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Anne Milgram 談為何智慧型統計是打擊犯罪的關鍵

Anne Milgram: Why smart statistics are the key to fighting crime

 

Photo of three lions hunting on the Serengeti.

講者:Anne Milgram

2013年10月演講,2014年1月在TED@BCG San Francisco上線

 

翻譯:洪曉慧

編輯:朱學恒

簡繁轉換:洪曉慧

後制:洪曉慧

字幕影片後制:謝旻均

 

影片請按此下載

MAC及手持裝置版本請按此下載

閱讀中文字幕純文字版本

 

關於這場演講

Anne Milgram於2007年成為紐澤西州檢察總長時,隨即發現一些驚人的事實:她的團隊不僅不清楚自己將什麼樣的人關進監獄,也無法瞭解所做的決定是否確實能增進公共安全。因此她開始進行一連串激勵人心的探索行動,將數據分析及統計分析運用在美國刑事司法體系中。

 

關於Anne Milgram

Anne Milgram致力於使用數據及分析來打擊犯罪。

 

為什麼要聽她演講

Anne Milgram在司法體系步步高升,曾擔任曼哈頓地方檢察官辦公室刑事檢察官及任職於美國司法部。2007年,她被任命為紐澤西州檢察總長,當時她發現自己掌管21位檢察官及約30,000名執法人員。在那裡,她意識到刑事司法體系的崩壞,太過仰賴個人直覺及洞察力,並未善用日新月異的龐大數據資料。

 

從那時起,Milgram致力於嘗試解決這個問題,無論在紐澤西州,或目前任職的阿諾德基金會,她是基金會的刑事司法副總裁。

 

Milgram以優異成績畢業於羅格斯大學,擁有英國劍橋大學社會及政治理論哲學碩士學位。她於紐約大學法學院取得法律學位,目前擔任紐約大學法學院刑事司法管理中心資深研究員。她也是國際聖約之家董事會成員。

 

Anne Milgram的英語網上資料

Web: Arnold Foundation

 

[TED科技‧娛樂‧設計]

已有中譯字幕的TED影片目錄(繁體)(簡體)。請注意繁簡目錄是不一樣的。

 

Anne Milgram 談為何智慧型統計是打擊犯罪的關鍵

 

2007年,我成為紐澤西州的檢察總長。之前,我曾擔任刑事檢察官,最初任職於曼哈頓地方檢察官辦公室,後來任職於美國司法部。

 

但當我成為檢察總長後,兩件事改變了我對刑事司法的看法。第一是:我提出我認為相當基本的問題:我想瞭解我們逮捕、控告和關進監獄及拘留所的是哪些人;我也想瞭解我們做決定的方式是否能讓我們更安全。我無法獲得相關資訊。事實上,多數大型刑事司法機關,如我任職的機構,並未追蹤重要的事項。因此歷經約一個月的強烈挫折感後,我走進一間會議室,裡面滿是警探和堆積如山的案件卷宗。警探們坐在那裡,用黃色筆記本做筆記。他們試著尋找我想獲得的資訊,藉由逐一檢視過去五年的案件。你可以想像,最後獲得的成果差強人意。結論是,我們辦了許多低階街頭毒品案件,就在我們位於翠登的辦公室附近。

 

第二件是,我在紐澤西肯頓警局待了一天。當時紐澤西肯頓是美國最危險的城市,這正是我前往肯頓警局的原因。我在警局待了一天,我被帶進一間滿是資深警員的房裡,他們都卯盡全力、試著降低肯頓的犯罪率。當我們討論如何降低犯罪率時,我在房裡看見一堆警員拿著許多小型黃色便利貼。他們撕下一張黃色便利貼,在上面寫點東西,然後貼在佈告欄上。有的寫著:「兩星期前發生搶案,沒有嫌犯。」有的寫著:「上星期附近發生槍擊案,沒有嫌犯。」我們並未藉由資訊處理治安,基本上我們試著用黃色便利貼打擊犯罪。

 

好,這兩件事讓我意識到,從根本來說,我們失敗了。我們甚至對刑事司法體系涉及的對象一無所知。我們沒有任何關於重要事項的資訊。我們並未共用資訊,運用分析或工具幫助我們做更好的決定,以減少犯罪。第一次地,我開始思考我們做決定的方式。當我擔任地方助理檢察官及聯邦檢察官時,我審視眼前的案件,通常基於直覺和經驗做決定。當我成為檢察總長時,我能全面檢視這個體系。令人驚訝的是,我發現這正是整個體系的做法-在警局、檢察署、法院和監獄。我隨即瞭解這並非適當做法,因此我想採用不同方式。我希望將數據、分析學和精密統計分析運用在工作上。簡言之,我希望將「魔球策略」運用在刑事司法上。

 

好,魔球,如在座許多人所知,是奧克蘭運動家隊的策略。他們藉由智慧型數據和統計,瞭解如何選擇能幫球隊贏球的球員。過去的體系基於球探的判斷,他們通常親自觀察球員,藉由直覺和經驗-藉由球探的直覺和經驗選擇球員。現在轉變成使用智慧型數據及精密統計分析,瞭解如何選擇能幫他們贏球的球員。

 

這適用於奧克蘭運動家隊,也適用於紐澤西州。我們使肯頓不再名列美國最危險城市名單之首;我們使謀殺案減少了41%,這意味著拯救了37條人命。我們使本市所有犯罪行為減少26%,我們也改變了刑事起訴方式。因此我們從處理發生在辦公大樓外的低階毒品犯罪,轉為處理全州範圍內的重要案件。例如減少高危險暴力罪犯的暴力犯罪,起訴街頭幫派、槍枝、毒品交易及政治貪污。

 

這一切都十分重要。因為在我看來,公共安全是政府最重要的功能。如果無法享有安全,就無法接受教育、擁有健康,無法從事任何想做的事。目前我們居住的國家正面臨嚴重的刑事司法問題。我們每年有1200萬件拘捕行動,絕大多數拘捕行動是針對低階犯罪,例如輕罪,佔70%到80%,不到5%的拘捕行動是針對暴力犯罪。但我們每年花費75 b(750億美元)-b代表十億-用於州及地方的矯正支出。目前有230萬人身處監獄和拘留所,我們面臨難以想像的公共安全挑戰。因為我們面臨的處境是,監獄中有2/3的人正等待審判。他們尚未被定罪,他們只是等待開庭的日子。67%的人會重返監獄,我們是全球再犯率最高的國家之一,出監者幾乎10個有7個會再次被逮捕,形成犯罪與監禁的常態循環。

 

因此當我開始任職於阿諾德基金會時,我回顧許多相關問題;我開始回顧我們如何藉由數據及分析改變紐澤西的刑事司法體系。當我審視現今美國刑事司法體系時,我發現最初在紐澤西時所遭遇的相同情況。我們當然必須做得更好,我知道我們能做得更好。

 

因此我決定著眼於藉由數據和分析,協助進行公共安全方面最關鍵的決定。這個決定在於,當某人被逮捕時,決定他們是否可能對公共安全造成風險,應被拘留;或他們是否不至於對公共安全造成風險,應被釋放。刑事案件中發生的一切都來自這個決定,這影響了一切。影響了判決、影響了某人是否需接受藥物治療、影響了犯罪和暴力。當我和全美法官談話時-現在我經常這麼做-他們都提出相同說法:我們把危險份子關進牢裡,釋放不具危險性、非暴力的人。他們確實這麼認為,且深信不疑。但當你開始檢視數據-順帶一提,法官並未這麼做-當我們開始檢視數據,我們一再發現事實並非如此。我們發現低風險罪犯佔刑事司法總人數50%,我們發現他們在監獄裡。以Leslie Chew為例,他是德州人,在寒冷的冬夜裡偷了四條毛毯。他被逮捕,關進監獄,需繳交3,500美元保釋金。這是一筆他付不起的金額,因此他在牢裡待了八個月,直到他的案子進入審判,這花費納稅人超過9,000美元。另一方面,我們做得同樣糟。我們發現高風險罪犯-這是我們認為若被釋放最可能再次犯罪的人-我們發現全國有50%這樣的人被釋放了,原因在於我們做決定的方式。

 

當法官做出這些關乎風險的決定時,意圖是良善的;但他們以主觀進行判斷。他們就像20年前的棒球球探,藉由直覺和經驗,試圖判斷某人是否會造成風險。他們以主觀判斷;我們知道主觀的決定會帶來什麼後果,那就是經常導致錯誤。我們在這方面需要的是有力的數據和分析。

 

我決定尋找有力的數據和分析性風險評估工具,藉由科學及客觀方式,使法官確實瞭解他們面前的人可能造成什麼風險。我檢視整個國家,發現全美轄區有5%到10%確實使用某種型式的風險評估工具。當我檢視這些工具,隨即明白其中原因。這些工具應用起來十分昂貴、相當耗時、僅適用於當地轄區,因此基本上無法擴大規模或轉移到其它地方。

 

因此我打造了一個出色的團隊,由數據科學家、研究人員及統計學家組成,建立全面性風險評估工具。如此一來,美國所有法官都可進行客觀、科學性的風險評估。在我們建立的工具中,我們收集了150萬個案件,來自全美各地,來自各縣市、全國各州及聯邦特區。藉由這150萬個案件-這是目前美國最大的審判前資料庫-基本上我們能找到多個可觀察的風險因子,試著判斷何者最重要。我們發現有九項資訊對全國來說都很重要,最擅於預測風險。因此我們建立了全面性風險評估工具,看起來像這樣。如你所見,我們放入一些資訊,但多半是相當簡單的資訊,操作簡單,著眼於-例如被告的前科。他們是否曾被判刑入監、他們是否曾涉入暴力事件、他們是否甚至並未出庭。藉由這項工具,我們可預測三件事。第一,如果某人被釋放,是否可能再次犯罪。第二,有史以來第一次-我認為這相當重要-我們可預測,如果某人被釋放,是否可能從事暴力犯罪;這是我和法官談話時他們認為最重要的事。第三,我們可預測某人是否會出庭。美國每位法官都能使用,因為它以全面性資料庫建立。

 

如果操作這個風險評估工具,法官看到的是這個介面。在頂端,你看見新犯罪活動評分;六分當然是最高分。在中間,你看見「暴力風險增長度」,意味著這個人的暴力風險增長機率,法官應多加考量。接著,在下方,你看見未出庭指數,意味著某人出庭的可能性。

 

現在我想說明一些非常重要的事。我並非認為應將法官的直覺和經驗排除於這個過程外;我不這麼認為。我確實相信我們看見的問題,及體制中不可思議的錯誤-我們監禁低階、非暴力罪犯,卻釋放高風險的危險份子,原因在於我們並未客觀評估風險。但我認為我們應將這項基於數據的風險評估,結合法官的直覺和經驗,使我們做出更好的決定。這項工具於7月1日在肯塔基州全面施行,我們即將推廣到全美許多轄區。我們的目標十分簡單,就是讓美國每一位法官,在未來五年內全面使用這套基於數據的風險評估工具。我們現在正致力於設計適用於檢察官及警員的風險評估工具,試著使這套系統如50年前的方式運作於現今的美國。基於直覺和經驗,使它轉變成運用數據及分析的系統。

 

好,最棒的是,我們仍有許多努力的空間,仍有許多文化待改善。但最棒的是,我們知道這是有效的。這正是Google之所以為Google的原因;正是所有運用魔球策略的棒球隊贏球的原因。對我們來說,另一項好消息是,我們可藉此改變美國刑事司法體系,我們可藉此使街道更安全,我們可藉此減少監獄支出,我們可使體制更公平、公正。有些人稱它為數據科學,我稱它為刑事司法魔球策略。

 

謝謝。

 

(掌聲)

 

以下為系統擷取之英文原文

About this talk
When she became the attorney general of New Jersey in 2007, Anne Milgram quickly discovered a few startling facts: not only did her team not really know who they were putting in jail, but they had no way of understanding if their decisions were actually making the public safer. And so began her ongoing, inspirational quest to bring data analytics and statistical analysis to the US criminal justice system.
 
About Anne Milgram
Anne Milgram is committed to using data and analytics to fight crime.
 
About the transcript
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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Thank you.

(Applause)


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