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年時,我成為紐澤西州 總檢察長。 在那之前,我擔任刑事檢察官, 一開始在曼哈頓區檢察官辦公室, 後來在美國司法部。
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.
但是當我成為總檢察長後, 發生了兩件事, 改變我對刑事司法的看法。 第一件事是我提出了一些我認為 很基本的問題。 我想了解我們逮捕誰、 控告誰、 以及把誰關進我國的拘留所 和監獄。 我也想了解 我們做決定的方式 是否能讓我們更安全。 我找不出答案。 結果是多數的大型刑事司法機關, 就像我工作的地方, 沒有追蹤重要的事情。 因此歷經大約一個月的強烈挫折感後, 我走進一間會議室, 裡面擠滿警探 和堆積如山的案件資料, 警探坐在那, 手拿黃色標準便條紙在做筆記。 他們試圖得到我在找的資訊, 逐一檢視 過去五年的案件。 如你所料, 最後得到的結果不太好。 結論是我們辦了 很多低階的街頭毒品案件, 就在附近的街上, 離我們在翠登的辦公室不遠。 第二件發生的事情是
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.
我在紐澤西肯頓警局待的那一天。 那時候紐澤西州肯頓 是全美最危險的城市, 這是我去肯頓警局的原因。 我在警局待一整天, 被帶到一間房間, 和資深警官在一起, 他們都很努力 試著降低肯頓的犯罪率。 當我們討論如何降低犯罪率時, 我在那房裡看到 一大堆警官拿著 很多黃色的小型便利貼。 他們拿著一張黃色便利貼, 在上面寫點東西, 把它貼在公布欄上。 其中一位會說:「兩個星期前有搶案。 沒有嫌犯。」 另一位說:「上星期這附近 有槍擊案。沒有嫌犯。」 我們沒有運用任何數據處理治安。 基本上,我們打算用黃色便利貼 來打擊犯罪。
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.
這兩件事讓我了解 我們徹底失敗了。 我們甚至不知道誰在 我們的刑事司法體系裡, 我們沒有重要資料的數據, 也沒有共享數據、運用分析 或工具來幫助我們做更好的決定, 並減少犯罪。 我第一次思考 我們是如何做決定。 我擔任地區助理檢察官 和聯邦檢察官, 研究眼前的案件時, 幾乎都是靠直覺 和經驗做決定。 當我成為總檢察長, 能夠全面檢視體制, 讓我驚訝的是發現了 我們就是那樣做, 整個體制都是如此── 在警察局、檢察署、 法院和監獄。 很快我就了解 我們做得不好, 於是就想用不同的方式做事。 我想把數據、邏輯分析 和精密統計分析 運用到工作上。 簡而言之,我想用 魔球的方式處理刑事司法。
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.
如在座許多人所知, 魔球是奧克蘭運動家隊 所運用的策略, 他們用數據和統計 找出選擇球員的方法 去幫助球隊贏球, 他們從前根據棒球球探意見, 球探會出門去看球員, 然後以直覺和經驗, 球探的直覺和經驗, 去挑選球員,從運用 數據和精密統計分析 找出要怎麼選出 能讓他們贏得比賽的球員。
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.
對奧克蘭運動家隊奏效了, 對紐澤西州也奏效了。 我們讓肯頓不再名列 美國最危險城市之一。 我們把當地兇殺案減少了 41%, 意謂著救了 37 條人命。 我們將城裡各種犯罪活動減少了 26% 。 我們也改變刑事訴訟的方式, 從處理低階的毒品犯罪, 那些發生在我們的大樓外, 轉變為遍及全州的重要案件, 像是減少高度危險暴力犯的再犯率、 起訴街頭幫派、 槍枝和毒品運送,以及政治貪汙。
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.
這一切帶來的影響甚大, 因為公共安全對我來說 是政府最重要的功能。 如果我們不安全, 我們就無法接受教育, 就無法擁有健康, 我們就無法做所有生活中想做的事。 今天我們居住的國家 正面對嚴重的刑事司法問題。 我們每年有 1,200 萬件逮捕案。 這些逮捕案最大部分的是 低階犯罪,像是輕罪, 佔 70% 到 80%。 只有不到 5% 的逮捕 是暴力犯罪。 然而我們花費 750 億美元, 是「百億」元, 在州和地方一年的懲治支出上。 現在,我們有 230 萬人 在監獄和拘留所。 我們面對難以致信的公安問題 因為面對的處境是 拘留所內三分之二的嫌疑犯 正在等著審判, 他們還沒被判有罪, 等著上法庭的那一天。 67% 的嫌疑犯會回來。 我們是全球累犯率最高的國家之一。 在監被釋放的人之中, 幾乎 10 個就有 7 個 會再次被逮捕, 呈現不斷犯罪和監禁的循環。
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.
因此當我開始在阿諾德基金會工作, 回頭來看這一大堆問題, 回頭來思考 該如何使用數據和邏輯分析來轉變 我們在紐澤西刑事司法採取的方式。 當我檢視現今 美國的刑事司法體制時, 我發現和當年在紐澤西起頭時 相同的情況, 毫無疑問我們在那做得更好了, 我也知道我們可以做得更好。
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.
因此我決定著眼在 使用數據和邏輯分析, 協助我們在公共安全中 做最重要的決定, 而那個決定即是 在某疑犯被逮捕時的判定, 不管是他們危及公共安全 該被拘留, 又或是他們沒有危及公共安全 而該被釋放。 每件在刑事案件中發生的事 都來自於這個決定。 這個決定影響每一件事, 影響每一個判決, 影響某疑犯是否接受藥物治療, 影響暴力和犯罪。 當我和全美法官談話時, ── 我現在常這麼做 ── 他們都說一樣的話, 那就是我們把危險人物關進牢裡, 讓不危險、非暴力的人出來。 他們很認真,也深信不疑。 但當你開始檢視數據, 附帶一提的是, 法官沒有看過數據, 當我們開始檢視數據, 就會一次又一次地發現 根本不是如此。 我們見到低風險犯人 佔了所有刑事司法總人數的一半, 我們發現他們在坐牢。 看看一個名為萊斯理的德州人, 他在一個寒冷冬夜偷了四件毛毯。 他被逮捕,關在牢裡, 保釋金為 3,500 美元, 他繳不出保釋金, 因此留在牢裡八個月, 直到案子開審, 花費納稅人超過 9,000 美元。 而在相反的那一端, 我們做得一樣很糟。 我們見到的是 高風險的犯人, 我們認為這些人若被釋放, 將會極有可能再次犯罪, 這些人全國大概有一半 都被釋放了,
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.
原因出自於我們做決定的方式。 當法官要做出關於風險的這些決定時, 他們是出於好意, 但是卻主觀地做出決定, 就像 20 年前的棒球球探, 用直覺和經驗 嘗試裁定某人會造成什麼危險。 他們主觀意識強, 而我們知道主觀的決定 會帶來什麼結果, 那就是我們經常會做錯。 我們在這裡需要的是 強而有力的數據和邏輯分析。
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.
我決定找出 強而有力的數據 和邏輯分析風險評估工具, 透過科學和客觀的方式 讓法官確實了解 他們面前的人 可能造成什麼風險。 我檢視整個國家, 發現美國的所有司法轄區之中 約有 5% 到 10% 確實使用某種型式的風險評估工具, 當我研究這些工具, 很快就理解事出何因。 這些工具管理起來貴得嚇人, 耗費時間, 而且只能限制在 他們所在的司法轄區。 基本上,他們無法擴大規模 或是移轉到其它地方。
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.
因此我建立了一個出色的團隊, 由數據科學家、研究人員 和統計學家組成, 來建立全面的風險評估工具, 如此一來每一位美國法官 都可以擁有客觀、科學的風險測量。 在我們設計的工具中, 我們搜集了 全美 150 萬個案件, 它們來自城市、郡市、 來自全國每一個州、 來自聯邦地區。 而這 150 萬個案件 是美國現今審判前 最大的資料組, 基本上我們能找出 900 個以上的危險因子, 我們可以從其中檢視, 嘗試找出什麼是最重要的。 我們發現有特定的九件事 在全國各地都很重要, 而那些是最容易看得出來的風險。 我們建置出一套全面的風險評估工具, 看起來就像這樣, 就像你看到的,我們會放入一些資訊, 但大部分都是很簡單的東西, 操作也簡單, 像是著眼在被告之前的犯罪記錄, 不管是否被判監禁, 不管是否曾涉入暴力案件, 或只是未曾出庭。 有了這個工具,我們可以預測三件事。 首先,如果某疑犯被釋放的話, 他會否再犯罪。 第二,這是第一次 ── 我想這十分重要 ── 我們可以預測某疑犯如果被釋放, 會不會從事暴力犯罪。 那是法官跟你說話時, 對他而言最重要的事。 第三,我們可以預測 某疑犯會否回到法庭上。 每一位美國法官都能使用這個工具, 因為它是以全面性的資料組建立。
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.
當法官們操作這個風險評估工具時, 看到的就是這個介面。 在最上面的是新犯罪活動評分, 六分當然是最高分, 接著在中間可以看到 「增加的暴力風險」。 意謂著這個疑犯 有較高機率的暴力風險, 法官應該再多看一眼。 接著,往底部看, 你會看到「未能出庭指數」, 這再次意謂著 某疑犯會回到法院的可能性。
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.
接下來我要說的十分重要。 我認為我們並不是應該排除 法官在這個過程中的 直覺和經驗。 不是這個意思。 我確實相信我們看到的問題 以及造成這些體制裡 重大錯誤的原因, 我們監禁低階、非暴力的人, 卻把高風險的危險人物放出來, 是因為我們沒有客觀的風險評估。 但我相信我們應該 拿這份依照數據產生的風險評估, 結合法官的直覺和經驗, 讓我們做出更好的決定。 這項工具七月一日開始 在肯塔基州全州使用, 我們還要擴展到全美許多轄區。 我們的目標很簡單, 就是讓每一個美國法官 在五年內都可運用這套 以數據為導向的風險工具。 我們現在設計 檢察官和警官也能使用這個風險工具, 試著讓這套系統在現今美國運作, 就像 50 年前的方式一樣, 根據直覺和經驗, 讓它改變為根據 數據和邏輯分析。
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.
現在這一切最棒的是 我們有一大堆工作等著我們去做, 有很多文化要改變, 但這一切最棒的是 我們知道那有用。 這是 Google 之所以 是 Google 的原因, 這就是為什麼所有這些棒球隊運用 魔球策略來贏球。 同樣對我們來說很棒的是 這是我們能夠改變 美國刑事司法體系的方式, 這是我們可以讓街道更安全的方式, 我們可以減少監獄支出, 我們可以讓體制更公平 且更正義。 有些人稱它為數據科學。 我稱它為魔球的刑事司法。
Thank you.
謝謝大家。
(Applause)
(掌聲)