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

TED
28 Jan 201412:42

Summary

TLDRThe speaker, a former attorney general, recounts their journey to reform the criminal justice system through data-driven approaches. They highlight the inefficiencies of relying on instinct and experience, instead advocating for a 'moneyball' strategy that uses data analytics to assess risk and guide decisions. The development of a universal risk assessment tool, backed by a vast dataset, aims to help judges make more objective decisions about pretrial detention, ultimately aiming to enhance public safety, reduce costs, and improve fairness in the justice system.

Takeaways

  • 📚 The speaker began as the attorney general of New Jersey, with a background in criminal prosecution.
  • 🔍 Upon taking office, the speaker found a lack of data on who was being arrested and charged, and how decisions were made for public safety.
  • 😤 Frustration led to a realization that most criminal justice agencies did not track critical information effectively.
  • 👮‍♂️ A day spent in Camden's police department highlighted the absence of data-driven policing, with crime-fighting relying on rudimentary methods.
  • 🚨 The speaker recognized a systemic failure in the criminal justice system, with a lack of data and analytics in decision-making.
  • 🏈 The concept of 'moneyballing' was introduced, drawing a parallel between data-driven sports management and criminal justice reform.
  • 📉 In New Jersey, data and analytics were successfully applied, leading to a significant reduction in crime rates.
  • 🏛️ The speaker's work at the Arnold Foundation focused on improving the critical decision of whether to detain or release an arrested individual.
  • 📊 A universal risk assessment tool was developed, utilizing a vast dataset to predict recidivism and the likelihood of violence.
  • 🌐 The tool aims to provide judges with objective risk assessments, complementing their instinct and experience for better decision-making.
  • 📈 The speaker's vision is for all U.S. judges to use data-driven risk tools, transforming the criminal justice system into one based on data and analytics.

Q & A

  • What significant change did the speaker experience when they became the attorney general of New Jersey?

    -The speaker experienced a significant change in perspective when they became the attorney general of New Jersey. They realized that the criminal justice system was failing due to a lack of data tracking and analytics, which led them to introduce data-driven decision-making into the system.

  • Why were the initial efforts to gather data on criminal cases in New Jersey frustrating for the speaker?

    -The initial efforts to gather data on criminal cases were frustrating because the speaker found that most big criminal justice agencies, including their own, did not track the necessary information. Detectives had to manually go through case files case by case, which was time-consuming and inefficient.

  • What was the impact of the speaker's changes in the Camden Police Department?

    -The impact of the speaker's changes in the Camden Police Department was significant. They reduced murders by 41 percent, which equates to saving 37 lives, and reduced all crime in the city by 26 percent.

  • What is 'moneyballing' in the context of the criminal justice system as described by the speaker?

    -In the context of the criminal justice system, 'moneyballing' refers to the use of data and analytics to make more informed decisions, similar to how the Oakland A's used smart data and statistics to pick players. The speaker aimed to apply this approach to criminal justice to improve decision-making and outcomes.

  • How did the speaker's approach to criminal justice differ from traditional methods?

    -The speaker's approach differed from traditional methods by focusing on data-driven decision-making, using analytics and statistical analysis to determine the risk posed by individuals in the criminal justice system. This was in contrast to the traditional reliance on instinct and experience.

  • What was the problem the speaker identified with the current bail system in the United States?

    -The speaker identified that the current bail system in the United States often leads to the incarceration of low-risk offenders who cannot afford bail, while high-risk offenders are sometimes released. This is due to subjective decision-making by judges without an objective measure of risk.

  • What is the purpose of the risk assessment tool developed by the speaker's team?

    -The purpose of the risk assessment tool developed by the speaker's team is to provide judges with an objective, scientific measure of risk for individuals in the criminal justice system. It helps predict whether someone will commit a new crime, an act of violence, or fail to appear in court if released.

  • How does the risk assessment tool work, and what information does it use?

    -The risk assessment tool works by analyzing data such as the defendant's prior convictions, history of violence, and failure to appear in court. It uses a universal dataset of 1.5 million cases to predict the likelihood of new crimes, acts of violence, and court appearance failures.

  • What was the outcome of implementing the risk assessment tool in Kentucky?

    -The outcome of implementing the risk assessment tool in Kentucky was not explicitly stated in the script, but the speaker's goal is for every judge in the United States to use a data-driven risk tool within the next five years, indicating a positive view on the potential impact of the tool.

  • What is the speaker's ultimate goal for the criminal justice system in the United States?

    -The speaker's ultimate goal is to transform the American criminal justice system by introducing data-driven decision-making tools, making the streets safer, reducing prison costs, and ensuring the system is fairer and more just.

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Keywords

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Transcripts

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Связанные теги
Criminal JusticeData AnalyticsPublic SafetyRisk AssessmentMoneyballLaw EnforcementSystem ReformNew JerseyJudicial DecisionsData Science
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