12:42
TED@BCG San Francisco

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

アン・ミルグラム: 犯罪抑止の鍵はハイテク統計

Filmed:

2007年にニュージャージー州の州司法長官になったアン・ミルグラムは驚くべき事実に気づきました。自分達がどんな人間を刑務所に送っているのか把握していない上に、判決によって社会が本当に安全になったかを知る術が無かったのです。ここから彼女のインスピレーションに満ちた探求、すなわち、アメリカの刑事司法制度にデータ分析と統計分析を取り入れる試みが始まったのです。

- Criminal justice reformer
Anne Milgram is committed to using data and analytics to fight crime. Full bio

In 2007, I became the attorney general
2007年に 私はニュージャージー州の
00:12
of the state of New Jersey.
州司法長官になりました
00:15
Before that, I'd been a criminal prosecutor,
それまではマンハッタン地区の
00:17
first in the Manhattan district attorney's office,
地区検察局や
00:19
and then at the United States Department of Justice.
司法省で検察官をしていました
00:22
But when I became the attorney general,
州司法長官になって
00:24
two things happened that changed
the way I see criminal justice.
刑事裁判の見方が変わる
2つの出来事がありました
00:26
The first is that I asked what I thought
1つ目は根本的な疑問を
00:30
were really basic questions.
抱くようになったことです
00:32
I wanted to understand who we were arresting,
自分達が どんな人間を
00:35
who we were charging,
逮捕し 告訴して
00:37
and who we were putting in our nation's jails
刑務所や拘置所に
送っているのだろうか?
00:39
and prisons.
刑務所や拘置所に
送っているのだろうか?
00:41
I also wanted to understand
また 社会が
もっと安全になるような
00:43
if we were making decisions
また 社会が
もっと安全になるような
00:44
in a way that made us safer.
判決を下しているのだろうか?
00:46
And I couldn't get this information out.
でも そういう情報は
手に入りませんでした
00:48
It turned out that most big criminal justice agencies
ニュージャージー州を含む
大規模な刑事司法機関では
00:51
like my own
肝心な点を追跡調査して
いなかったのです
00:55
didn't track the things that matter.
肝心な点を追跡調査して
いなかったのです
00:56
So after about a month of being incredibly frustrated,
不満を募らせながら
1か月待ち
00:58
I walked down into a conference room
刑事が居並ぶ会議室に
01:02
that was filled with detectives
乗り込みました
01:04
and stacks and stacks of case files,
事件ファイルが
山積みになっていて
01:06
and the detectives were sitting there
皆 黄色のレポート用紙に
01:08
with yellow legal pads taking notes.
メモを取っていました
01:10
They were trying to get the information
必要な情報を
手に入れるために
01:12
I was looking for
必要な情報を
手に入れるために
01:13
by going through case by case
過去5年間の事件を
01:15
for the past five years.
全てチェックさせたのです
01:17
And as you can imagine,
すると 皆さんの想像通り —
01:19
when we finally got the results, they weren't good.
ひどい状況が明らかになりました
01:20
It turned out that we were doing
調査によると扱った事件の多くが
01:23
a lot of low-level drug cases
トレントンにある自分達の職場の
01:25
on the streets just around the corner
周辺で発生した ―
01:27
from our office in Trenton.
比較的軽い
麻薬犯罪だったのです
01:28
The second thing that happened
2つ目の出来事とは
01:30
is that I spent the day in the Camden,
New Jersey police department.
ニュージャージー州
カムデン警察署での経験です
01:32
Now, at that time, Camden, New Jersey,
当時カムデンは
01:35
was the most dangerous city in America.
アメリカで最も危険な街でした
01:37
I ran the Camden Police
Department because of that.
だから私がカムデン署を
指揮することになったのです
01:40
I spent the day in the police department,
私はその日 署を訪れ
01:44
and I was taken into a room
with senior police officials,
カムデンの犯罪を減らそうと
01:46
all of whom were working hard
全力を尽くしている —
01:49
and trying very hard to reduce crime in Camden.
警察幹部の部屋に通されました
01:50
And what I saw in that room,
私達が犯罪撲滅について
01:54
as we talked about how to reduce crime,
話し合っている時 目にしたのは
01:55
were a series of officers with a
lot of little yellow sticky notes.
黄色い付箋を
大量に持った警官達でした
01:58
And they would take a yellow sticky
and they would write something on it
皆 付箋にメモを書いて
ボードに貼り
02:01
and they would put it up on a board.
次々と報告していきます
02:04
And one of them said,
"We had a robbery two weeks ago.
「2週間前 強盗事件発生 —
02:06
We have no suspects."
容疑者情報なし」
02:08
And another said, "We had a shooting in this neighborhood last week. We have no suspects."
「先週この付近で銃撃事件発生
容疑者情報なし」・・・
02:10
We weren't using data-driven policing.
捜査にデータを
活用していなかったのです
02:15
We were essentially trying to fight crime
黄色の付箋だけを頼りに
02:18
with yellow Post-it notes.
犯罪に闘いを
挑むようなものです
02:20
Now, both of these things made me realize
この2つの出来事がきっかけで
02:22
fundamentally that we were failing.
根本的な失敗に
気づいたのです
02:24
We didn't even know who was
in our criminal justice system,
私達は どんな人間が
裁判を受けているか知らず
02:28
we didn't have any data about
the things that mattered,
必要なデータがない上に
共有もしていない・・・
02:31
and we didn't share data or use analytics
適切な判断を下したり
犯罪を減らすための
02:34
or tools to help us make better decisions
データ分析手法やツールさえ
使っていませんでした
02:36
and to reduce crime.
データ分析手法やツールさえ
使っていませんでした
02:39
And for the first time, I started to think
私は 初めて判決に至る過程を
02:41
about how we made decisions.
検討し始めました
02:43
When I was an assistant D.A.,
地方検事補 時代も
02:45
and when I was a federal prosecutor,
連邦検事だった時も
02:46
I looked at the cases in front of me,
私は目の前の事件に集中し
02:48
and I generally made decisions based on my instinct
勘と経験だけを頼りに
02:50
and my experience.
判決を下してきました
02:52
When I became attorney general,
州司法長官になって
02:54
I could look at the system as a whole,
制度全体が見えるようになると
02:56
and what surprised me is that I found
驚くべき発見がありました
02:57
that that was exactly how we were doing it
司法制度における どの部門でも
02:59
across the entire system --
勘と経験だけで
判断していたのです
03:01
in police departments, in prosecutors's offices,
警察署でも検察局でも
03:03
in courts and in jails.
裁判所でも刑務所でも同じでした
03:06
And what I learned very quickly
上手くいっていないのは
03:09
is that we weren't doing a good job.
明白だったので 別の方法が
03:11
So I wanted to do things differently.
必要だと感じました
03:14
I wanted to introduce data and analytics
データや分析法や
03:16
and rigorous statistical analysis
厳密な統計解析の導入が
03:19
into our work.
必要だと思ったのです
03:21
In short, I wanted to moneyball criminal justice.
要は刑事司法制度界の
「マネーボール」です
03:22
Now, moneyball, as many of you know,
ご存じの通り マネーボールとは
03:25
is what the Oakland A's did,
オークランド・A's が
03:27
where they used smart data and statistics
勝利に貢献できる選手を
獲得するために
03:29
to figure out how to pick players
データと統計を
駆使した手法のことです
03:31
that would help them win games,
データと統計を
駆使した手法のことです
03:32
and they went from a system that
was based on baseball scouts
かつてはスカウトが
実際に選手を見て
03:34
who used to go out and watch players
勘と経験を頼りに
03:37
and use their instinct and experience,
勘と経験を頼りに
03:39
the scouts' instincts and experience,
選手を獲得していましたが
03:40
to pick players, from one to use
今ではデータと
厳密な統計分析によって
03:42
smart data and rigorous statistical analysis
今ではデータと
厳密な統計分析によって
03:44
to figure out how to pick players
that would help them win games.
勝利に貢献できる選手を
選びだしています
03:47
It worked for the Oakland A's,
A's で成功した この手法は
03:50
and it worked in the state of New Jersey.
ニュージャージーでも成功しました
03:52
We took Camden off the top of the list
カムデンは全米で最も危険な街という
03:54
as the most dangerous city in America.
汚名を返上しました
03:56
We reduced murders there by 41 percent,
殺人事件は41%減少しました
03:58
which actually means 37 lives were saved.
つまり37人の命が
救われたのです
04:01
And we reduced all crime in the city by 26 percent.
犯罪の総数は26%減少しました
04:04
We also changed the way
we did criminal prosecutions.
また刑事訴追の方法も
改めました
04:08
So we went from doing low-level drug crimes
私達の身の回りで起こる ―
04:11
that were outside our building
比較的軽い麻薬犯罪よりも
04:13
to doing cases of statewide importance,
州全体に関わる事件 例えば
04:15
on things like reducing violence
with the most violent offenders,
重大な暴力犯罪の抑止や
04:17
prosecuting street gangs,
ストリートギャングの摘発 —
04:20
gun and drug trafficking, and political corruption.
銃と麻薬の取引や
政治汚職の摘発に力を入れました
04:22
And all of this matters greatly,
どれも非常に重要です
04:26
because public safety to me
なぜなら 治安こそが
04:28
is the most important function of government.
政府の最も重要な
仕事だと考えるからです
04:30
If we're not safe, we can't be educated,
安全でなければ
教育も健康も保障できません
04:33
we can't be healthy,
安全でなければ
教育も健康も保障できません
04:35
we can't do any of the other things
we want to do in our lives.
自分のやりたいことが
不可能になるのです
04:36
And we live in a country today
現在この国は
04:39
where we face serious criminal justice problems.
刑事司法制度上の
深刻な問題を抱えています
04:41
We have 12 million arrests every single year.
毎年1,200万人が逮捕されますが
04:44
The vast majority of those arrests
そのほとんどは
04:48
are for low-level crimes, like misdemeanors,
重要度の低い軽犯罪で
04:50
70 to 80 percent.
70〜80%を占めます
04:53
Less than five percent of all arrests
凶悪犯罪は 逮捕総数の
04:55
are for violent crime.
わずか5%未満です
04:57
Yet we spend 75 billion,
それでも州や地方で
04:58
that's b for billion,
それでも州や地方で
05:01
dollars a year on state and local corrections costs.
犯罪者の更正にかかるコストは
750億ドルにのぼります
05:02
Right now, today, we have 2.3 million people
現在 230万人が拘置所や刑務所に
05:06
in our jails and prisons.
収監されています
05:09
And we face unbelievable public safety challenges
これは治安上
極めて深刻な状況です
05:11
because we have a situation
というのも拘置所に
05:14
in which two thirds of the people in our jails
収容されている人間の
3分の2は
05:16
are there waiting for trial.
裁判の開始を
待っているだけなのです
05:18
They haven't yet been convicted of a crime.
彼らは有罪判決を
受けたわけではなく
05:20
They're just waiting for their day in court.
ただ出廷の時を待っています
05:22
And 67 percent of people come back.
さらに67%が再犯を重ねます
05:24
Our recidivism rate is amongst
the highest in the world.
州の再犯率は
世界的に見ても最悪です
05:28
Almost seven in 10 people who are released
刑務所から10人釈放されても
05:31
from prison will be rearrested
7人程度が再逮捕され
05:33
in a constant cycle of crime and incarceration.
犯罪と監獄生活を
繰り返しています
05:35
So when I started my job at the Arnold Foundation,
私がアーノルド財団で
働きはじめた時 —
05:39
I came back to looking at a lot of these questions,
これまでの問題を振り返りました
05:41
and I came back to thinking about how
データと分析法を どう活用して
05:44
we had used data and analytics to transform
ニュージャージー州の刑事司法制度を
05:46
the way we did criminal justice in New Jersey.
改革したか振り返ったのです
05:48
And when I look at the criminal justice system
現在のアメリカの
05:51
in the United States today,
刑事司法制度には
05:53
I feel the exact same way that I did
ニュージャージー州と
05:54
about the state of New Jersey when I started there,
同じ課題があると思います
05:56
which is that we absolutely have to do better,
つまり まだまだ
改善の余地はあるし
05:59
and I know that we can do better.
改善できるはずです
06:02
So I decided to focus
そこで 私が集中して
取り組むことにしたのは
06:04
on using data and analytics
そこで 私が集中して
取り組むことにしたのは
06:05
to help make the most critical decision
治安上 最も重要な判断で
06:08
in public safety,
データ分析を使うことです
06:10
and that decision is the determination
そのような判断の一つは
06:12
of whether, when someone has been arrested,
誰かを逮捕した時に
06:14
whether they pose a risk to public safety
治安上のリスクが高いと
考えて勾留するか
06:16
and should be detained,
治安上のリスクが高いと
考えて勾留するか
06:18
or whether they don't pose a risk to public safety
リスクは低いと考えて
釈放するかを
06:20
and should be released.
決めるような場合です
06:22
Everything that happens in criminal cases
この判断こそが
06:24
comes out of this one decision.
刑事裁判の出発点で
06:26
It impacts everything.
全てに影響を及ぼします
06:27
It impacts sentencing.
量刑の判断にも
06:29
It impacts whether someone gets drug treatment.
薬物治療の必要性にも
06:30
It impacts crime and violence.
暴力や犯罪にも影響を及ぼします
06:32
And when I talk to judges around the United States,
最近 全国の判事から
06:34
which I do all the time now,
話を聞く機会が多いのですが
06:36
they all say the same thing,
全員が こう言います
06:38
which is that we put dangerous people in jail,
「自分達は 危険な人間を
刑務所に送り
06:40
and we let non-dangerous, nonviolent people out.
そうではない人間は
釈放している」
06:43
They mean it and they believe it.
全員がそう確信しています
06:47
But when you start to look at the data,
でも判事達は
06:49
which, by the way, the judges don't have,
データを持っていません
06:51
when we start to look at the data,
実際にデータを検討すると
06:53
what we find time and time again,
それに当てはまらないケースが
06:55
is that this isn't the case.
しばしば見つかるのです
06:57
We find low-risk offenders,
刑事裁判を受けた者の
06:59
which makes up 50 percent of our
entire criminal justice population,
50%を占める
危険度の低い犯罪者が
07:01
we find that they're in jail.
刑務所に入っています
07:05
Take Leslie Chew, who was a Texas man
例えばテキサス出身の
レズリー・チューは
07:07
who stole four blankets on a cold winter night.
寒い冬の夜に
毛布を4枚盗んで
07:09
He was arrested, and he was kept in jail
逮捕されましたが
07:12
on 3,500 dollars bail,
3,500ドルの保釈金を
07:15
an amount that he could not afford to pay.
払う事ができず
拘置所に入りました
07:17
And he stayed in jail for eight months
それから裁判が始まるまで
07:20
until his case came up for trial,
8か月も勾留されたのです
07:22
at a cost to taxpayers of more than 9,000 dollars.
納税者の負担額は
9,000ドル以上になります
07:24
And at the other end of the spectrum,
逆の場合でも
07:28
we're doing an equally terrible job.
状況は深刻です
07:30
The people who we find
極めて危険性が
高いと判断され
07:33
are the highest-risk offenders,
極めて危険性が
高いと判断され
07:34
the people who we think have the highest likelihood
釈放された場合
再犯の可能性が
07:36
of committing a new crime if they're released,
非常に高い犯罪者の内 ―
07:39
we see nationally that 50 percent of those people
実に50%が
釈放されています
07:41
are being released.
実に50%が
釈放されています
07:44
The reason for this is the way we make decisions.
こうなった原因は
判断の下し方にあります
07:46
Judges have the best intentions
判事はリスクの見極めに
07:49
when they make these decisions about risk,
最善を尽くしていますが
07:50
but they're making them subjectively.
判断が主観的なのです
07:52
They're like the baseball scouts 20 years ago
20年前 野球のスカウト達が
07:55
who were using their instinct and their experience
勘と経験だけを頼りに
07:57
to try to decide what risk someone poses.
リスクを評価したのと
同じことをしているのです
07:59
They're being subjective,
判事達は主観で判断しています
08:02
and we know what happens
with subjective decision making,
ただ主観的な判断は
08:03
which is that we are often wrong.
しばしば誤りにつながります
08:06
What we need in this space
この分野に必要なのは
08:09
are strong data and analytics.
確かなデータと分析法なのです
08:11
What I decided to look for
私が求めていたものは
08:13
was a strong data and analytic risk assessment tool,
確固たるデータと
分析的なリスク評価ツール —
08:15
something that would let judges actually understand
すなわち
判事の前にいる人間が
08:18
with a scientific and objective way
どんな危険性を持つかを
08:20
what the risk was that was posed
科学的 客観的に
08:23
by someone in front of them.
捉えるためのツールでした
08:24
I looked all over the country,
全国的に見ると
08:26
and I found that between five and 10 percent
何らかのリスク評価ツールを
08:28
of all U.S. jurisdictions
利用しているのは
08:30
actually use any type of risk assessment tool,
全体の
わずか5〜10%でした
08:31
and when I looked at these tools,
実際のツールを見ていくと
08:34
I quickly realized why.
原因はすぐにわかりました
08:35
They were unbelievably expensive to administer,
どれも管理コストが恐ろしく高く
08:37
they were time-consuming,
時間もかかり
08:40
they were limited to the local jurisdiction
地元でしか使えない —
08:42
in which they'd been created.
ツールばかりだったのです
08:44
So basically, they couldn't be scaled
そのため 基本的に
08:45
or transferred to other places.
対象エリアを広げたり
転用はできませんでした
08:47
So I went out and built a phenomenal team
だから私はデータ科学者や
08:49
of data scientists and researchers
研究者や統計学者からなる
08:51
and statisticians
優秀なチームを編成し
08:53
to build a universal risk assessment tool,
どこでも使える
リスク評価ツールを製作しました
08:55
so that every single judge in
the United States of America
目指したのは全米の判事全員が
08:58
can have an objective, scientific measure of risk.
客観的かつ科学的な
リスク評価ができることです
09:00
In the tool that we've built,
このツールを使って
09:05
what we did was we collected 1.5 million cases
私達は150万件の
事例を集めました
09:06
from all around the United States,
アメリカ全土 つまり
09:09
from cities, from counties,
市や郡の裁判所 ―
09:11
from every single state in the country,
すべての州裁判所と
09:12
the federal districts.
連邦地裁からです
09:14
And with those 1.5 million cases,
そして 公判前の
事例データとしては
09:16
which is the largest data set on pretrial
全米で最大規模となる —
09:18
in the United States today,
この150万例から
09:20
we were able to basically find that there were
900以上のリスク要因を見つけ
09:22
900-plus risk factors that we could look at
どの要素が
最も重要なのかを
09:23
to try to figure out what mattered most.
突き止めようとしました
09:27
And we found that there were nine specific things
その結果 全国的に共通し
09:30
that mattered all across the country
最もリスクを予測しやすい
09:32
and that were the most highly predictive of risk.
要因が9つあると
わかりました
09:34
And so we built a universal risk assessment tool.
こうして ユニバーサルな
リスク評価ツールが出来たのです
09:37
And it looks like this.
画面をご覧ください
09:41
As you'll see, we put some information in,
多少の入力は必要ですが
09:42
but most of it is incredibly simple,
全体的に とてもシンプルで
09:45
it's easy to use,
使うのは簡単です
09:47
it focuses on things like the
defendant's prior convictions,
このツールが扱うのは
被告の前科や
09:48
whether they've been sentenced to incarceration,
禁固刑を受けた経験
暴力事件への関与や
09:51
whether they've engaged in violence before,
禁固刑を受けた経験
暴力事件への関与や
09:53
whether they've even failed to come back to court.
裁判所に出頭しなかった前歴です
09:55
And with this tool, we can predict three things.
このツールで
3つの予測が可能になります
09:58
First, whether or not someone will commit
まず 釈放後に別の犯罪を
犯す可能性の予測です
10:00
a new crime if they're released.
まず 釈放後に別の犯罪を
犯す可能性の予測です
10:02
Second, for the first time,
2つ目は 初の試みですが
10:04
and I think this is incredibly important,
とても重要と思われることで
10:05
we can predict whether someone will commit
釈放後に暴力事件を
10:07
an act of violence if they're released.
起こす可能性の予測です
10:09
And that's the single most important thing
これは どの判事も
10:11
that judges say when you talk to them.
重要な要素だと考えています
10:13
And third, we can predict whether someone
最後に 裁判所に出頭する ―
10:15
will come back to court.
可能性の予測です
10:16
And every single judge in the
United States of America can use it,
アメリカの判事なら
誰でも このツールを利用できます
10:18
because it's been created on a universal data set.
どこにでも当てはまる
データに基づいているからです
10:22
What judges see if they run the risk assessment tool
リスク評価ツールを起動すると
10:25
is this -- it's a dashboard.
ダッシュボードが現れます
10:28
At the top, you see the New Criminal Activity Score,
一番上は
「新規犯罪スコア」で
10:30
six of course being the highest,
最高値は「6」です
10:33
and then in the middle you
see, "Elevated risk of violence."
その下は「暴力リスクの増加度」です
10:35
What that says is that this person
この値は 対象となる人物が
10:37
is someone who has an elevated risk of violence
暴力的な傾向が強いかどうかを
10:39
that the judge should look twice at.
判事が検討するために使います
10:41
And then, towards the bottom,
さらに その下 ―
10:43
you see the Failure to Appear Score,
「未出頭スコア」は
10:44
which again is the likelihood
裁判所に再び出頭する
可能性を示しています
10:46
that someone will come back to court.
裁判所に再び出頭する
可能性を示しています
10:48
Now I want to say something really important.
さて ここで強調したい点があります
10:51
It's not that I think we should be eliminating
私はリスク評価において
判事の勘と経験を
10:53
the judge's instinct and experience
すべて排除すべきだとは
10:56
from this process.
考えていません
10:58
I don't.
そうすべきではありません
10:59
I actually believe the problem that we see
私達が直面している問題 つまり
11:00
and the reason that we have
these incredible system errors,
非暴力的な者を刑務所に入れ
11:02
where we're incarcerating
low-level, nonviolent people
リスクが高い危険な者を
釈放するという
11:05
and we're releasing high-risk, dangerous people,
制度上のひどい誤りが
起きる原因は
11:08
is that we don't have an objective measure of risk.
客観的にリスクを評価する
手段がないことです
11:12
But what I believe should happen
しかし これからは
11:14
is that we should take that
data-driven risk assessment
データに基づくリスク評価と
11:16
and combine that with the
judge's instinct and experience
判事の勘や経験を
組み合わせることで
11:18
to lead us to better decision making.
よりよい判断を
目指すべきだと考えます
11:21
The tool went statewide in Kentucky on July 1,
このツールは7月1日に
ケンタッキー州全域で稼動し
11:24
and we're about to go up in a
number of other U.S. jurisdictions.
他の管轄区域にも
広がりつつあります
11:28
Our goal, quite simply, is that every single judge
私達の目標は ただひとつ —
5年以内に
11:31
in the United States will use a data-driven risk tool
全米の判事が
このリスク評価ツールを
11:34
within the next five years.
使うようになることです
11:36
We're now working on risk tools
私達は現在 ―
11:38
for prosecutors and for police officers as well,
検察官や警官用の
ツールにも取り掛かっています
11:39
to try to take a system that runs today
現在のやり方は
11:43
in America the same way it did 50 years ago,
50年前と同じで
11:45
based on instinct and experience,
勘と経験に頼っていますが
11:48
and make it into one that runs
これをデータと分析による
システムに替えたいのです
11:50
on data and analytics.
これをデータと分析による
システムに替えたいのです
11:52
Now, the great news about all this,
確かに まだ課題は
11:55
and we have a ton of work left to do,
山ほど残っています
11:56
and we have a lot of culture to change,
考え方も変える必要があります
11:58
but the great news about all of it
ただ この変革の素晴らしい点は
12:00
is that we know it works.
効果が証明されていることです
12:02
It's why Google is Google,
Googleはデータ分析により成功し
12:04
and it's why all these baseball teams use moneyball
野球チームは勝つために
「マネーボール」を採用するのです
12:06
to win games.
野球チームは勝つために
「マネーボール」を採用するのです
12:08
The great news for us as well
この手法が素晴らしいのは
12:10
is that it's the way that we can transform
アメリカの刑事司法制度を
12:12
the American criminal justice system.
改善できるからです
12:14
It's how we can make our streets safer,
私達の街はもっと安全になり
12:16
we can reduce our prison costs,
刑務所のコストは減り
12:18
and we can make our system much fairer
制度は よりフェアで
公正なものになるのです
12:21
and more just.
制度は よりフェアで
公正なものになるのです
12:23
Some people call it data science.
これを「データ科学」と呼ぶ人もいますが
12:24
I call it moneyballing criminal justice.
私にとっては刑事司法界の
マネーボールなのです
12:26
Thank you.
ありがとうございました
12:29
(Applause)
(拍手)
12:31
Translated by Kazunori Akashi
Reviewed by Tomoyuki Suzuki

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About the Speaker:

Anne Milgram - Criminal justice reformer
Anne Milgram is committed to using data and analytics to fight crime.

Why you should listen

Anne Milgram is focused on reforming systems through smart data, analytics and technology. She is currently a Professor of Practice and Distinguished Scholar in Residence at New York University School of Law, where she is building a Criminal Justice Innovation Lab, dedicated to using data and technology to transform the American criminal justice system. She also teaches seminars on criminal justice policy and human trafficking. Milgram began her career as a criminal prosecutor, serving in state, local and federal prosecution offices.  She then became the Attorney General of the State of New Jersey, where she served as the Chief Law Enforcement Officer for the State and oversaw the Camden Police Department.

Though her work, Milgram seeks to bring the best of the modern world -- data, technology and analytics -- to bear in an effort to transform outdated systems and practices. Milgram is centered on creating a paradigm shift in how we think about innovation and reform in the criminal justice system and beyond.

Milgram graduated summa cum laude from Rutgers University and holds a Master of Philosophy in social and political theory from the University of Cambridge. She received her law degree from New York University School of Law.

More profile about the speaker
Anne Milgram | Speaker | TED.com