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(单词翻译:双击或拖选)
AUDIE CORNISH, HOST:
We live in an age of big data as our daily decisions are increasingly steered1 by computers. This week on All Tech Considered, we learn how the police are catching2 up with the rest of us.
(SOUNDBITE OF ULRICH SCHNAUSS' "NOTHING HAPPENS IN JUNE")
CORNISH: Big police departments are relying more and more on data to decide where to patrol and even whom to keep a close eye on. But as NPR's Martin Kaste reports from Los Angeles, some people worry that data-driven policing is just a high-tech3 cover for old-fashioned profiling.
UNIDENTIFIED PERSON #1: So we have the name Joseph.
MARTIN KASTE, BYLINE4: You probably already assumed that the police could do this kind of search - type in someone's name and see what pops up.
UNIDENTIFIED PERSON #1: It's linking it to a residence, you know, user of this phone number, associated with this vehicle, works in that address.
KASTE: But actually, this is kind of revolutionary. TV cops do this all the time of course. But in real life, police information systems are often clunky with different kinds of information kept in separate silos. Cops spend hours logging into the different systems, printing stuff out, collating5 it all by hand. But lately, some bigger departments have finally set up internal search systems that can combine all this in one place, on one screen. And the Los Angeles Police Department is one of them.
UNIDENTIFIED PERSON #1: Hollywood.
UNIDENTIFIED PERSON #2: Hollywood's on.
UNIDENTIFIED PERSON #1: Thank you. Wilshire.
UNIDENTIFIED PERSON #3: Wilshire's on.
KASTE: When you listen to the daily crime conference call here, the effect of these new data tools is obvious. The crime stats are up-to-the-minute, tracked and mapped in granular fashion.
UNIDENTIFIED PERSON #4: So Hollywood - so I see yesterday we have one robbery, three burglary for motor vehicles. So what's going on?
KASTE: You probably remember CompStat. That was the crime tracking system pioneered in New York in the 1990s. But this is much faster. Instead of looking at last week's statistics, LAPD is analyzing6 crime trends within hours and responding almost as quickly. It's an ever-shrinking feedback loop that directly affects the assignments of cops on the street like Jennifer Ramirez.
JENNIFER RAMIREZ: So basically this is what we get on a daily basis. And we take out a mission sheet.
KASTE: Ramirez patrols in the department's Olympic division, which includes Koreatown. The mission sheet she mentioned is generated by her captain and lieutenant7 using a variety of software tools. And it includes detailed8 maps of problem areas that the LAPD calls LASER zones.
RAMIREZ: I want to make sure that I try and spend as much time in this area of my free time because these are the hot spots. These are where the crimes will tend to happen.
KASTE: And this system isn't just putting officers at certain places. It's also pointing them at certain people. Ramirez's mission sheet also comes with names and photos.
RAMIREZ: These are people that we are going to be looking out for in the area who have been our chronic9 offenders10.
KASTE: Chronic offenders - this may be the most sensitive aspect of the LAPD's recent data analytics push. Each of these LASER zones comes with a list of the top people that the cops are supposed to watch for. Membership on this list is determined11 by a formula that draws on data about past interactions with the justice system. The cops are supposed to try to interact with these people as much as possible sort of as a warning to stay out of trouble. But to Anthony Robles, it all sounds way too familiar.
ANTHONY ROBLES: They're just reinventing their surveillance techniques and machinery12.
KASTE: Robles is an organizer with the Youth Justice Coalition13. It's an activist14 group run by people who've been incarcerated15. He was one of the plaintiffs in a lawsuit16 against the LAPD's long-standing gang injunction system which relied on lists of people that the police categorized as gang affiliates17. He recalls what it was like for him as a teenager when he was on that gang list.
ROBLES: Every time I drove by to that block or drove anywhere, I'd get pulled over. A lot of times they would search my car. They wouldn't find anything, and then they would just give me, like, a moving violation18 ticket. It led to a lot of anger. It made me want to go do something bad.
KASTE: This gang list system now seems to be on its way out, but Robles thinks it's being replaced by the Chronic Offenders Bulletin and the LASER zones and the other data analysis tools. And Jamie Garcia agrees. She's with an activist group called the Stop LAPD Spying Coalition, which sued to get more details about how the department is using data.
JAMIE GARCIA: These programs are nothing new in the history of policing. What they're trying to call science is really pseudoscience. The bias19 is still very much inherent in the data that's being used, and the same communities are being impacted.
KASTE: For instance, she says the formula for determining whether someone's on the Chronic Offenders Bulletin is based partly on how often someone is interviewed by the police. But that's something that's simply more likely to happen in those places with heavier police presence. To her, it's just a vicious cycle with a high-tech veneer20. But inside the LAPD, the brass21 are pleased with the new data tools.
DENNIS KATO: It works.
KASTE: Dennis Kato is a deputy chief who's been tasked with helping22 to roll out these new analytics systems to all of this sprawling23 department's divisions by 2020. He says what people have to remember is that compared to a generation ago, crime in this city is much lower.
KATO: But you know what - so is our arrest numbers. So that's a good thing - right? - because that means we're arresting the right people. We're not out there saturating24. We're not just picking up people for everything.
KASTE: At the same time, Kato says police do need to be careful as they get more, as he puts it, creative with the data.
KATO: We've got to figure out, what is the boundaries? How much is good data? And what is the input25 mechanisms26? And all of that's happening. We question this stuff all the time.
CORNISH: And Martin joins us now to talk a little more about how police are using data. And, Martin, just start with a clarification here. Is the LAPD getting new kinds of data?
KASTE: It's not really new data, Audie. What we're talking about here is a new way to access the data they already have in about 19 databases. They say this is all just information in government-created databases like licenses28 and arrest records, that kind of thing. What they're doing, though, is - you know, these things are in silos. They're separate. They have some software by a company called Palantir that lets them sort of search across it and look for relationships. Now, this is not unique.
Palantir gets a lot of press because there's a lot of sort of suspicions about their contracts with the federal government, with national security contracts. But really other software can do the same kind of thing. It's called sort of relational databases. But really what we're talking about here is just speed and ease of searching across a lot of different kinds of data.
CORNISH: Besides the concerns we heard in this story about profiling certain people or neighborhoods, are there broader concerns just about privacy?
KASTE: Yeah, there may be. I mean, as I said, this is easier now to look at different kinds of data, a lot less time logging into different things. And where there's ease there's temptation to abuse it. In California, there's been some discussion about incidences of police officers inappropriately getting into something called the C-L-E-T-S or CLETS database, which is DMV information. There's been some pressure on them to police that better, so to speak. And so you can imagine if an officer has access to a computer where he can sit down and just search a lot of stuff all at once with one screen, yeah, there might be some temptations.
CORNISH: And is there a limit at least on how long they can hold onto all this information?
KASTE: Well, there are some practical considerations involving storage. One example here is the license27 plate reader data. You probably know about license plate readers in cars. As the squad29 cars drive around, they're just gathering30 up all the license plates that they spot. It gets stored. You can recreate on a map where a car has been.
It turns out they're keeping that indefinitely. You know, if it bugs31 you that someone in the LAPD could reconstruct where you've driven over the last few years, I just talked to Deputy Chief Kato, who's in this story, about that. He says it looks like they have yet to delete any of that data. So going back to when they first started doing that, they could probably reconstruct where a car in LA has been.
CORNISH: That's NPR's Martin Kaste. Martin, thanks for your reporting.
KASTE: You're welcome.
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1 steered | |
v.驾驶( steer的过去式和过去分词 );操纵;控制;引导 | |
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2 catching | |
adj.易传染的,有魅力的,迷人的,接住 | |
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3 high-tech | |
adj.高科技的 | |
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4 byline | |
n.署名;v.署名 | |
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5 collating | |
v.校对( collate的现在分词 );整理;核对;整理(文件或书等) | |
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6 analyzing | |
v.分析;分析( analyze的现在分词 );分解;解释;对…进行心理分析n.分析 | |
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7 lieutenant | |
n.陆军中尉,海军上尉;代理官员,副职官员 | |
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8 detailed | |
adj.详细的,详尽的,极注意细节的,完全的 | |
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9 chronic | |
adj.(疾病)长期未愈的,慢性的;极坏的 | |
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10 offenders | |
n.冒犯者( offender的名词复数 );犯规者;罪犯;妨害…的人(或事物) | |
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11 determined | |
adj.坚定的;有决心的 | |
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12 machinery | |
n.(总称)机械,机器;机构 | |
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13 coalition | |
n.结合体,同盟,结合,联合 | |
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14 activist | |
n.活动分子,积极分子 | |
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15 incarcerated | |
钳闭的 | |
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16 lawsuit | |
n.诉讼,控诉 | |
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17 affiliates | |
附属企业( affiliate的名词复数 ) | |
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18 violation | |
n.违反(行为),违背(行为),侵犯 | |
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19 bias | |
n.偏见,偏心,偏袒;vt.使有偏见 | |
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20 veneer | |
n.(墙上的)饰面,虚饰 | |
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21 brass | |
n.黄铜;黄铜器,铜管乐器 | |
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22 helping | |
n.食物的一份&adj.帮助人的,辅助的 | |
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23 sprawling | |
adj.蔓生的,不规则地伸展的v.伸开四肢坐[躺]( sprawl的现在分词 );蔓延;杂乱无序地拓展;四肢伸展坐着(或躺着) | |
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24 saturating | |
浸湿,浸透( saturate的现在分词 ); 使…大量吸收或充满某物 | |
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25 input | |
n.输入(物);投入;vt.把(数据等)输入计算机 | |
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26 mechanisms | |
n.机械( mechanism的名词复数 );机械装置;[生物学] 机制;机械作用 | |
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27 license | |
n.执照,许可证,特许;v.许可,特许 | |
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28 licenses | |
n.执照( license的名词复数 )v.批准,许可,颁发执照( license的第三人称单数 ) | |
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29 squad | |
n.班,小队,小团体;vt.把…编成班或小组 | |
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30 gathering | |
n.集会,聚会,聚集 | |
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31 bugs | |
adj.疯狂的,发疯的n.窃听器( bug的名词复数 );病菌;虫子;[计算机](制作软件程序所产生的意料不到的)错误 | |
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