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.
Postala sam državni tužilac 2007. godine u državi Nju Džerzi. Pre toga bila sam javni tužilac, prvo u kancelariji okružnog tužioca na Menhetnu, a zatim u Ministarstvu pravde Sjedinjenih Država.
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.
Ali, kada sam postala državni tužilac, desile su se dve stvari koje su promenile moj stav prema pravosudnom sistemu. Prvo je bilo to što sam postavljala pitanja za koja sam mislila da su sasvim obična. Želela sam da razumem koga hapsimo, koga tužimo, i koga stavljamo u državne pritvore i zatvore. Takođe sam htela da razumem da li pravimo dobre odluke zbog kojih ćemo svi biti sigurniji. Ali, nisam uspevala da dobijem povratne informacije. Ispostavilo se da se većina krivično-pravnih agencija poput moje ne bavi stvarima koje su zaista bitne. Zato sam nakon neverovatno frustrirajućih mesec dana otišla do konferencijske sale u kojoj je bilo mnogo detektiva i ogromnih hrpa slučajeva, detektivi su sedeli tamo sa žutim notesima hvatajući beleške. Pokušavali su da dođu do informacija koje sam tražila proveravajući slučaj za slučajem u proteklih pet godina. I kao što možete zamisliti, kada smo konačno došli do rezultata, oni nisu bili dobri. Ispostavilo se da smo radili na sitnim narko slučajevima sa ulica iza ćoška naše kancelarije u Trentonu. Druga stvar koja se dogodila
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.
je to da sam provela dan u policijskoj stanici u Kemdenu u Nju Džerziju. U to vreme, Kemden u Nju Džerziju je bio najopasniji grad u Americi. Zbog toga sam i vodila policijsku stanicu u Kemdenu. Provela sam dan u toj stanici, i uveli su me u prostoriju u kojoj su bili stariji policajci koji su predano radili na tome da smanje stopu kriminala u Kemdenu. Ono što sam videla u toj prostoriji, dok smo pričali o tome kako možemo smanjiti kriminal, bilo je mnogo policajaca sa mnogo lepljivih žutih blokčića. Oni bi uzeli taj žuti lepljivi papirić i napisali nešto na njemu i onda bi to stavili na tablu. Jedan reče: "Bila je pljačka pre dve nedelje. Nemamo osumnjičenih". Drugi reče: "Prošle nedelje je bila pucnjava u ovom komšiluku. Nema osumnjičenih". Nismo se bavili policijskim radom baziranim na informacijama. Borili smo se protiv kriminala
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.
sa žutim lepljivim papirićima. Obe ove situacije su me naterale da shvatim da zapravo ništa nismo uspevali. Nismo čak ni znali ko je bio u našem pravosudnom sistemu, nismo imali podatke o stvarima koje su bile bitne, nismo delili podatke, ni koristili analitičke sisteme ni alate za donošenje boljih odluka i suzbijanje kriminala. I po prvi put, počela sam da razmišljam o tome kako smo uopšte donosili odluke. Kad sam bila asistent okružnog tužioca, a onda javni tužilac, posmatrala sam slučajeve pred sobom i donosila odluke zasnovane na mom instinktu i iskustvu. Kada sam postala državni tužilac, mogla sam da vidim sistem u celosti i iznenadilo me je saznanje da je upravo to bio način na koji smo radili u celom sistemu, u policijskim stanicama, u kancelarijama tužilaca, u sudovima i u zatvorima. I vrlo brzo sam naučila da nismo obavljali dobar posao. Zato sam htela da uradim nešto drugačije. Želela sam da uvedem analitiku i podatke i rigorozne statističke analize u naš sistem rada. Ukratko, želela sam da uvedem "Manibol" u krivičnu pravdu.
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.
Kao što možda znate, "Manibol" se odnosi na ono što su uradili Oukland Atletiks, upotrebivši podatke i statistiku da bi shvatili kako odabrati igrače koji će dobijati utakmice i tako su od sistema u kojem su bejzbol posmatrači išli na teren i posmatrali igrače i birali ih pomoću svog instinkta i iskustva, posmatračkog iskustva i instinkta, prešli na sistem baziran na podacima i rigoroznoj statističkoj analizi ne bi li shvatili koje igrače treba da biraju da bi dobili utakmice.
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.
To je uspelo Oukland Atletiksima, a uspelo je i u državi Nju Džerzi. Skinuli smo Kemden sa vrha liste najopasnijih gradova u Americi. Stopa ubistava se smanjila za 41 odsto, što znači da je spašeno 37 života. Stopu kriminala u gradu smo smanjili za 26 odsto. Takođe smo promenili način na koji smo vodili krivična gonjenja. Tako smo sa sitnih narko slučajeva ispred naše zgrade prešli na slučajeve od državnog značaja, na slučajeve suzbijanja nasilja kod najnasilnijih prestupnika, gonjenje uličnih bandi, bavili smo se trgovinom oružja i drogom i političkom korupcijom.
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.
Sve ovo je izuzetno važno jer mislim da je obezbeđivanje javne bezbednosti najvažnija funkcija vlade. Ako nismo sigurni, ne možemo se ni obrazovati, ne možemo da budemo zdravi, ne možemo da radimo ništa što želimo da radimo u životu. I danas živimo u zemlji gde smo suočeni sa ozbiljnim problemima u pravosudnom sistemu. Svake godine se izvrši 12 miliona hapšenja. Velika većina ovih hapšenja su manji prestupi, poput prekršaja, 70 do 80 odsto. Manje od pet odsto svih hapšenja vezana su za nasilne zločine. Ipak, mi trošimo 75 milijardi dolara, milijardi sa velikim M, godišnje na lokalne i državne zatvore. Upravo sada, danas, 2,3 miliona ljudi se nalazi u zatvoru ili pritvoru. I javna bezbednost se nalazi pred neverovatnom izazovima jer imamo situaciju u kojoj dve trećine zatvorenika čeka na suđenje. Oni još uvek nisu ni optuženi za zločine. Samo čekaju na svoj dan suđenja. 67 odsto ljudi se vrati u društvo. Stopa našeg recidivizma je među najvišima u svetu. Skoro sedam od 10 otpuštenih zatvorenika će ponovo biti uhapšeni u konstantnom krugu kriminala i zatvoreništva.
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.
Kada sam počela da radim u Fondaciji Arnold, ponovo sam počela da se bavim većinom istih pitanja, i ponovo sam počela da mislim o tome kako smo koristili podatke i analitiku da bismo promenili način na koji smo se borili protiv kriminala u Nju Džerziju. I kada pogledam pravosudni sistem u SAD-u danas, osećam isto ono što sam osećala u državi Nju Džerzi kada sam tamo radila, a to je da apsolutno moramo da funkcionišemo bolje, a znam da možemo bolje.
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.
Tako sam odlučila da se fokusiram na upotrebu podataka i analize za lakše donošenje ključnih odluka o javnoj bezbednosti, a te odluke se odnose na odlučivanje toga da, kada je neko uhapšen, da li taj neko predstavlja opasnost za okolinu, i treba li ga zadržati, ili taj neko ne predstavlja opasnost za okolinu i treba da bude pušten. Sve što se desi u kriminalnim slučajevima proizilazi iz samo te jedne odluke. Ona utiče na sve. Utiče na kaznu. Utiče na to hoće li nekome biti prepisani lekovi. Utiče na kriminal i na nasilje. A kada pričam sa sudijama širom SAD-a, što sada konstantno radim, svi govore jednu te istu stvar, a to je da opasne ljude stavljamo u zatvor, a bezopasne, nenasilne puštamo napolje. Oni to misle i u to veruju. Ali, ako pogledate podatke koje, uzgred rečeno, sudije ni nemaju, kada ih proučimo, iznova i iznova se pokazuje da to uopšte nije tačno. Naći ćemo bezopasne prestupnike koji čine do 50 odsto krivičnih slučajeva, i saznaćemo da su svi oni u zatvoru. Uzmite na primer slučaj Leslija Čua, čoveka iz Teksasa, koji je jedne hladne zimske noći ukrao četiri pokrivača. Uhapšen je i držan u pritvoru pod kaucijom od 3.500 dolara, iznosom koji nikako nije mogao da plati. I ostao je tamo osam meseci dok njegov slučaj nije stigao na suđenje. Sve to je poreske obveznike koštalo više od 9000 dolara. S druge strane, i mi radimo podjednako loš posao. Ljudi za koje mislimo da su izuzetno opasni zločinci, za koje mislimo da imaju najveću šansu da će nakon izlaska iz zatvora napraviti nov zločin, vidimo da na nacionalnom nivou 50 odsto ovih ljudi biva pušteno na slobodu.
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.
Razlog tome je način na koji donosimo odluke. Sudije imaju najbolje namere kada donose odluke vezane za bezbednost, ali oni ih donose subjektivno. Oni su poput bejzbol posmatrača od pre 20 godina koji su koristili svoj instinkt i iskustvo kako bi odlučili koliko je ko opasan. Oni su subjektivni, i znamo šta se dešava kada se odluke donose subjektivno, a to je da često grešimo. Ono što nam sada treba su sigurni podaci i analitički sistemi.
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.
Ja sam zato odlučila da tražim sigurne podatke i analitičke alatke za procenu rizika, nešto što bi sudijama pomoglo da razumeju, na naučni i objektivni način, kakav rizik predstavlja osoba koja stoji pred njima. Tražila sam širom čitave zemlje i saznala sam da između 5 i 10 odsto svih jurisdikcija u SAD-u zapravo koriste neki tip alata za procenu rizika, a kada sam potražila te alatke ubrzo sam shvatila zašto je to tako. One su bile neverovatno skupe, zahtevale su previše vremena, bile su ograničene na lokalnu jurisdikciju u kojoj su napravljene. U suštini, nisu mogle da se savladaju niti da se premeste u druga mesta.
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.
Zato sam oformila fenomenalni tim naučnika, istraživača i statističara podataka da naprave univerzalni instrument za procenu rizika, kako bi svi do jednog sudije u SAD-u imali objektivnu, naučnu meru rizika. Za potrebe ovog instrumenta prikupili smo 1,5 miliona slučajeva iz svih krajeva Sjedinjenih Država. Iz gradova, iz opština, iz svake države u zemlji, iz federalnih okruga. U tih 1,5 miliona slučajeva koji čine najveću bazu pretkrivičnih podataka u SAD-u danas, uspeli smo da nađemo više od 900 rizičnih faktora koji nam mogu služiti za primer da bismo shvatili šta je najbitnije. Otkrili smo da postoji devet specifičnosti koje su bitne u svim delovima zemlje i koje su najčešće ukazivale na rizik. I tako smo napravili taj univerzalni instrument za procenu. On izgleda ovako. Kao što ćete videti, uneli smo neke podatke, ali većina je vrlo jednostavna, lako se upotrebljava, fokusira se na stvari kao što su pređašnji prestupi okrivljenih, na to jesu li služili kaznu, jesu li i ranije imali nasilne istupe, jesu li ikada propustili ročište na sudu. Sa ovim instrumentom možemo da predvidimo tri stvari. Prvo, hoće li neko napraviti novi zločin nakon što ga puste iz zatvora. Drugo, po prvi put, a ja mislim da je to izuzetno važno, možemo da predvidimo hoće li neko počiniti nasilje nakon što ga puste. A to je ubedljivo najvažnija stvar koju sudije kažu kada razgovarate s njima. I treće, možemo da predvidimo hoće li se neko vratiti na sud. Svaki sudija u Sjedinjenim Državama može da ga koristi jer je napravljen na univerzalnom skupu podataka.
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.
Ono što sudije primete kada koriste ovaj instrument je sledeće - to je jedna komandna tabla. Na vrhu vidite "Registar novih kriminalnih aktivnosti", šest je najviši stepen, a u sredini ćete videti "Povišen rizik od nasilja". To govori da je ta osoba neko sa povišenim rizikom od nasilja i da sudije treba da obrate pažnju na nju. Dole prema dnu ćete videti "Registar nepojavljivanja na sudu" što opet ukazuje na to kolike su šanse da se neko ponovo pojavi na sudu.
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.
Sada želim da kažem nešto izuzetno važno. Ja ne mislim da bi trebalo da eliminišemo instinkt i iskustvo sudije iz ovog procesa. Ne mislim to. Ja zapravo verujem da problem koji imamo i razlog zbog kojeg dolazi do neverovatnih grešaka u sistemu, zbog čega zatvaramo bezopasne, nenasilne ljude, a puštamo izuzetno opasne, nasilne ljude, leži u tome što ne postoji objektivna mera rizika. Ali ja verujem u sledeće, da procene o riziku koje se zasnivaju na podacima treba da ukombinujemo sa instinktom i iskustvom sudija kako bismo donosili bolje odluke. Ova alatka je počela da se koristi širom države Kentaki prvog jula, a to uskoro očekuje i druge jurisdikcije u SAD. Naš cilj je da, jednostavno, svaki sudija u SAD-u počne da koristi ovu alatku u narednih pet godina. Sada radimo na alatkama za tužioce i policajce, takođe, kako bismo sistem koji je i danas na snazi u Americi, kao i pre 50 godina, sistem baziran na instinktu i iskustvu, pretvorili u onaj koji se bazira na podacima i analizi.
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.
Odlična vest u vezi sa svim tim, a ostalo nam je zaista puno posla, i mnogo toga da promenimo, ali odlična stvar u vezi sa tim je činjenica da sada znamo da to radi. Zato je Gugl - Gugl, i zato svi bejzbol timovi koriste "Manibol" da dobiju utakmice. Još jedna odlična vest za nas je to što ovako možemo promeniti američki pravosudni sistem. Tako naše ulice možemo učiniti bezbednijima, možemo smanjiti troškove zatvora, i sam sistem može postati pravedniji i puno više fer. Neki to zovu naukom o podacima. Ja to zovem Manibol pravosudni sistem.
Thank you.
Hvala vam.
(Applause)
(Aplauz)