviewed, but peer reviewed domestically, and in 357 00:21:03,560 --> 00:21:07,240 Speaker 1: this UH, Professor She Young, who is a director of 358 00:21:07,240 --> 00:21:12,120 Speaker 1: the Chinese Academy of Sciences Big Data and Knowledge Management Laboratory, 359 00:21:12,359 --> 00:21:16,679 Speaker 1: broke down the basics of how this AI works. It 360 00:21:16,880 --> 00:21:20,400 Speaker 1: is a machine. It was built and tested in Shanghai, 361 00:21:20,520 --> 00:21:22,479 Speaker 1: and it makes sense there was built and tested in 362 00:21:22,480 --> 00:21:28,520 Speaker 1: Shanghai because Shanghai has the largest and busiest district prosecution office. 363 00:21:29,000 --> 00:21:32,600 Speaker 1: For like a very rough comparison, folks, imagine this if 364 00:21:32,640 --> 00:21:36,200 Speaker 1: you're familiar with the US, this would be like California, 365 00:21:36,400 --> 00:21:40,040 Speaker 1: which is the United States biggest court system. We're talking 366 00:21:40,080 --> 00:21:45,240 Speaker 1: like twelve percent of all litigation. Imagine California saying, chiechh, 367 00:21:45,280 --> 00:21:48,119 Speaker 1: we have to make our lives easier somehow cut to 368 00:21:48,600 --> 00:21:52,439 Speaker 1: the infomercial, but it's the California legal system saying there's 369 00:21:52,480 --> 00:21:56,200 Speaker 1: got to be a better way. Uh. That's what that's 370 00:21:56,200 --> 00:21:59,240 Speaker 1: what people in Shanghai. We're thinking as well, Professor. She 371 00:21:59,640 --> 00:22:04,680 Speaker 1: argues that this technology could reduce prosecutor's daily workload and 372 00:22:04,800 --> 00:22:08,040 Speaker 1: let them focus on more difficult tasks that need that 373 00:22:08,160 --> 00:22:13,159 Speaker 1: human touch. So it's often being framed as a not 374 00:22:13,440 --> 00:22:17,200 Speaker 1: a hard hitting law and order style prosecutor, but as 375 00:22:17,240 --> 00:22:20,919 Speaker 1: a twenty four hour judges assistant. So very much a 376 00:22:20,960 --> 00:22:23,440 Speaker 1: clippy vibe. Can we also just say that the name 377 00:22:23,520 --> 00:22:27,359 Speaker 1: of the District Prosecution office in Shanghai is a delight. 378 00:22:27,640 --> 00:22:33,240 Speaker 1: It is the Poodong People's procuretret procuret. I don't know 379 00:22:33,280 --> 00:22:36,399 Speaker 1: this word. I'm used to like words like protectorate, but 380 00:22:36,560 --> 00:22:41,600 Speaker 1: like it's like something that procures things, and I don't know. 381 00:22:41,600 --> 00:22:43,760 Speaker 1: It's just a very new word for me. It's it's 382 00:22:43,880 --> 00:22:46,640 Speaker 1: um I I had. I looked up just quite a bit. 383 00:22:47,320 --> 00:22:51,400 Speaker 1: The procureator is kind of the same as the prosecutor, Yes, 384 00:22:52,200 --> 00:22:55,440 Speaker 1: very very similar, not exactly the same, very similar. Yeah, 385 00:22:55,520 --> 00:23:00,119 Speaker 1: it's both the it's the individual or the department that 386 00:23:00,200 --> 00:23:03,480 Speaker 1: has charged with the investigation prosecution of crimes. It dates back, 387 00:23:03,600 --> 00:23:06,359 Speaker 1: I believe, to the Roman Empire, which is kind of 388 00:23:06,400 --> 00:23:08,680 Speaker 1: neat because you see a lot of human history in 389 00:23:08,720 --> 00:23:12,000 Speaker 1: these sort of legacy terms and they pop up in 390 00:23:12,240 --> 00:23:16,000 Speaker 1: you know, the legal systems across the world. Yeah, so 391 00:23:16,280 --> 00:23:19,680 Speaker 1: here's here's what they did. They developed this AI prosecutor 392 00:23:20,000 --> 00:23:23,879 Speaker 1: and it can run on a desktop computer. So for 393 00:23:23,960 --> 00:23:28,879 Speaker 1: each suspect or, like each case it is considering, it 394 00:23:29,240 --> 00:23:32,080 Speaker 1: decides whether or not to press a charge and what 395 00:23:32,200 --> 00:23:35,199 Speaker 1: kind of charge to press based on one thousand or 396 00:23:35,280 --> 00:23:41,040 Speaker 1: so traits obtained from this description text. And most of 397 00:23:41,040 --> 00:23:43,920 Speaker 1: the traits that it's picking up are things that are 398 00:23:44,080 --> 00:23:48,320 Speaker 1: too small or too abstract to maybe really stand out 399 00:23:48,400 --> 00:23:52,600 Speaker 1: to um to a meet sack prosecutor, a human prosecutor. 400 00:23:52,680 --> 00:23:55,359 Speaker 1: And this reminds me a lot of the AI that 401 00:23:55,480 --> 00:23:59,480 Speaker 1: figured out the new way to make an efficient coil gun. 402 00:24:00,000 --> 00:24:03,320 Speaker 1: Remember that when it was noticing tiny variables you could 403 00:24:03,359 --> 00:24:08,199 Speaker 1: change to up the performance of those of those arms. 404 00:24:08,600 --> 00:24:13,280 Speaker 1: Uh yeah, but man, I I want to know what 405 00:24:13,359 --> 00:24:16,560 Speaker 1: the heck they're talking about. Because they're basing that on 406 00:24:16,760 --> 00:24:19,600 Speaker 1: a description that is written by a human who has 407 00:24:19,680 --> 00:24:23,119 Speaker 1: gathered facts about the case. Then this AI is just 408 00:24:23,160 --> 00:24:26,240 Speaker 1: combing over that written description that a human made to 409 00:24:26,240 --> 00:24:29,760 Speaker 1: to pick out these little things that the other human 410 00:24:30,240 --> 00:24:34,600 Speaker 1: who didn't it's the little things. It also kind of 411 00:24:34,640 --> 00:24:37,960 Speaker 1: goes into the larger conversation about like what AI even means. 412 00:24:38,400 --> 00:24:40,879 Speaker 1: Like we talked about Ada Lovelace right in the past, 413 00:24:40,920 --> 00:24:44,040 Speaker 1: like when we've discussed AI and her concept of AI 414 00:24:44,280 --> 00:24:46,760 Speaker 1: isn't possible because the computer can only ever do what 415 00:24:46,840 --> 00:24:49,600 Speaker 1: the programmer tells it to do. But essentially what we're 416 00:24:49,600 --> 00:24:52,040 Speaker 1: talking about here is a computer doing what the programmer 417 00:24:52,080 --> 00:24:55,000 Speaker 1: tells it to do, or the very least um operating 418 00:24:55,080 --> 00:24:57,919 Speaker 1: under a set of instructions that the programmer tells it 419 00:24:57,960 --> 00:25:01,760 Speaker 1: to consider. So like it makes me question, like what 420 00:25:01,880 --> 00:25:04,399 Speaker 1: AI means? I don't think it's the same in every case. 421 00:25:04,720 --> 00:25:07,440 Speaker 1: It's not like this computer has such quote unquote artificial 422 00:25:07,440 --> 00:25:11,080 Speaker 1: intelligence that it can just automatically dissect, you know, court 423 00:25:11,160 --> 00:25:15,280 Speaker 1: transcripts without any extra you know, parameters and just figure out, 424 00:25:15,440 --> 00:25:17,760 Speaker 1: you know, where the justice lies. It has to have 425 00:25:17,800 --> 00:25:20,440 Speaker 1: a set of instructions that it's operating from which are 426 00:25:20,600 --> 00:25:25,080 Speaker 1: inherently um influenced by humans. Right, And then this is 427 00:25:25,080 --> 00:25:27,280 Speaker 1: something we'll talk about a little later. To one of 428 00:25:27,320 --> 00:25:31,480 Speaker 1: the big questions is is this simply automating a process? 429 00:25:31,840 --> 00:25:36,120 Speaker 1: For instance, I think that everyone can agree that if 430 00:25:36,200 --> 00:25:39,199 Speaker 1: you are in a spreadsheet or something or on some 431 00:25:39,280 --> 00:25:43,200 Speaker 1: kind of platform and you make like a listwide change, 432 00:25:43,720 --> 00:25:46,760 Speaker 1: then that's not really a I just told it to 433 00:25:46,840 --> 00:25:50,360 Speaker 1: change everything to three or whatever. I want to talk 434 00:25:50,359 --> 00:25:55,280 Speaker 1: a little bit about how about how misreported this story 435 00:25:55,359 --> 00:25:57,680 Speaker 1: has been in the West, because again there's a lot 436 00:25:57,680 --> 00:26:01,760 Speaker 1: of anti Chinese prejudice in the West. Prosecutors in China 437 00:26:02,080 --> 00:26:06,280 Speaker 1: were already using System two oh six for several years 438 00:26:06,680 --> 00:26:10,560 Speaker 1: to help figure out how to approach evidence to determine 439 00:26:10,600 --> 00:26:13,840 Speaker 1: whether or not a suspected criminal was a danger to 440 00:26:13,880 --> 00:26:17,840 Speaker 1: the public at large. But this was pretty limited up 441 00:26:17,840 --> 00:26:21,080 Speaker 1: to now. All System to A six could do was 442 00:26:21,280 --> 00:26:26,359 Speaker 1: corelate that information, make some indications and recommendations. It could 443 00:26:26,400 --> 00:26:29,680 Speaker 1: not quote this from the management review paper, could not 444 00:26:29,880 --> 00:26:33,800 Speaker 1: participate in the decision making process of filing charges and 445 00:26:33,920 --> 00:26:37,720 Speaker 1: suggesting sentences. And when we talked about sentences, we're not 446 00:26:37,720 --> 00:26:40,480 Speaker 1: talking about the quick brown fox jumps over the lazy dog. 447 00:26:40,720 --> 00:26:43,520 Speaker 1: We're talking about prison time. And I think we can 448 00:26:43,560 --> 00:26:45,320 Speaker 1: all agree a lot of people don't want, you know, 449 00:26:45,480 --> 00:26:49,160 Speaker 1: judge a Tron nine thousand to decide whether they get 450 00:26:49,200 --> 00:26:53,119 Speaker 1: five years of hard time. And that's what's happening because 451 00:26:54,320 --> 00:26:58,479 Speaker 1: the AI to be able to suggest sentences would have 452 00:26:58,560 --> 00:27:02,159 Speaker 1: to be able to identify and remove information in a 453 00:27:02,240 --> 00:27:05,399 Speaker 1: case that it deems irrelevant, and then it would be 454 00:27:05,560 --> 00:27:08,520 Speaker 1: it would also need to process human language in its 455 00:27:08,560 --> 00:27:12,760 Speaker 1: neural network. This is it would also be based on 456 00:27:12,960 --> 00:27:17,040 Speaker 1: the reporting of a human who's writing down facts about 457 00:27:17,080 --> 00:27:22,120 Speaker 1: the case right for now. And this, this is where 458 00:27:22,160 --> 00:27:24,960 Speaker 1: we're at. This is the precipice. And this means that 459 00:27:25,040 --> 00:27:28,400 Speaker 1: for the first time in human or AI history, there 460 00:27:28,480 --> 00:27:31,840 Speaker 1: is now a machine that can use these processes to 461 00:27:31,960 --> 00:27:36,600 Speaker 1: not just not just put the final stamp on existing charges, 462 00:27:36,920 --> 00:27:41,320 Speaker 1: but to actually charge people with crimes. And I get 463 00:27:41,320 --> 00:27:43,480 Speaker 1: the feeling. All three of us definitely want to talk 464 00:27:43,520 --> 00:27:47,120 Speaker 1: about how stuff works. That's one for all our old 465 00:27:47,119 --> 00:27:50,679 Speaker 1: friends there. The this machine was trained, and there's a 466 00:27:50,720 --> 00:27:54,320 Speaker 1: way a lot of these pieces of tach get trained. 467 00:27:54,520 --> 00:27:58,560 Speaker 1: It was fed more than seventeen thousand cases, all distinct, 468 00:27:58,640 --> 00:28:03,240 Speaker 1: separate cases, from twenty fifteen up to and it would 469 00:28:03,280 --> 00:28:07,080 Speaker 1: capture things like everything from the metadata to ultimately the 470 00:28:07,160 --> 00:28:10,200 Speaker 1: content of what was happening, the time that something occurred, 471 00:28:10,400 --> 00:28:14,840 Speaker 1: the place, the region, the people, the individuals, their past behavior, 472 00:28:14,960 --> 00:28:18,720 Speaker 1: their current behavior, and then we'll calculate consequences. And so 473 00:28:18,760 --> 00:28:22,800 Speaker 1: far it can identify and press charges for Shanghai's eight 474 00:28:22,920 --> 00:28:26,280 Speaker 1: most common crimes, and this we're gonna give you the 475 00:28:26,280 --> 00:28:29,760 Speaker 1: eight most common crimes. I think they paint interesting picture 476 00:28:30,000 --> 00:28:34,720 Speaker 1: of crime and fun times in Shanghai. They're fun, every 477 00:28:34,720 --> 00:28:38,440 Speaker 1: one of them, but a couple in particular. M Yeah, 478 00:28:38,600 --> 00:28:42,440 Speaker 1: credit card fraud, Okay, that's really common, running a gambling operation, 479 00:28:42,680 --> 00:28:48,040 Speaker 1: dangerous driving, intentional injury, obstructing official duties, theft, fraud, and 480 00:28:48,520 --> 00:28:53,280 Speaker 1: perhaps the most vague and dangerous quote, picking quarrels and 481 00:28:53,400 --> 00:28:58,920 Speaker 1: provoking trouble a k. A. Rabble rousing being being like 482 00:28:59,200 --> 00:29:01,880 Speaker 1: what do you call it? Um? Being like an agitator? 483 00:29:02,160 --> 00:29:05,760 Speaker 1: Is that what we're talking about here? And ne'er do well? Yeah, 484 00:29:05,760 --> 00:29:08,080 Speaker 1: it could be. I mean, it could be something pretty serious, 485 00:29:09,480 --> 00:29:13,120 Speaker 1: serious assault, or it could be like, you know, spreading 486 00:29:14,320 --> 00:29:16,680 Speaker 1: about the party. But there's there but but but why 487 00:29:16,680 --> 00:29:21,240 Speaker 1: don't they call it serious assault? Like there's obvious to everybody, 488 00:29:21,240 --> 00:29:23,959 Speaker 1: there's in this very nature of this charge. It's up 489 00:29:24,000 --> 00:29:26,800 Speaker 1: to it's the in the eye of the beholder. You know, 490 00:29:27,160 --> 00:29:28,400 Speaker 1: I don't know what to call it, but I know 491 00:29:28,440 --> 00:29:30,600 Speaker 1: what it is when I see it, right, Like the 492 00:29:30,600 --> 00:29:33,440 Speaker 1: old ruling about pornography in the US, I think the 493 00:29:33,520 --> 00:29:36,880 Speaker 1: serious assault would fall under intentional injury in a more 494 00:29:36,920 --> 00:29:42,280 Speaker 1: extreme way, maybe like um provoking trouble might be something 495 00:29:42,320 --> 00:29:47,479 Speaker 1: as simple as passing out following Gong literature, which you know, 496 00:29:47,600 --> 00:29:50,800 Speaker 1: which is Oh, by the way, guys, Shenyan is back 497 00:29:50,800 --> 00:29:53,040 Speaker 1: in town. If you if you want to see the show, 498 00:29:54,440 --> 00:29:56,080 Speaker 1: have you seen that meme where it's like a shen 499 00:29:56,160 --> 00:29:59,760 Speaker 1: Yan billboard on the moon? I have not, but I 500 00:29:59,840 --> 00:30:02,320 Speaker 1: not surprised it might be a real photo. They know 501 00:30:02,440 --> 00:30:04,120 Speaker 1: they're trying to get ahead of a lot of stuff. 502 00:30:04,200 --> 00:30:07,200 Speaker 1: The promotion game is on point. But my other question is, 503 00:30:07,240 --> 00:30:10,560 Speaker 1: like running a gambling operation, is that such a common 504 00:30:10,600 --> 00:30:13,840 Speaker 1: crime that someone's like, we do not have enough prosecutors 505 00:30:13,880 --> 00:30:16,520 Speaker 1: for this, we need a robot, and that that that 506 00:30:16,680 --> 00:30:20,640 Speaker 1: it is because it's outlawed as a practice, you know, 507 00:30:21,160 --> 00:30:25,200 Speaker 1: like large like there's no scenario in which gambling is legal. 508 00:30:25,200 --> 00:30:29,640 Speaker 1: Am I right? Or is this yeah? For me? It's 509 00:30:29,680 --> 00:30:32,720 Speaker 1: you If you multiply the population of the United States 510 00:30:32,760 --> 00:30:35,600 Speaker 1: by four, you get China's population. When you've got jurisdiction 511 00:30:35,640 --> 00:30:38,360 Speaker 1: over that many human beings, I can only imagine that 512 00:30:38,360 --> 00:30:41,880 Speaker 1: that is still a problem just with that many human 513 00:30:41,880 --> 00:30:46,320 Speaker 1: beings around. People like the gamble. Well, yeah, gambling or 514 00:30:46,440 --> 00:30:51,360 Speaker 1: casino gambling specifically is illegal in mainland China, but it 515 00:30:51,640 --> 00:30:54,920 Speaker 1: is legal in the Cow, and the Cow is the 516 00:30:54,920 --> 00:30:58,720 Speaker 1: world's biggest gambling hub. That's where the richies go when 517 00:30:58,720 --> 00:31:00,800 Speaker 1: they like, you know, they always talk here about like 518 00:31:01,080 --> 00:31:03,360 Speaker 1: I think that was even a joke in succession, Like uh, 519 00:31:03,440 --> 00:31:06,400 Speaker 1: Alexander Scar Scar's character, who's like a you know, Google 520 00:31:06,440 --> 00:31:08,880 Speaker 1: type dude, like does a tweet about and going to 521 00:31:08,960 --> 00:31:11,480 Speaker 1: Macau and it implies that he's like got a lot 522 00:31:11,480 --> 00:31:15,280 Speaker 1: of money coming on the horizon. Oh yeah, Macau or Monico, right, 523 00:31:15,320 --> 00:31:18,160 Speaker 1: But Monica always seemed like the more expensive of the two. 524 00:31:18,720 --> 00:31:22,080 Speaker 1: It was Macau because it was specifically like about gambling. 525 00:31:23,200 --> 00:31:26,920 Speaker 1: Monica is more like a vacation destination, right, And I 526 00:31:27,000 --> 00:31:29,400 Speaker 1: might be wrong. Well, there's a lot of gambling Monica too. 527 00:31:29,400 --> 00:31:32,000 Speaker 1: It's a rich person's paradise, is what it's supposed to be. 528 00:31:32,080 --> 00:31:35,040 Speaker 1: But but we'll get to Monica in a future episode. 529 00:31:35,160 --> 00:31:37,120 Speaker 1: Tell us if you'd like, if you've got some dirt 530 00:31:37,160 --> 00:31:39,800 Speaker 1: on there, we'd love to hear it. Uh. With with this, 531 00:31:40,200 --> 00:31:44,040 Speaker 1: we know, okay that we're essentially talking about something based 532 00:31:44,120 --> 00:31:47,720 Speaker 1: on this pre existing thing called System two thousand and six. 533 00:31:48,360 --> 00:31:52,680 Speaker 1: And if you just cast aside all concerns, right, and 534 00:31:52,840 --> 00:31:55,080 Speaker 1: just look at it the way you would look at 535 00:31:55,320 --> 00:32:00,000 Speaker 1: a fancy new car. This new version of system, to say, 536 00:32:00,240 --> 00:32:04,760 Speaker 1: can do some pretty impressive things in real time. Yes, 537 00:32:05,240 --> 00:32:07,760 Speaker 1: and it really does take you back to the civil 538 00:32:07,800 --> 00:32:11,960 Speaker 1: law system. Right. We said there are rules that if 539 00:32:12,000 --> 00:32:15,480 Speaker 1: they're broken there there's there's input in, there's output, and 540 00:32:15,560 --> 00:32:19,200 Speaker 1: it's exacting. It's not an interpretation of some judge. So 541 00:32:19,440 --> 00:32:22,160 Speaker 1: so let's get into this thing. Can transcribe as suspects 542 00:32:22,200 --> 00:32:25,920 Speaker 1: testimony at hearings. That's good, right. It can recognize the 543 00:32:25,960 --> 00:32:30,280 Speaker 1: identity of that person based on conversations in a courtroom. 544 00:32:30,360 --> 00:32:34,640 Speaker 1: It can transfer large amounts of legal documents, which is good, right, 545 00:32:35,360 --> 00:32:39,520 Speaker 1: Like take the actual legal documents, turn them into something digitized, 546 00:32:39,840 --> 00:32:42,640 Speaker 1: mark them up. Now you've got that ready for the 547 00:32:42,720 --> 00:32:47,959 Speaker 1: prosecutor or or for the protector. I can't remember. They 548 00:32:47,960 --> 00:32:52,800 Speaker 1: can also identify defective evidence to avoid wrongful convictions. That's good, right, 549 00:32:53,480 --> 00:32:55,840 Speaker 1: there's something wrong with this evidence that doesn't match up 550 00:32:55,840 --> 00:32:57,920 Speaker 1: the way it should be and as it's normally entered. 551 00:32:58,320 --> 00:33:01,520 Speaker 1: I mean that's those are pretty good things, I think, 552 00:33:02,120 --> 00:33:05,040 Speaker 1: But it doesn't seem like something at the moment that 553 00:33:05,080 --> 00:33:09,120 Speaker 1: I would trust with, you know, deciding who's going to 554 00:33:09,240 --> 00:33:11,560 Speaker 1: get charged with something. It just feels like what it 555 00:33:11,600 --> 00:33:14,600 Speaker 1: was used for in the past. Yeah, exactly. And then 556 00:33:15,320 --> 00:33:17,920 Speaker 1: this system can also This is like one of the 557 00:33:17,960 --> 00:33:20,000 Speaker 1: most sci fi parts to me. This system can also 558 00:33:20,120 --> 00:33:24,120 Speaker 1: respond of voice commands. It can display evidence and info 559 00:33:24,360 --> 00:33:28,320 Speaker 1: on digital screens for different people in a courtroom at 560 00:33:28,320 --> 00:33:33,480 Speaker 1: a hearing, so it saves time presenting evidence. Some folks, 561 00:33:34,040 --> 00:33:37,440 Speaker 1: like Ma Chong Chan, professor at East China University of 562 00:33:37,440 --> 00:33:40,760 Speaker 1: Political Science Law in Shanghai, as some folks see it 563 00:33:40,800 --> 00:33:45,240 Speaker 1: as a vast improvement over the old ways. Uh. Ma 564 00:33:45,480 --> 00:33:49,480 Speaker 1: Changshan points out that, for instance, a suspect might make 565 00:33:49,600 --> 00:33:54,000 Speaker 1: multiple confessions over some interval of time, and that the 566 00:33:54,160 --> 00:34:00,000 Speaker 1: AI system could instantly detect contradictions when comparing and contract 567 00:34:00,000 --> 00:34:03,360 Speaker 1: sting those statements. Uh. And the scary thing is again 568 00:34:03,360 --> 00:34:06,880 Speaker 1: the way the West is reporting this is is odd 569 00:34:07,040 --> 00:34:10,880 Speaker 1: in that it's been reported as though this just happened. 570 00:34:11,280 --> 00:34:14,080 Speaker 1: Some parts of this just happened, But this is only 571 00:34:14,120 --> 00:34:17,520 Speaker 1: a new chapter in a much longer story. China has 572 00:34:17,560 --> 00:34:21,040 Speaker 1: been working in this field since at least the mids. 573 00:34:21,120 --> 00:34:25,480 Speaker 1: The first public inklings of this technology are surfacing around 574 00:34:26,480 --> 00:34:29,880 Speaker 1: and back. In January of twenty nineteen, the first version 575 00:34:30,080 --> 00:34:33,640 Speaker 1: of System two oh six went into action. It was 576 00:34:33,760 --> 00:34:38,319 Speaker 1: deployed in Shanghai Number two Intermediate People's Court. Like we 577 00:34:38,320 --> 00:34:41,759 Speaker 1: were saying, this is already a thing, and this is 578 00:34:41,840 --> 00:34:46,600 Speaker 1: one example of the larger push toward artificial intelligence. And 579 00:34:46,719 --> 00:34:49,600 Speaker 1: also I want to say, um right now, I want 580 00:34:49,600 --> 00:34:52,040 Speaker 1: to say thank you to you, Matt, because we did 581 00:34:52,160 --> 00:34:55,040 Speaker 1: need to hold this, uh and make it an episode. 582 00:34:55,560 --> 00:34:58,160 Speaker 1: So I think we all we all picked this up 583 00:34:58,800 --> 00:35:00,799 Speaker 1: when it when it first came out, and I don't 584 00:35:00,800 --> 00:35:02,799 Speaker 1: know about you, but I was surprised that there was 585 00:35:02,880 --> 00:35:05,880 Speaker 1: much more to the story. This is not this is 586 00:35:05,920 --> 00:35:09,000 Speaker 1: not like a white board prototype. At this point, it 587 00:35:09,200 --> 00:35:13,399 Speaker 1: is well past that. Um. So people are saying, hey, 588 00:35:13,440 --> 00:35:16,000 Speaker 1: this is good, let's make people's lives easier. They're even 589 00:35:16,040 --> 00:35:19,960 Speaker 1: saying get this, that it will make a less biased system. 590 00:35:20,000 --> 00:35:23,120 Speaker 1: But what about the critics? Is this all well and good? 591 00:35:23,400 --> 00:35:27,400 Speaker 1: Spoiler alert? Yeah, not really. We're gonna pause for a 592 00:35:27,480 --> 00:35:30,839 Speaker 1: word from our sponsor and we'll return to explore some 593 00:35:31,000 --> 00:35:35,520 Speaker 1: of the criticism of this new system, which I think 594 00:35:35,560 --> 00:35:39,280 Speaker 1: some of our fellow conspiracy realists are already uh talking 595 00:35:39,320 --> 00:35:49,680 Speaker 1: about out loud to themselves. Quite possibly, we're back. Surprise, 596 00:35:50,000 --> 00:35:54,640 Speaker 1: not everybody's on board. What critics are in particularly concerned 597 00:35:54,880 --> 00:35:58,360 Speaker 1: with the system's accuracy and lack thereof, and that's something 598 00:35:58,440 --> 00:36:02,120 Speaker 1: we really really need to talk talk about. Researchers are 599 00:36:02,200 --> 00:36:06,080 Speaker 1: over the moon about this new version. System two oh six, 600 00:36:06,520 --> 00:36:10,600 Speaker 1: according to their studies and their conclusions, has a nineties 601 00:36:10,640 --> 00:36:14,239 Speaker 1: seven percent accuracy rates. That means that it can use 602 00:36:14,320 --> 00:36:18,560 Speaker 1: this aggregate description, this conglomeration of those thousand or so 603 00:36:18,680 --> 00:36:23,239 Speaker 1: traits on a suspective criminal case to correctly file a 604 00:36:23,400 --> 00:36:29,880 Speaker 1: charge of the time. That's pretty good, that's admirable. Yeah, 605 00:36:30,080 --> 00:36:33,440 Speaker 1: unless you're in that three percent margin of error. You 606 00:36:33,480 --> 00:36:35,920 Speaker 1: know that they were not talking about three percent chance 607 00:36:36,000 --> 00:36:40,000 Speaker 1: of getting the wrong concert set or having your flight 608 00:36:40,040 --> 00:36:42,480 Speaker 1: info and correct. We're talking about three percent chance of 609 00:36:43,520 --> 00:36:49,080 Speaker 1: it is and people going to prison exactly. And I 610 00:36:49,200 --> 00:36:50,719 Speaker 1: haven't really gotten into this yet, and I'm not sure 611 00:36:50,760 --> 00:36:55,200 Speaker 1: if we necessarily know fully, but like these wouldn't be 612 00:36:55,320 --> 00:37:00,160 Speaker 1: used for crimes of significantly high severity with like you know, 613 00:37:01,200 --> 00:37:03,040 Speaker 1: life and death on the line, right, I mean, this 614 00:37:03,080 --> 00:37:07,680 Speaker 1: would be for the specific set of crimes that maybe 615 00:37:07,719 --> 00:37:12,520 Speaker 1: are like low prison sentences or hefty fines or how 616 00:37:12,880 --> 00:37:16,120 Speaker 1: extreme do the types of punishments that these things are 617 00:37:16,239 --> 00:37:19,319 Speaker 1: used for go. Yeah, it's a good question because it's 618 00:37:19,320 --> 00:37:22,279 Speaker 1: still we're working with live fire here. This is a 619 00:37:22,320 --> 00:37:25,719 Speaker 1: fluid situation right now. It's just those eight crimes. But 620 00:37:25,880 --> 00:37:28,520 Speaker 1: I would you know, point out, as we were saying earlier, 621 00:37:28,560 --> 00:37:32,799 Speaker 1: that one of those is troublingly vague, you know what 622 00:37:32,840 --> 00:37:36,520 Speaker 1: I mean, Even intentional injury is troublingly vague. Did you 623 00:37:36,560 --> 00:37:39,600 Speaker 1: punch someone in a fight, ay to karaoke bar, or 624 00:37:39,640 --> 00:37:42,520 Speaker 1: did you like remove their ability to walk for the 625 00:37:42,560 --> 00:37:46,320 Speaker 1: foreseeable future? You know, there are two different things. But um, 626 00:37:46,360 --> 00:37:49,520 Speaker 1: at this point, you know, the most dangerous thing is 627 00:37:49,880 --> 00:37:52,320 Speaker 1: what is figuring out what the punishment would be for, 628 00:37:52,440 --> 00:37:57,279 Speaker 1: you know, promoting dissenter, picking quarrels and causing trouble. That's right, 629 00:37:57,320 --> 00:37:58,920 Speaker 1: because that's the thing I mean. I think a lot 630 00:37:58,960 --> 00:38:01,480 Speaker 1: of the critics argued this could be like I think 631 00:38:01,480 --> 00:38:04,400 Speaker 1: the word that I've seen is weaponized by the government 632 00:38:04,800 --> 00:38:07,879 Speaker 1: where it can be used to like you know, pick 633 00:38:07,920 --> 00:38:11,799 Speaker 1: out like malcontents based on you know, or enemies of 634 00:38:11,840 --> 00:38:15,440 Speaker 1: the state, based on this very draconian set of conditions 635 00:38:15,840 --> 00:38:19,160 Speaker 1: with but but but then again, is this that much 636 00:38:19,200 --> 00:38:21,400 Speaker 1: different than the way the government already does it with 637 00:38:21,440 --> 00:38:24,440 Speaker 1: a bureaucracy that size, And is the margin of error 638 00:38:24,680 --> 00:38:29,759 Speaker 1: higher or lower for the AI versus the actual overworked bureaucrats. 639 00:38:31,000 --> 00:38:34,400 Speaker 1: That's the difficult question because there's not a lot of 640 00:38:34,440 --> 00:38:38,719 Speaker 1: transparency in that regard. It's sort of like how uh, 641 00:38:38,920 --> 00:38:42,800 Speaker 1: the Japanese police have an incredibly high clearance rate on crimes, 642 00:38:43,000 --> 00:38:45,359 Speaker 1: higher than the two US has ever gotten to, you know, 643 00:38:45,640 --> 00:38:47,920 Speaker 1: And part of that goes down to the question of 644 00:38:47,960 --> 00:38:52,840 Speaker 1: self assessment or lack thereof, within these organizations. Who again, 645 00:38:52,880 --> 00:38:56,720 Speaker 1: who watches the watchman? Uh? Indie? In the South China 646 00:38:56,760 --> 00:39:00,239 Speaker 1: Morning Post article that you mentioned earlier, matt Uh, as 647 00:39:00,280 --> 00:39:04,320 Speaker 1: well as in some other articles I have found digging 648 00:39:04,360 --> 00:39:08,080 Speaker 1: into this, there's something really telling that happens. There is 649 00:39:08,120 --> 00:39:12,440 Speaker 1: a prosecutor who levels some criticism about this. This prosecutor 650 00:39:12,520 --> 00:39:15,640 Speaker 1: is based in Shanghai. This prosecutor has chosen to do 651 00:39:15,719 --> 00:39:19,160 Speaker 1: their best to remain anonymous, which I think also gives 652 00:39:19,200 --> 00:39:23,640 Speaker 1: you some insight into the fear that people have about 653 00:39:23,680 --> 00:39:27,600 Speaker 1: speaking out around this thing. The prosecutor said, the issue sensitive. 654 00:39:27,640 --> 00:39:30,399 Speaker 1: They don't want to talk about it, uh, with their 655 00:39:30,520 --> 00:39:32,920 Speaker 1: they want to talk about on record. But they pointed 656 00:39:32,960 --> 00:39:34,480 Speaker 1: out something I think a lot of us have been 657 00:39:34,520 --> 00:39:38,560 Speaker 1: thinking about, which is this, the accuracy of may be 658 00:39:38,719 --> 00:39:41,359 Speaker 1: high from a technological point of view, but there will 659 00:39:41,400 --> 00:39:44,160 Speaker 1: always be a chance of a mistake. And so this 660 00:39:44,239 --> 00:39:48,120 Speaker 1: prosecutor said, who takes responsibility when that happens. Is that 661 00:39:48,239 --> 00:39:51,000 Speaker 1: the prosecutor is that the machine, is that the person 662 00:39:51,040 --> 00:39:53,719 Speaker 1: who designed the algorithm. It's like the question of whether 663 00:39:53,800 --> 00:39:57,080 Speaker 1: or not an AI can have its own patent. But 664 00:39:57,200 --> 00:40:00,880 Speaker 1: it's a dangerous important question, Uh, because I don't know 665 00:40:00,920 --> 00:40:03,560 Speaker 1: about you guys, but I don't think I don't think 666 00:40:03,560 --> 00:40:06,600 Speaker 1: anybody's gonna be able to really address that until the 667 00:40:06,719 --> 00:40:10,000 Speaker 1: legal rubber hits the road, which would mean that several 668 00:40:10,040 --> 00:40:12,800 Speaker 1: things have to happen. Are if then in that case, 669 00:40:12,960 --> 00:40:18,320 Speaker 1: is someone is wrongfully convicted, they win that terrible brutal 670 00:40:18,360 --> 00:40:20,960 Speaker 1: lottery and they're one of the three percent right they 671 00:40:21,000 --> 00:40:25,960 Speaker 1: didn't actually intentionally injure somebody, They won't quourly or you know, 672 00:40:26,760 --> 00:40:31,400 Speaker 1: raising trouble, but they're wrongfully convicted, they're going to jail, 673 00:40:31,520 --> 00:40:33,760 Speaker 1: or they're paying some massive fine. Like you said, Matt, 674 00:40:34,680 --> 00:40:37,120 Speaker 1: The next if thing is they would have to be 675 00:40:37,160 --> 00:40:41,600 Speaker 1: able to somehow fight back in court, and that's kind 676 00:40:41,640 --> 00:40:44,560 Speaker 1: of difficult, right, how do you how do you do that? 677 00:40:44,600 --> 00:40:47,360 Speaker 1: How do you get representation there? So it's gonna be 678 00:40:47,400 --> 00:40:53,160 Speaker 1: a while before someone can answer that anonymous prosecutors question. Also, 679 00:40:53,200 --> 00:40:57,279 Speaker 1: the prosecutor says, you know, if we have I want 680 00:40:57,280 --> 00:40:59,080 Speaker 1: to ask you guys this, I want to ask if 681 00:40:59,160 --> 00:41:01,200 Speaker 1: you think, based on the fact that we don't know 682 00:41:01,360 --> 00:41:05,360 Speaker 1: anything about this person, um, do you think there's ego 683 00:41:05,400 --> 00:41:09,279 Speaker 1: at play here? When the prosecutor says direct involvement of 684 00:41:09,320 --> 00:41:15,160 Speaker 1: AI and decision making can affect the autonomy of human prosecutors, uh, 685 00:41:15,280 --> 00:41:20,320 Speaker 1: this person said, most prosecutors don't want computer scientists meddling 686 00:41:20,400 --> 00:41:23,759 Speaker 1: in our legal judgments. Where do you think that's coming from? 687 00:41:23,960 --> 00:41:28,080 Speaker 1: Fear for their job security? Sure, I mean that's got 688 00:41:28,080 --> 00:41:32,080 Speaker 1: to be at play here to some extent rights it 689 00:41:32,160 --> 00:41:34,799 Speaker 1: must be a I don't know how pay works there, 690 00:41:34,800 --> 00:41:38,759 Speaker 1: but it must be like a respected position to be 691 00:41:38,960 --> 00:41:41,520 Speaker 1: a prosecutor, right, or to be hard to pronounce name 692 00:41:41,600 --> 00:41:49,280 Speaker 1: like procractrotect, direct. And so then this prosecutor and people 693 00:41:49,280 --> 00:41:52,840 Speaker 1: who are criticizing this system are also aware of something 694 00:41:52,840 --> 00:41:55,920 Speaker 1: that we just brought up the old Lovelace dilemma, which 695 00:41:56,080 --> 00:42:01,360 Speaker 1: is that this thing can only file charge based on 696 00:42:01,440 --> 00:42:07,160 Speaker 1: its previous experience. It can't foresee a public reaction to 697 00:42:07,239 --> 00:42:12,000 Speaker 1: a case in a fluid, social, human populated environment, And 698 00:42:12,040 --> 00:42:16,280 Speaker 1: so the prosecutor concludes AI might help us detect mistakes, 699 00:42:16,320 --> 00:42:21,200 Speaker 1: but it cannot replace humans in making a decision. So yeah, 700 00:42:21,239 --> 00:42:23,400 Speaker 1: I think most people can agree with that now, But 701 00:42:23,520 --> 00:42:25,960 Speaker 1: why all the hubbub Like, why all the hubbats. I 702 00:42:26,000 --> 00:42:30,560 Speaker 1: guess that's not worth it, thank you. But but but I mean, 703 00:42:30,560 --> 00:42:32,200 Speaker 1: we talked about at the top of the show how 704 00:42:32,280 --> 00:42:36,480 Speaker 1: overloaded every you know court system in the freaking universe 705 00:42:36,680 --> 00:42:39,400 Speaker 1: is just by the nature of what it is, um 706 00:42:39,480 --> 00:42:41,520 Speaker 1: and and how many people it touches, you know, in 707 00:42:41,520 --> 00:42:45,280 Speaker 1: a society. Do you think that in a perfect world, 708 00:42:45,320 --> 00:42:48,120 Speaker 1: this could just take some of the lower level cases 709 00:42:48,200 --> 00:42:52,160 Speaker 1: off of these you know, high level officials or these 710 00:42:52,200 --> 00:42:55,080 Speaker 1: you know lawyers um, since I can't pronounce that word um, 711 00:42:55,160 --> 00:42:57,400 Speaker 1: and actually free them up to do the kind of 712 00:42:57,440 --> 00:43:00,360 Speaker 1: case work that really does require more new wants and 713 00:43:00,640 --> 00:43:03,600 Speaker 1: intellect and human touch. I don't necessarily think this is 714 00:43:03,640 --> 00:43:06,160 Speaker 1: a job killer in the way that it's being pitched, 715 00:43:06,160 --> 00:43:09,279 Speaker 1: but the way it's being pitched could also be I mean, 716 00:43:09,360 --> 00:43:12,880 Speaker 1: in a perfect world, yeah, that'd be awesome, right, that 717 00:43:12,920 --> 00:43:16,640 Speaker 1: would free up a lot of people. But there are 718 00:43:16,880 --> 00:43:19,560 Speaker 1: we do need to talk about why people are up 719 00:43:19,560 --> 00:43:22,200 Speaker 1: in arms about this, and I would say at the top, 720 00:43:22,920 --> 00:43:24,520 Speaker 1: I don't know if we'll agree, but I think some 721 00:43:24,560 --> 00:43:28,880 Speaker 1: of the reasons and concerns are much more valid than others. Like, first, yes, 722 00:43:29,040 --> 00:43:31,960 Speaker 1: there is a lot of prejudice against China, especially in 723 00:43:32,000 --> 00:43:38,360 Speaker 1: the West. It's a dystopian AI prosecutor is perfect red meat. 724 00:43:38,400 --> 00:43:41,560 Speaker 1: It's the perfect juice for the anti China crowd. And 725 00:43:41,600 --> 00:43:47,200 Speaker 1: I'll like, like, that's intensely problematic. So this system is 726 00:43:47,320 --> 00:43:51,480 Speaker 1: again it's making determinations based on past information, which is 727 00:43:51,520 --> 00:43:54,440 Speaker 1: honestly also a thing that humans do all the time. 728 00:43:54,800 --> 00:43:58,239 Speaker 1: It's it's regurgitating what it's been fed, and it's been 729 00:43:58,239 --> 00:44:01,239 Speaker 1: fed as much as possible. There are big questions about 730 00:44:01,280 --> 00:44:04,000 Speaker 1: who decides what info to give it and how does 731 00:44:04,040 --> 00:44:07,600 Speaker 1: that shape its decision, And that's I mean, that's that 732 00:44:07,680 --> 00:44:11,680 Speaker 1: transparent answer, and it's not. No, it's not. Um. It 733 00:44:11,760 --> 00:44:14,440 Speaker 1: might be transparent in terms of the type of information, 734 00:44:14,760 --> 00:44:16,279 Speaker 1: but the public is not going to know a lot 735 00:44:16,280 --> 00:44:18,239 Speaker 1: of that. That's why I keep bringing up that there's 736 00:44:18,320 --> 00:44:22,719 Speaker 1: human element before the AI gets involved. So, like you know, 737 00:44:23,080 --> 00:44:27,600 Speaker 1: in each individual case, what it's being fed is based 738 00:44:27,640 --> 00:44:32,520 Speaker 1: upon how well someone performs their duties right um each 739 00:44:32,600 --> 00:44:34,920 Speaker 1: each time, each iteration, and the thought is you do 740 00:44:34,960 --> 00:44:38,160 Speaker 1: that enough times that it can have the best view 741 00:44:38,400 --> 00:44:41,560 Speaker 1: over a wide you know, number of cases as possible, 742 00:44:42,200 --> 00:44:46,600 Speaker 1: but still in an autonomous car to drive. Actually it 743 00:44:46,600 --> 00:44:49,040 Speaker 1: reminds me of I don't remember exactly the case, but 744 00:44:49,120 --> 00:44:53,799 Speaker 1: there was a city government that was using UM either 745 00:44:53,920 --> 00:44:56,759 Speaker 1: city government or a corporation or a group of corporations 746 00:44:56,760 --> 00:45:00,319 Speaker 1: that was using AI to sift through job applications, and 747 00:45:00,520 --> 00:45:05,040 Speaker 1: the AI inherently was sexist and maybe a little racist 748 00:45:05,160 --> 00:45:09,200 Speaker 1: because it was using legacy information from back when people 749 00:45:09,239 --> 00:45:13,320 Speaker 1: were maybe more racist and sexist to determine what types 750 00:45:13,440 --> 00:45:17,120 Speaker 1: of UM you know, applicants would would rise to the top. 751 00:45:17,160 --> 00:45:21,280 Speaker 1: And they weren't basing it on race or sex necessarily directly, 752 00:45:21,600 --> 00:45:25,000 Speaker 1: but there are all these sub factors that would figure 753 00:45:25,040 --> 00:45:28,200 Speaker 1: in based on the legacy data that the systems were fed. 754 00:45:28,480 --> 00:45:32,040 Speaker 1: So it showed this inherent bias based on the type 755 00:45:32,040 --> 00:45:34,520 Speaker 1: of you know data that it was being fed in 756 00:45:34,560 --> 00:45:37,440 Speaker 1: the same way that the outcome of whatever cases were 757 00:45:37,520 --> 00:45:41,080 Speaker 1: chosen to be fed to this system would absolutely affect 758 00:45:41,120 --> 00:45:43,719 Speaker 1: the outcome. Well, yeah, that's that's the thing. I mean. 759 00:45:43,920 --> 00:45:45,840 Speaker 1: I put this in because I think it's one of 760 00:45:45,880 --> 00:45:48,759 Speaker 1: the most important parts of the conversation about any conversation 761 00:45:48,840 --> 00:45:51,640 Speaker 1: in AI or this sort of automation. I'm glad we've 762 00:45:51,640 --> 00:45:54,160 Speaker 1: anticipated it before we got to it and showed because 763 00:45:54,560 --> 00:45:59,160 Speaker 1: AI can, yes, regularly outperform human minds in multiple fields, 764 00:45:59,400 --> 00:46:01,960 Speaker 1: but it is still very much built by humans, and 765 00:46:02,040 --> 00:46:06,040 Speaker 1: as such, I would argue that means it inherently remains 766 00:46:06,239 --> 00:46:09,840 Speaker 1: vulnerable to human bias. They think about the problems that 767 00:46:09,880 --> 00:46:13,080 Speaker 1: occur in other automated systems like facial recognition in the 768 00:46:13,120 --> 00:46:16,560 Speaker 1: field of law enforcement. There is absolutely no reason that 769 00:46:16,640 --> 00:46:19,440 Speaker 1: this version of system two oh six should be considered 770 00:46:19,800 --> 00:46:22,319 Speaker 1: any exception to the rule. But a lot of people 771 00:46:22,360 --> 00:46:25,480 Speaker 1: aren't really talking about that as much as they should. Instead, 772 00:46:25,800 --> 00:46:28,800 Speaker 1: especially in the West, we're hearing people talk about uh 773 00:46:28,880 --> 00:46:34,560 Speaker 1: anti China concern. It's it's misleading, and folks, please, we're 774 00:46:34,560 --> 00:46:37,479 Speaker 1: not painting a situation where there are clear good guys here. 775 00:46:38,200 --> 00:46:40,959 Speaker 1: It's pretty rare actually for there to be clear good 776 00:46:40,960 --> 00:46:43,680 Speaker 1: guys and these kind of conversations. This is not some 777 00:46:43,719 --> 00:46:48,080 Speaker 1: sanctimonious defense of China. This is instead a point about hypocrisy. 778 00:46:48,120 --> 00:46:51,160 Speaker 1: Here's our Shamalan plot twist of the day. Check it 779 00:46:51,200 --> 00:46:55,000 Speaker 1: out here the italics. When we say this, legal systems 780 00:46:55,080 --> 00:46:59,879 Speaker 1: in other countries, including the United States, are also all 781 00:47:00,040 --> 00:47:04,799 Speaker 1: ready using AI to some degree. True story. So next 782 00:47:04,840 --> 00:47:06,840 Speaker 1: time you hear like a news report about this, so 783 00:47:06,960 --> 00:47:10,920 Speaker 1: you see a quick video about this, uh check, just 784 00:47:11,080 --> 00:47:13,120 Speaker 1: like keep that in the back of your mind. Are 785 00:47:13,120 --> 00:47:15,840 Speaker 1: they going to say anything about what's happening in the US? 786 00:47:16,320 --> 00:47:21,760 Speaker 1: My uh, my experience sadly is no, they're not. Yeah. 787 00:47:22,120 --> 00:47:25,239 Speaker 1: So should we we move on to to the thing 788 00:47:25,400 --> 00:47:28,960 Speaker 1: that was announced ten or at least that we found 789 00:47:28,960 --> 00:47:33,960 Speaker 1: out about in Yeah, okay, cool. Uh. We learned about 790 00:47:33,960 --> 00:47:37,960 Speaker 1: this thing called the Public Safety Assessment. What is that? 791 00:47:37,960 --> 00:47:45,200 Speaker 1: That sounds great, That sounds normal, very innocuous. Yeah. This 792 00:47:45,320 --> 00:47:49,200 Speaker 1: is a software tool developed by Laura and John Arnold 793 00:47:49,320 --> 00:47:52,400 Speaker 1: to their foundation, the Laura and John Arnold Foundation. This 794 00:47:52,440 --> 00:47:55,480 Speaker 1: is based in Texas. It's a system that is designed 795 00:47:55,480 --> 00:47:59,000 Speaker 1: to give judges very similar thing to what system too 796 00:47:59,080 --> 00:48:01,520 Speaker 1: U six was supposed to do, to give judges the 797 00:48:01,560 --> 00:48:05,920 Speaker 1: most objective information available for a particular case so that 798 00:48:05,960 --> 00:48:10,480 Speaker 1: they can make a decision about someone moving through the system. Yeah. 799 00:48:10,520 --> 00:48:13,080 Speaker 1: And I like to bring up that important distinction, Matt, 800 00:48:13,160 --> 00:48:17,759 Speaker 1: because it's letting the judges ultimately make the decision. It's 801 00:48:17,800 --> 00:48:21,680 Speaker 1: not saying this guy was running and dirty game of 802 00:48:21,719 --> 00:48:25,319 Speaker 1: p knuckle or whatever people gamble on. Uh, black jack 803 00:48:25,440 --> 00:48:28,399 Speaker 1: might be more relevant. I don't know. I guess people 804 00:48:28,440 --> 00:48:31,040 Speaker 1: can gamble on anything. But the point is a state 805 00:48:31,080 --> 00:48:34,359 Speaker 1: judge in New Jersey now can use this p s 806 00:48:34,400 --> 00:48:38,680 Speaker 1: A to assist in making those pre trial decisions. And Matt, 807 00:48:38,719 --> 00:48:40,200 Speaker 1: you and I were talking about this a little bit 808 00:48:40,239 --> 00:48:44,920 Speaker 1: off air. Pre trial decisions for people who are fortunate 809 00:48:45,000 --> 00:48:47,040 Speaker 1: enough not to have to be aware of those are 810 00:48:47,120 --> 00:48:49,560 Speaker 1: things were like, let's say there's a teenager in the 811 00:48:49,640 --> 00:48:52,920 Speaker 1: US is while and out. You know, you're in high school, 812 00:48:52,920 --> 00:48:55,160 Speaker 1: you're not old enough to drink, you got caught with 813 00:48:55,239 --> 00:48:57,680 Speaker 1: some beers, you're trying to be too cool, you got 814 00:48:57,680 --> 00:49:01,200 Speaker 1: your wings clipped, and uh, the secutors say, well, you 815 00:49:01,239 --> 00:49:04,840 Speaker 1: know it's your first offense. Keep your nose clean. Instead 816 00:49:04,840 --> 00:49:08,759 Speaker 1: of putting you in the system and possibly ruining your 817 00:49:08,800 --> 00:49:13,040 Speaker 1: life or damaging your chances of success and adulthood, we're 818 00:49:13,040 --> 00:49:16,600 Speaker 1: gonna do a pre trial intervention, which means you check out, 819 00:49:16,680 --> 00:49:19,600 Speaker 1: you seem like a good kid, you gotta do community service, 820 00:49:19,640 --> 00:49:23,120 Speaker 1: you gotta like take a class, Your license is gonna 821 00:49:23,320 --> 00:49:26,000 Speaker 1: you know, have these different little caveats on it for 822 00:49:26,040 --> 00:49:30,880 Speaker 1: a year or so. That has like this can be 823 00:49:30,960 --> 00:49:35,200 Speaker 1: automated now, and judges in other states have used this system, 824 00:49:35,280 --> 00:49:37,520 Speaker 1: but New Jersey is getting the most pressed for it 825 00:49:37,680 --> 00:49:39,600 Speaker 1: um because they were sort of first to the post. 826 00:49:39,800 --> 00:49:41,239 Speaker 1: It just feels like those are the kinds of things 827 00:49:41,239 --> 00:49:44,799 Speaker 1: that should be automated, because you know, a system could 828 00:49:44,920 --> 00:49:49,520 Speaker 1: very easily look up a person's record, determine if something 829 00:49:49,600 --> 00:49:51,879 Speaker 1: was a first offense, and then every all that pre 830 00:49:51,960 --> 00:49:55,400 Speaker 1: trial intervention stuff is all by the numbers, Like do 831 00:49:55,480 --> 00:49:59,200 Speaker 1: we really need that clogging up a physical court appearance? 832 00:49:59,560 --> 00:50:02,359 Speaker 1: You know that that could be used for something more 833 00:50:02,680 --> 00:50:05,000 Speaker 1: serious that requires a little more oversight, because at the 834 00:50:05,080 --> 00:50:07,120 Speaker 1: end of the day, you're showing up. We've all been 835 00:50:07,160 --> 00:50:09,719 Speaker 1: to court, whether the traffic court or like you know, 836 00:50:09,800 --> 00:50:11,479 Speaker 1: some other thing that you have to kind of wait 837 00:50:11,520 --> 00:50:13,560 Speaker 1: in line for, whether jury duty or whatever. And the 838 00:50:13,560 --> 00:50:18,200 Speaker 1: whole process is incredibly slow and inefficient, as if as 839 00:50:18,200 --> 00:50:19,960 Speaker 1: efficient as they try to make it wouldn't it free 840 00:50:20,040 --> 00:50:22,560 Speaker 1: up some things? Theoretically if they just got rid of 841 00:50:22,600 --> 00:50:25,759 Speaker 1: all of those physical appearances and just had a system say, ope, 842 00:50:26,080 --> 00:50:28,879 Speaker 1: you the first offense, here's your thing, click the link, 843 00:50:29,200 --> 00:50:31,759 Speaker 1: do the thing, pay the thing. You're good, you don't 844 00:50:31,760 --> 00:50:33,680 Speaker 1: have to show up. I feel like it makes sense 845 00:50:33,680 --> 00:50:36,080 Speaker 1: and I'm with you there to an extent. There's other 846 00:50:36,120 --> 00:50:39,120 Speaker 1: things though, like the setting of bail. How much should 847 00:50:39,320 --> 00:50:41,840 Speaker 1: you know somebody have to pay to not go to 848 00:50:41,920 --> 00:50:45,640 Speaker 1: jail while they await their trial. How much is someone 849 00:50:45,680 --> 00:50:48,839 Speaker 1: a flight risk? I mean maybe you can assess that 850 00:50:49,160 --> 00:50:52,560 Speaker 1: objectively through an AI system, but I feel like, again, 851 00:50:52,560 --> 00:50:57,480 Speaker 1: it's a well, it's a whole weird thing anyway. Access. Yeah, 852 00:50:57,640 --> 00:51:01,319 Speaker 1: the access would have to have to the access For 853 00:51:01,400 --> 00:51:05,040 Speaker 1: an AI to make that decision with all the context 854 00:51:05,239 --> 00:51:08,360 Speaker 1: would be a clear violation of privacy laws in the US. 855 00:51:08,520 --> 00:51:12,359 Speaker 1: That's how you would know, like, hey, someone's uncle is 856 00:51:12,400 --> 00:51:16,759 Speaker 1: a millionaire and can clearly just fly them the hell 857 00:51:16,840 --> 00:51:18,919 Speaker 1: out of here and then they'll go to a non 858 00:51:18,920 --> 00:51:23,000 Speaker 1: extradition country like that. That can happen, and we can't 859 00:51:23,120 --> 00:51:29,080 Speaker 1: like the differences the US officially, Uh, what needs to 860 00:51:29,120 --> 00:51:34,000 Speaker 1: seem hesitant to break those walls? Of privacy and that 861 00:51:34,200 --> 00:51:37,880 Speaker 1: is not as much of a concern for you know, 862 00:51:38,000 --> 00:51:41,480 Speaker 1: the majority of cases in the Chinese government. And you 863 00:51:41,520 --> 00:51:45,360 Speaker 1: know it's gonna look again class courtrooms. Guys. Germany is 864 00:51:45,400 --> 00:51:50,120 Speaker 1: considering similar moves right as we record. And it is 865 00:51:50,760 --> 00:51:53,520 Speaker 1: whether you think it's great, whether you think it removes 866 00:51:53,719 --> 00:51:57,520 Speaker 1: bias or only automates it. We can easily predict other 867 00:51:57,560 --> 00:52:00,960 Speaker 1: countries are right now following suit where they will follow 868 00:52:01,000 --> 00:52:05,160 Speaker 1: suit in the future. And that's where that's where we 869 00:52:05,160 --> 00:52:08,439 Speaker 1: get to the bleeding edge stuff we're referencing earlier. So 870 00:52:09,760 --> 00:52:13,920 Speaker 1: the AI prosecutor like one of those characters, and dragon 871 00:52:13,960 --> 00:52:17,839 Speaker 1: ball Z is powering up. You know, it's like it's 872 00:52:17,920 --> 00:52:22,919 Speaker 1: gathering its power balls. It's gonna get continually upgraded. It's soon, 873 00:52:23,360 --> 00:52:26,360 Speaker 1: the professor, she says, it will soon be able to 874 00:52:26,400 --> 00:52:29,640 Speaker 1: recognize less common crimes. It will be able to file 875 00:52:29,760 --> 00:52:34,120 Speaker 1: multiple charges against one UH suspect. So for instance, let's 876 00:52:34,120 --> 00:52:37,200 Speaker 1: say someone has a crazy night and they do some 877 00:52:37,239 --> 00:52:40,080 Speaker 1: regrettable things. Then they can get hit with dangerous driving, 878 00:52:40,360 --> 00:52:42,480 Speaker 1: they can get hit with causing trouble, they can get 879 00:52:42,560 --> 00:52:45,080 Speaker 1: hit with intentional injury. You know what I mean. I 880 00:52:45,120 --> 00:52:47,400 Speaker 1: don't know, man, Maybe if your weekends. Crazy enough you 881 00:52:47,440 --> 00:52:52,000 Speaker 1: get all eight? Um, but uh, this this scope is 882 00:52:52,040 --> 00:52:55,840 Speaker 1: going to expand both still X axis, yeah, x axis 883 00:52:55,960 --> 00:52:59,120 Speaker 1: more regions and then the y axis of depth of 884 00:52:59,120 --> 00:53:01,000 Speaker 1: its ability. And I mean it feels like a lot 885 00:53:01,000 --> 00:53:03,160 Speaker 1: of this is like these are like slippery slope arguments, 886 00:53:03,239 --> 00:53:05,680 Speaker 1: like there are versions of this that could be useful, 887 00:53:06,000 --> 00:53:09,640 Speaker 1: but we don't trust the implement towards of this stuff 888 00:53:09,680 --> 00:53:12,680 Speaker 1: to do it right, you know, because it becomes a 889 00:53:12,760 --> 00:53:15,759 Speaker 1: question of a how much do you trust AI? Like 890 00:53:15,880 --> 00:53:18,239 Speaker 1: is this something that you you feel confident enough that 891 00:53:18,280 --> 00:53:20,799 Speaker 1: it's at an advanced enough level to do this kind 892 00:53:20,840 --> 00:53:22,839 Speaker 1: of stuff and not worry about it and be do 893 00:53:22,880 --> 00:53:25,960 Speaker 1: you trust the implementers of said AI to use it 894 00:53:26,000 --> 00:53:30,000 Speaker 1: as advertised? Um? I think maybe for me it's a 895 00:53:30,080 --> 00:53:32,360 Speaker 1: yes to number one and a note to number two. 896 00:53:32,640 --> 00:53:34,920 Speaker 1: And that's where the weirdness comes in for me. I 897 00:53:34,960 --> 00:53:37,359 Speaker 1: don't know what do you guys think? Well? I mean 898 00:53:37,480 --> 00:53:42,000 Speaker 1: this That's the other thing. It's unclear when or whether 899 00:53:42,200 --> 00:53:45,480 Speaker 1: this technology will find applications in other fields. I would 900 00:53:45,480 --> 00:53:48,399 Speaker 1: say it definitely will. Like think how valuable this would 901 00:53:48,400 --> 00:53:51,320 Speaker 1: be for the decision to make or reject, to accept 902 00:53:51,400 --> 00:53:55,400 Speaker 1: or reject alone? Right. Uh, basically, the sky nets the limit. 903 00:53:56,880 --> 00:54:00,640 Speaker 1: But the the issue is the who is that the 904 00:54:01,480 --> 00:54:04,160 Speaker 1: badgers are already out of the bag. This stuff is, 905 00:54:04,239 --> 00:54:08,560 Speaker 1: this stuff is coming, um, maybe sooner to some places 906 00:54:08,680 --> 00:54:12,400 Speaker 1: rather than later. But China is making aggressive use of 907 00:54:12,400 --> 00:54:16,680 Speaker 1: this technology nearly every sector of government to improve efficiency, 908 00:54:16,880 --> 00:54:22,080 Speaker 1: ostensibly to reduce corruption, and honestly, I feel like it's 909 00:54:22,080 --> 00:54:27,520 Speaker 1: an open secret to further solidify control over the populace 910 00:54:27,840 --> 00:54:32,520 Speaker 1: um this. So remember how we're talking about this AI 911 00:54:32,640 --> 00:54:35,080 Speaker 1: system being used in New Jersey. Well, Chinese courts have 912 00:54:35,160 --> 00:54:39,319 Speaker 1: been doing this with like just the just the very 913 00:54:39,360 --> 00:54:43,920 Speaker 1: same thing, using AI to help automate the decision about 914 00:54:43,960 --> 00:54:48,000 Speaker 1: whether or not to accept or reject an appeal in 915 00:54:48,040 --> 00:54:53,360 Speaker 1: the legal system, and that again doesn't seem incredibly dystoping 916 00:54:53,440 --> 00:54:55,600 Speaker 1: and dangerous. But then what what do you guys think 917 00:54:55,640 --> 00:54:59,640 Speaker 1: about the Chinese prisons using a I tech to track 918 00:54:59,760 --> 00:55:03,800 Speaker 1: the physical and mental status of prisoners. Official goal is 919 00:55:03,840 --> 00:55:08,120 Speaker 1: to reduce violence? It depends, I mean, is it something 920 00:55:08,160 --> 00:55:12,160 Speaker 1: that you could judge has a positive impact on the 921 00:55:12,200 --> 00:55:16,560 Speaker 1: safety of guards? Does it in fact reduce violence? I 922 00:55:16,560 --> 00:55:19,399 Speaker 1: think there are ways that you could find that out, um, 923 00:55:19,440 --> 00:55:21,319 Speaker 1: But again, it all depends on who's doing it, and 924 00:55:21,320 --> 00:55:23,480 Speaker 1: I don't. I think we don't particularly inherently trust the 925 00:55:23,560 --> 00:55:26,360 Speaker 1: Chinese government to to give us the real reasons. But 926 00:55:26,480 --> 00:55:28,960 Speaker 1: I think it could be really helpful in a prison 927 00:55:29,040 --> 00:55:32,840 Speaker 1: situation and keep guards from getting hurt and keep in 928 00:55:32,960 --> 00:55:35,200 Speaker 1: mates from getting hurt if you used it right. They're 929 00:55:35,200 --> 00:55:37,799 Speaker 1: already being monitored anyway. I just don't quite see how 930 00:55:37,840 --> 00:55:41,120 Speaker 1: this is nefarious. I mean, people in prison are inherently monitored. 931 00:55:41,120 --> 00:55:43,479 Speaker 1: That's sort of the whole deal. Are we essentially saying 932 00:55:43,520 --> 00:55:47,040 Speaker 1: give every incarcerated person a fitbit kind of thing like 933 00:55:47,120 --> 00:55:49,239 Speaker 1: where then they would all all that data would be 934 00:55:49,320 --> 00:55:53,080 Speaker 1: collected and decisions would be made upon it. That's what 935 00:55:53,120 --> 00:55:56,680 Speaker 1: it sounds like to me. I don't know. Yeah, I 936 00:55:56,680 --> 00:55:58,359 Speaker 1: don't know. It's a good question. It's a good point 937 00:55:58,360 --> 00:56:02,759 Speaker 1: about people already being kind of uh panopticon monitoring situation. 938 00:56:03,400 --> 00:56:08,000 Speaker 1: But either way, you know, the question then verges on 939 00:56:08,200 --> 00:56:12,080 Speaker 1: the realm of philosophy. Right, can an artificial intelligence of 940 00:56:12,120 --> 00:56:15,560 Speaker 1: some sort remove the human bias of a judge or 941 00:56:15,640 --> 00:56:20,400 Speaker 1: does it only automate existing bias, possibly exacerbating existing bias 942 00:56:20,640 --> 00:56:23,800 Speaker 1: while removing some of the checks that are in place 943 00:56:24,080 --> 00:56:28,760 Speaker 1: to uh protect people against incorrect conclusions and what happens 944 00:56:28,760 --> 00:56:32,439 Speaker 1: if someone powerful decides to influence the system. How would 945 00:56:32,480 --> 00:56:36,239 Speaker 1: the public ever know? What, if anything, could the public do. 946 00:56:37,040 --> 00:56:39,600 Speaker 1: It's not outside the realm of possibility. It's also like, 947 00:56:39,640 --> 00:56:42,640 Speaker 1: I mean, the human touch part is the empathy part. 948 00:56:43,080 --> 00:56:46,400 Speaker 1: And honestly, and you could argue that empathy makes things 949 00:56:46,520 --> 00:56:51,200 Speaker 1: less efficient. You know, whenever we see like dystopian future 950 00:56:51,440 --> 00:56:55,640 Speaker 1: AI Skynet type situations or like RoboCop, uh, it takes 951 00:56:55,640 --> 00:56:58,560 Speaker 1: empathy out of the equation and judges things exclusively on 952 00:56:58,600 --> 00:57:01,560 Speaker 1: this like data set rather than like, you know, oh, 953 00:57:01,840 --> 00:57:04,000 Speaker 1: this kid had a bad day and maybe I'm gonna 954 00:57:04,600 --> 00:57:08,080 Speaker 1: inject this proceeding with a little bit of human empathy. 955 00:57:08,280 --> 00:57:10,680 Speaker 1: That's sort of out of the realm of this type 956 00:57:10,680 --> 00:57:15,440 Speaker 1: of processing. Yeah, I think overall, philosophically, the one thing 957 00:57:15,520 --> 00:57:18,880 Speaker 1: you do need in a judge system like that, in 958 00:57:18,920 --> 00:57:22,640 Speaker 1: a legal system is empathy to really look at the 959 00:57:22,640 --> 00:57:26,040 Speaker 1: the grey areas of law. But again, that works when 960 00:57:26,040 --> 00:57:27,680 Speaker 1: you're in a common law situation. When you're in a 961 00:57:27,680 --> 00:57:31,840 Speaker 1: civil law situation, it's different. And you know, ah, that's 962 00:57:31,880 --> 00:57:34,320 Speaker 1: a good point, and to think about that back full 963 00:57:34,360 --> 00:57:37,840 Speaker 1: circle there, like this would be harder to implement in 964 00:57:37,880 --> 00:57:42,320 Speaker 1: a way that people could stomach here in the US. Yeah, 965 00:57:42,400 --> 00:57:46,720 Speaker 1: because you can't consider it in the same way. Right, 966 00:57:47,120 --> 00:57:50,160 Speaker 1: That's what that's what that's what our system is all about. 967 00:57:50,240 --> 00:57:54,440 Speaker 1: A judge fully considering what's happened in the past around 968 00:57:54,440 --> 00:57:57,960 Speaker 1: a case like this and the exact aspects of the 969 00:57:58,000 --> 00:58:00,840 Speaker 1: current case. How do how are those things different, how's 970 00:58:00,880 --> 00:58:03,680 Speaker 1: the social setting different, and the more rays that are 971 00:58:03,680 --> 00:58:06,880 Speaker 1: existing right now? And that's just how people generally feel 972 00:58:06,920 --> 00:58:09,560 Speaker 1: about things, what's right and what's wrong. Then they make 973 00:58:09,640 --> 00:58:12,240 Speaker 1: that decision, and then that decision gets used down the road. 974 00:58:12,280 --> 00:58:15,480 Speaker 1: So I don't know it. I hate to say it, guys, 975 00:58:16,160 --> 00:58:19,680 Speaker 1: in a civil law system like this, it almost makes 976 00:58:19,760 --> 00:58:24,240 Speaker 1: complete sense to me that, right, because the judges are 977 00:58:24,360 --> 00:58:29,240 Speaker 1: checking facts and connecting the facts to condifying punishments. Yeah, 978 00:58:29,480 --> 00:58:34,320 Speaker 1: that's possible. But the question is what makes the um 979 00:58:34,440 --> 00:58:37,760 Speaker 1: what what has the best chance of attaining the most 980 00:58:37,800 --> 00:58:42,560 Speaker 1: accuracy while minimizing, if not erasing the existence of bias. 981 00:58:42,600 --> 00:58:45,800 Speaker 1: And those are very difficult questions that answer. But right 982 00:58:45,800 --> 00:58:48,240 Speaker 1: now we can say that this is open to corruption. 983 00:58:48,640 --> 00:58:52,160 Speaker 1: It really does depend upon the origin point of the 984 00:58:52,200 --> 00:58:55,120 Speaker 1: information it's being fed. It depends on you know, like 985 00:58:55,360 --> 00:58:58,920 Speaker 1: the thing about the thing about having to find facts 986 00:58:58,920 --> 00:59:00,560 Speaker 1: and then just go a media It lead to a 987 00:59:00,640 --> 00:59:04,480 Speaker 1: codified consequence is that we have to ask about the 988 00:59:04,520 --> 00:59:07,560 Speaker 1: bias of the people who wrote those codes of law, 989 00:59:07,600 --> 00:59:10,840 Speaker 1: who wrote those statutes first. So it's deeper than just 990 00:59:10,920 --> 00:59:14,960 Speaker 1: whomever programs system two oh six. And that's where we 991 00:59:15,040 --> 00:59:17,560 Speaker 1: That's where we leave it for you, folks. What do 992 00:59:17,680 --> 00:59:22,800 Speaker 1: you think is this trend of automating justice inevitable? Is 993 00:59:22,800 --> 00:59:26,200 Speaker 1: it overall a good or bad direction? And how do 994 00:59:26,240 --> 00:59:29,160 Speaker 1: you see it playing out in the future. Would love 995 00:59:29,200 --> 00:59:31,240 Speaker 1: to hear your thoughts. We try to be easy to 996 00:59:31,320 --> 00:59:34,919 Speaker 1: find online. Oh the Internet. We're all over the thing. 997 00:59:34,960 --> 00:59:38,160 Speaker 1: You can find us under the handle at conspiracy Stuff 998 00:59:38,240 --> 00:59:42,000 Speaker 1: on YouTube, Twitter, Facebook, or we also have a really 999 00:59:42,000 --> 00:59:44,200 Speaker 1: cool Facebook group called Here's where it It's Crazy that 1000 00:59:44,240 --> 00:59:46,960 Speaker 1: you can join. Um, if you're more of an Instagram type, 1001 00:59:47,000 --> 00:59:50,880 Speaker 1: you can find us at conspiracy Stuff Show. 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