In 2007, I became the attorney general of the state of New Jersey. Before that, I'd been a criminal prosecutor, first in the Manhattan district attorney's office, and then at the United States Department of Justice.
2007年,我担任了新泽西州的 司法部长。 在那之前,我曾是一名刑事检察官, 先是在曼哈顿地区检查官办公室, 后来是在国家司法部。
But when I became the attorney general, two things happened that changed the way I see criminal justice. The first is that I asked what I thought were really basic questions. I wanted to understand who we were arresting, who we were charging, and who we were putting in our nation's jails and prisons. I also wanted to understand if we were making decisions in a way that made us safer. And I couldn't get this information out. It turned out that most big criminal justice agencies like my own didn't track the things that matter. So after about a month of being incredibly frustrated, I walked down into a conference room that was filled with detectives and stacks and stacks of case files, and the detectives were sitting there with yellow legal pads taking notes. They were trying to get the information I was looking for by going through case by case for the past five years. And as you can imagine, when we finally got the results, they weren't good. It turned out that we were doing a lot of low-level drug cases on the streets just around the corner from our office in Trenton.
但是在担任司法部长之后, 发生了两件事让我改变了对刑事司法的看法 第一个是我提出我所认为的 很基本的问题。 我想要了解我们逮捕的是什么人, 我们指控的是什么人, 还有我们是将什么样的人关进看守所 和监狱。 我也想要了解 我们所做的决定是否 会让民众更加安全。 但我无法获取这类信息 原来多数大型刑事司法机构 就像我工作的地方 他们并没有对真正重要的事情进行持续的跟踪调查和记录。 所以经历了约一个月的异常沮丧之后, 我走进一个会议室 满屋都是探员 和成堆成堆的案件档案, 探员们坐在那里 用黄色便笺簿作着笔记。 他们试图获取的信息 就是我一直在寻找的 通过逐个分析 过去的五年间的所有案件。 你可以想象 我们终于得出的结果并不是很理想。 原来我们一直在做 很多低级的毒品案件 就在拐角处的街道上 离我们在特伦顿的办公室不远。
The second thing that happened is that I spent the day in the Camden, New Jersey police department. Now, at that time, Camden, New Jersey, was the most dangerous city in America. I ran the Camden Police Department because of that. I spent the day in the police department, and I was taken into a room with senior police officials, all of whom were working hard and trying very hard to reduce crime in Camden. And what I saw in that room, as we talked about how to reduce crime, were a series of officers with a lot of little yellow sticky notes. And they would take a yellow sticky and they would write something on it and they would put it up on a board. And one of them said, "We had a robbery two weeks ago. We have no suspects." And another said, "We had a shooting in this neighborhood last week. We have no suspects." We weren't using data-driven policing. We were essentially trying to fight crime with yellow Post-it notes.
第二件事是 我在卡姆登的新泽西州警察局耗了一天。 当时,新泽西州的卡姆登 是美国最危险的城市。 我跑了一趟卡姆登警察局就是因为这个原因。 我在警察局待了一整天, 被带到了一个高级警官待的房间, 那里所有人都在努力工作 并很努力的试图减少卡姆登的犯罪活动 在那个房间里, 当我们谈到如何减少犯罪, 有很多拿着小小的黄色便笺的警官。 他们会揭下一张黄色便笺,在上面写点东西 然后把它贴在板儿上。 其中一个警官说,“我们有一宗劫案发生在两个星期前 但没发现疑犯。” 另一个说:“上周在这附近发生了一场枪击事件,没发现疑犯” 我们未曾使用过数据分析来维持治安。 我们基本上在试图 用黄色便利签打击犯罪。
Now, both of these things made me realize fundamentally that we were failing. We didn't even know who was in our criminal justice system, we didn't have any data about the things that mattered, and we didn't share data or use analytics or tools to help us make better decisions and to reduce crime. And for the first time, I started to think about how we made decisions. When I was an assistant D.A., and when I was a federal prosecutor, I looked at the cases in front of me, and I generally made decisions based on my instinct and my experience. When I became attorney general, I could look at the system as a whole, and what surprised me is that I found that that was exactly how we were doing it across the entire system -- in police departments, in prosecutors's offices, in courts and in jails. And what I learned very quickly is that we weren't doing a good job. So I wanted to do things differently. I wanted to introduce data and analytics and rigorous statistical analysis into our work. In short, I wanted to moneyball criminal justice.
这两件事让我意识到 从根本上说,我们过去一直表现欠佳。 我们甚至不知道有谁涉及到刑事犯罪, 我们没有相关重要事件的任何数据, 我们未曾共享数据,使用分析技术 或分析工具,以帮助我们做出更好地判断 并减少犯罪。 我第一次开始思考 我们是如何作出决定的。 当我还是助理地方检察官, 和联邦检察官的时候, 我看着面前的那些案件, 我所做出的决定通常是依据我的直觉 和我的经验。 后来我成为司法部长, 我可以全面的观察整个司法系统 令人吃惊的是我发现 我们的这种做法恰恰适用于 整个司法系统 — — 警察部门,检察官办公室, 法庭和监狱。 很快,我了解到 我们过去的工作成果并不令人满意。 所以我想做些改变。 我想将数据、逻辑分析 和严格的统计分析 纳入到我们的工作。 总之,我想在刑事司法上做到点球成金。
Now, moneyball, as many of you know, is what the Oakland A's did, where they used smart data and statistics to figure out how to pick players that would help them win games, and they went from a system that was based on baseball scouts who used to go out and watch players and use their instinct and experience, the scouts' instincts and experience, to pick players, from one to use smart data and rigorous statistical analysis to figure out how to pick players that would help them win games.
正如大家所知,点球成金, 是奥克兰运动家棒球队的方法, 他们使用了智能数据和统计学 找出如何挑选球员的办法 这将有助于他们赢得比赛, 依据一个由棒球侦查员组成的系统 他们频繁的观察球员的表现 利用他们的直觉和经验, 侦查员的直觉和经验, 挑选球员,通过使用 智能数据和严格的统计分析 找出挑选球员的办法,这会帮助他们赢得比赛。
It worked for the Oakland A's, and it worked in the state of New Jersey. We took Camden off the top of the list as the most dangerous city in America. We reduced murders there by 41 percent, which actually means 37 lives were saved. And we reduced all crime in the city by 26 percent. We also changed the way we did criminal prosecutions. So we went from doing low-level drug crimes that were outside our building to doing cases of statewide importance, on things like reducing violence with the most violent offenders, prosecuting street gangs, gun and drug trafficking, and political corruption.
奥克兰运动家棒球队就是这样运作的, 这对新泽西州也是适用的。 我们已经将卡姆登从 美国最危险的城市的名单中剔除。 我们减少了41%的谋杀率, 实际上相当于拯救了37条生命。 整个城市的犯罪行为减少了26%。 我们也改变了刑事诉讼的工作方式。 所以我们从调查低级毒品犯罪案件 它们就发生在办公大楼外面 转移到调查全州范围内的重要案件, 比如减少暴力罪犯的暴力行为, 起诉街头帮派, 枪支和毒品的不法交易,还有政治腐败。
And all of this matters greatly, because public safety to me is the most important function of government. If we're not safe, we can't be educated, we can't be healthy, we can't do any of the other things we want to do in our lives. And we live in a country today where we face serious criminal justice problems. We have 12 million arrests every single year. The vast majority of those arrests are for low-level crimes, like misdemeanors, 70 to 80 percent. Less than five percent of all arrests are for violent crime. Yet we spend 75 billion, that's b for billion, dollars a year on state and local corrections costs. Right now, today, we have 2.3 million people in our jails and prisons. And we face unbelievable public safety challenges because we have a situation in which two thirds of the people in our jails are there waiting for trial. They haven't yet been convicted of a crime. They're just waiting for their day in court. And 67 percent of people come back. Our recidivism rate is amongst the highest in the world. Almost seven in 10 people who are released from prison will be rearrested in a constant cycle of crime and incarceration.
所有这些事项都很重要, 因为我认为,保证公共安全 是政府最重要的职能。 如果人身安全无法保证,我们就不能接受教育, 就不能保持身体健康, 就不能做生活中想要做的任何事。 今天我们生活的国家 面临着严重的刑事司法问题。 我们每年有1200 万起拘捕行动。 绝大多数的拘捕行动 是针对低级的犯罪行为,像轻罪, 这些占据了70%至80%。 不到5%的拘捕行动 是针对暴力犯罪。 然而,我们每年花费750亿美元, 以十亿为单位, 作为国家和地方的修正成本。 此时此刻,有230万人 被监禁在看守所和监狱里。 我们在公共安全方面面临着惊人的挑战 因为现在的形势是 监狱中有三分之二的人 在那里等待审判。 他们至今还没有被判定有罪。 他们一直等着出庭受审, 其中有67%的人会重返社会。 我们是世界上累犯率最高的国家之一。 几乎每释放10个人就有7个 将会再次被逮捕 这是一个恒定的犯罪和监禁的周期。
So when I started my job at the Arnold Foundation, I came back to looking at a lot of these questions, and I came back to thinking about how we had used data and analytics to transform the way we did criminal justice in New Jersey. And when I look at the criminal justice system in the United States today, I feel the exact same way that I did about the state of New Jersey when I started there, which is that we absolutely have to do better, and I know that we can do better.
所以,当我开始在阿诺德基金会工作时, 回头看了很多这类问题, 重新思考了我们怎样 利用数据和分析转变了 新泽西州的刑事司法。 后来我注意到 当今国家的刑事司法系统, 我觉得应使用同样的方法 即首先在新泽西州使用的那种方法, 毫无疑问我们要做得更好, 而且我知道我们可以做得更好。
So I decided to focus on using data and analytics to help make the most critical decision in public safety, and that decision is the determination of whether, when someone has been arrested, whether they pose a risk to public safety and should be detained, or whether they don't pose a risk to public safety and should be released. Everything that happens in criminal cases comes out of this one decision. It impacts everything. It impacts sentencing. It impacts whether someone gets drug treatment. It impacts crime and violence. And when I talk to judges around the United States, which I do all the time now, they all say the same thing, which is that we put dangerous people in jail, and we let non-dangerous, nonviolent people out. They mean it and they believe it. But when you start to look at the data, which, by the way, the judges don't have, when we start to look at the data, what we find time and time again, is that this isn't the case. We find low-risk offenders, which makes up 50 percent of our entire criminal justice population, we find that they're in jail. Take Leslie Chew, who was a Texas man who stole four blankets on a cold winter night. He was arrested, and he was kept in jail on 3,500 dollars bail, an amount that he could not afford to pay. And he stayed in jail for eight months until his case came up for trial, at a cost to taxpayers of more than 9,000 dollars. And at the other end of the spectrum, we're doing an equally terrible job. The people who we find are the highest-risk offenders, the people who we think have the highest likelihood of committing a new crime if they're released, we see nationally that 50 percent of those people are being released.
所以我决定将重点集中在 使用数据和分析 以帮助作出最关键的判断 在公共安全方面, 这一决定是判断 已经被逮捕的某个人, 是否会对公共安全构成风险 而被拘留 还是不会对公共安全造成风险 应被释放。 刑事案件中发生的一切 都出自这一决定。 它影响了全局。 它影响了判刑。 它影响到是否有人需要药物治疗。 它影响了犯罪和暴力行为。 当我同全美众多法官交谈时, 我现在无时无刻不在做这件事, 他们都说着同样的话, 我们把危险的人关进监狱, 把没有危险的人、非暴力的人放出去。 他们是认真的,他们相信自己所做的。 但当你开始查看那些数据, 顺便提一句,那些法官没看过, 当我们开始查看数据, 我们一次又一次的发现, 这不是个案。 我们发现, 占刑事司法总人数的50%的低风险罪犯 被关在监狱里。 举个例子,莱斯利丘是德克萨斯州人 在一个寒冷的冬夜偷了四个毯子。 他被拘捕,然后被关进监狱 需要三千五百美元保释金, 这是一笔他支付不起的金额。 他在监狱里呆了八个月 直到他的案子开庭, 共花了纳税人9,000 多美元税款。 在另一个极端, 我们所做的工作也同样糟糕, 那些我们抓获的 高危险罪犯, 那些被认为一旦释放会有极高的可能性 再次犯罪的人, 在全国范围内,其中的50% 正在回归社会。
The reason for this is the way we make decisions. Judges have the best intentions when they make these decisions about risk, but they're making them subjectively. They're like the baseball scouts 20 years ago who were using their instinct and their experience to try to decide what risk someone poses. They're being subjective, and we know what happens with subjective decision making, which is that we are often wrong. What we need in this space are strong data and analytics.
是我们做决定的方式导致的这种结果。 法官怀着善意 做出这些有风险的决定, 但,是主观的决定。 他们就像20 年前的棒球侦查员 他们凭本能和经验 试着去判断某个人制造的危险。 他们是主观的, 我们知道主观决策会导致什么, 那就是我们常常犯错。 我们需要的是 有力的数据和分析。
What I decided to look for was a strong data and analytic risk assessment tool, something that would let judges actually understand with a scientific and objective way what the risk was that was posed by someone in front of them. I looked all over the country, and I found that between five and 10 percent of all U.S. jurisdictions actually use any type of risk assessment tool, and when I looked at these tools, I quickly realized why. They were unbelievably expensive to administer, they were time-consuming, they were limited to the local jurisdiction in which they'd been created. So basically, they couldn't be scaled or transferred to other places.
我决定寻找 一个有力的数据分析的风险评估工具, 这个工具会让法官 以科学的客观的方式去了解 什么样的风险 摆在他们面前。 我找遍了全国, 发现5%至10%的 美国管辖区域 实际使用了某些类型的风险评估工具, 我查看了这些评估工具之后, 很快意识到其中缘由。 它们应用起来非常昂贵, 非常耗时, 它们被限制在地方管辖区域 因为它们就出自那里。 因此,基本上,它们不能扩展 或转移到其他地方。
So I went out and built a phenomenal team of data scientists and researchers and statisticians to build a universal risk assessment tool, so that every single judge in the United States of America can have an objective, scientific measure of risk. In the tool that we've built, what we did was we collected 1.5 million cases from all around the United States, from cities, from counties, from every single state in the country, the federal districts. And with those 1.5 million cases, which is the largest data set on pretrial in the United States today, we were able to basically find that there were 900-plus risk factors that we could look at to try to figure out what mattered most. And we found that there were nine specific things that mattered all across the country and that were the most highly predictive of risk. And so we built a universal risk assessment tool. And it looks like this. As you'll see, we put some information in, but most of it is incredibly simple, it's easy to use, it focuses on things like the defendant's prior convictions, whether they've been sentenced to incarceration, whether they've engaged in violence before, whether they've even failed to come back to court. And with this tool, we can predict three things. First, whether or not someone will commit a new crime if they're released. Second, for the first time, and I think this is incredibly important, we can predict whether someone will commit an act of violence if they're released. And that's the single most important thing that judges say when you talk to them. And third, we can predict whether someone will come back to court. And every single judge in the United States of America can use it, because it's been created on a universal data set.
所以我组建了一个出色的团队 由数据科学家和研究人员 还有统计人员组成 建立一个通用的风险评估工具, 这样一来,全美的每一个法官 都可以做一个客观、科学的风险评估。 在这个已经建立的这个工具里, 我们收集了150 万个案件 来自美国各地, 包括城市,县, 国内的每个州, 联邦区。 有了那150 万个案例, 这是美国审判前最大的数据库 截止到今天, 我们基本上能够找到 我们可以查看的九百多个危险因素 试着找出最重要的问题。 我们发现具体有九件事 在全国范围内都很重要 是可预测的最高风险。 于是我们建立了一种通用的风险评估工具。 它看起来就像这个。 正如你所看到的,我们把一些信息列在上面, 但大多数都格外简单, 它使用起来很容易, 它侧重的方面是被告的前科, 他们是否曾被判处监禁, 他们是否曾被卷入过暴力事件, 他们是否甚至没能回到法庭。 使用此工具,我们可以预测三件事。 第一,他们是否会再次犯罪 如果被释放的话。 第二,我第一次觉得, 这一点非常重要, 我们可以预测他们是否会 进行暴力活动,如果被释放的话。 法官说这是最重要的一件事 当你向他们问话的时候。 第三,我们可以预测是否他们 会回到法庭。 美国的任何一名法官都可以使用它, 因为它是由通用的数据库制成的。
What judges see if they run the risk assessment tool is this -- it's a dashboard. At the top, you see the New Criminal Activity Score, six of course being the highest, and then in the middle you see, "Elevated risk of violence." What that says is that this person is someone who has an elevated risk of violence that the judge should look twice at. And then, towards the bottom, you see the Failure to Appear Score, which again is the likelihood that someone will come back to court.
如果他们使用了这个风险评估工具你就会看到 这个--一个评分板。 在顶部的,是新的刑事犯罪活动评分, 六当然是最高分, 然后在中间你可以看到“增长的暴力风险” 说的是,这个人 进行暴力行为的概率有所升高 这是法官应该注意的重点。 然后在底部, 你看到的是未能出庭的分数, 这也是判断 此人会回到法庭的可能性。
Now I want to say something really important. It's not that I think we should be eliminating the judge's instinct and experience from this process. I don't. I actually believe the problem that we see and the reason that we have these incredible system errors, where we're incarcerating low-level, nonviolent people and we're releasing high-risk, dangerous people, is that we don't have an objective measure of risk. But what I believe should happen is that we should take that data-driven risk assessment and combine that with the judge's instinct and experience to lead us to better decision making. The tool went statewide in Kentucky on July 1, and we're about to go up in a number of other U.S. jurisdictions. Our goal, quite simply, is that every single judge in the United States will use a data-driven risk tool within the next five years. We're now working on risk tools for prosecutors and for police officers as well, to try to take a system that runs today in America the same way it did 50 years ago, based on instinct and experience, and make it into one that runs on data and analytics.
现在我想说些非常重要的事。 我不认为应该排除 法官的直觉和经验 在整个过程中。 我不这样想。 事实上,我相信我们看到的问题 以及系统内出现令人难以置信的错误的原因, 也就是我们关押低级、非暴力的人 我们释放高风险的、危险的人的原因, 是因为我们没有客观的衡量风险。 但我相信 我们应该将这种数据驱动的风险评估 与法官的直觉和经验相结合 会使我们做出更好的决策。 该评估工具于7月1日在肯塔基州全面推行, 我们还要在许多其他美国司法管辖区内推行。 我们的目标很简单,就是让全美的每一个法官 都能使用这种数据驱动的风险评估工具 在未来五年内实现。 我们现在正在研究风险工具 以便检察官和警官使用, 想要把过去50年不变的系统 继续运行下去, 基于直觉和经验, 并把它变成一种运用 数据分析的系统。
Now, the great news about all this, and we have a ton of work left to do, and we have a lot of culture to change, but the great news about all of it is that we know it works. It's why Google is Google, and it's why all these baseball teams use moneyball to win games. The great news for us as well is that it's the way that we can transform the American criminal justice system. It's how we can make our streets safer, we can reduce our prison costs, and we can make our system much fairer and more just. Some people call it data science. I call it moneyballing criminal justice.
现在, 我们仍有大量的工作要做, 仍要改变相关文化, 但关于这所有一切有个好消息 那就是我们知道这很管用。 这就是为什么数据成就了谷歌, 为什么棒球队会因使用点球成金的方法 而打赢比赛。 还有一个好消息是 我们可以用这种方法转变 美国的刑事司法系统。 它可以使我们的周边环境更安全, 可以减少我们的监狱花销, 可以让我们的司法系统更公平 更公正。 有人说这是数据科学。 我称之为点球成金式刑事司法。
Thank you.
谢谢。
(Applause)
(掌声)