The role of data in criminal justice

Summary

The discussion centers around the growing role of data-driven systems in the criminal justice system and their limitations. While data is often seen as objective and a solution to human bias, the speakers argue that it frequently reflects existing racial biases and systemic inequalities. They raise concerns about the use of predictive tools and risk assessments, which may reinforce past injustices rather than address the underlying issues. The conversation emphasizes the need for a paradigm shift, focusing on social justice interventions instead of relying solely on technology to predict or prevent crime.

Takeaways

• 🧑‍⚖️ Data-driven systems in criminal justice are viewed with skepticism due to concerns that they replicate systemic biases, particularly racism and inequality inherent in historical data.
• 📊 Data used in criminal justice often reflects enforcement patterns rather than actual crime rates, leading to distorted outcomes when predicting crime or behavior.
• 🤔 Many believe that data is objective, but it often fails to capture unreported crimes or the nuanced reasons behind certain behaviors, leading to unreliable predictions.
• 🔍 There's a need for a paradigm shift in criminal justice—away from predictive models that attempt to foresee human behavior and towards addressing root causes of crime through social interventions.
• 🚫 Critics argue that current data-driven tools focus too much on individuals caught in the system rather than on those perpetuating it, such as police officers, judges, and prosecutors.
• 🪞 Algorithmic tools are often seen as mirrors reflecting existing biases rather than correcting them, leading to recommendations that preserve the status quo rather than drive meaningful change.
• ⚖️ The rapid development of technology often outpaces policy reform, making it difficult for legal systems to adapt to new tools and ensure that they are used in ways that are fair and effective.
• 📉 There’s growing advocacy for shifting focus from prediction to intervention, using data to test what measures actually help reduce crime and recidivism rather than just forecasting risk.
• 💡 Social justice approaches, such as investing in community resources and decriminalization, are seen as more effective than criminal justice responses in addressing the root causes of crime.
• 🔄 Concepts like pretrial risk and danger are often ill-defined, subjective, and legally recent, complicating the fair use of data in criminal justice decision-making.

Q & A

What is the main concern about data-driven systems in the criminal justice system?

The main concern is that data used in criminal justice may be tainted by the country’s history of racism and inequality, potentially leading to biased outcomes and perpetuating systemic injustices.

Why do people trust data-driven systems despite these concerns?

People tend to trust data-driven systems because they are perceived as more objective and less prone to human error, which can improve decision-making in some aspects of criminal justice.

What is a key misconception about data in the criminal justice system?

A key misconception is that the data is purely objective. In reality, it often reflects enforcement patterns, not actual crime rates, and can be influenced by biases in policing.

How can domestic violence data be unreliable for criminal justice systems?

Domestic violence is often underreported for various reasons, including fear of police or social stigma, leading to unreliable data that doesn't accurately represent the true extent of the issue.

What is the 'garbage in, garbage out' problem mentioned in the context of predictive tools?

This refers to the idea that if biased or flawed data is used in a predictive model, the results will also be flawed and perpetuate existing inequalities.

Why is it difficult to ‘scrub’ data of racial bias?

Racial bias and inequality are deeply woven into many aspects of American society, such as housing, education, and law enforcement, making it challenging, if not impossible, to completely remove racial influence from the data.

What alternative uses of data tools were suggested by the speakers?

The speakers suggest using data tools to assess and modify the behavior of actors within the justice system, such as police officers, judges, and prosecutors, rather than predicting the behavior of individuals.

Why do the speakers doubt the ability of predictive tools to improve the criminal justice system?

They believe that predictive tools often fail to understand the complexity of human behavior and risk, and may over-rely on flawed data that reinforces existing biases in the system.

What is the role of prediction in criminal justice, and why is it problematic?

Prediction is used to foresee future actions, like the likelihood of reoffending, but it is problematic because it is based on biased historical data that may not accurately reflect individual or community realities.

What is a more forward-looking approach to criminal justice reform mentioned in the discussion?

A forward-looking approach involves using causal inference methods to evaluate the effectiveness of interventions, rather than relying on predictive tools that reflect past enforcement patterns. This would focus on identifying what policies and changes actually work to improve outcomes.

Outlines

00:00

💡 The Role of Data in Criminal Justice: Risks and Realities

This paragraph discusses the evolution of data usage in the criminal justice system, highlighting how data-driven systems are trusted for their supposed objectivity. However, concerns arise over the historical biases embedded in data, such as racism, which could lead to flawed outcomes. The speakers express skepticism about the effectiveness of data-driven decision-making, emphasizing that biased data only perpetuates systemic inequalities. The conversation points out that while awareness is increasing, the fundamental issue lies in the data's flawed origins.

05:02

🔍 Misconceptions About Data Objectivity

The misconception that data is inherently objective is addressed. The speakers argue that criminal justice data reflects enforcement patterns rather than actual crime rates. For instance, underreporting of crimes like domestic violence skews the data, making predictive models unreliable. The paragraph explores how different groups face different arrest patterns, leading to biased outcomes. It also questions whether these biases can be overcome, suggesting that certain crimes might show less disparity but that systemic issues remain prevalent.

10:03

🤖 The Complexity of Human Behavior and Prediction Tools

Here, the focus shifts to the challenges of predicting human behavior using machines. Speakers share personal experiences with unpredictable court appearances and emphasize the limits of data in capturing the nuances of human behavior. They criticize the over-reliance on tools that attempt to predict outcomes and argue that human unpredictability makes such predictions flawed. The paragraph advocates for a paradigm shift, suggesting that trying to fix a broken system with these tools misses the point.

15:06

⚖️ Objectivity in Criminal Justice: A False Promise?

This section delves deeper into the perceived objectivity of criminal justice tools, suggesting that they often reinforce existing biases rather than offering real solutions. The speakers argue that relying on tools to make decisions without understanding the underlying human factors leads to shallow assessments. They criticize pre-trial risk tools for overlooking important variables, like personal hardships, that affect court appearances. They also touch on how slow policy reforms struggle to keep pace with rapid technological advancements.

🔄 Historical Data and the Challenges of Policy Reform

Technological advancements in criminal justice are often based on historical data that doesn't reflect new policies or positive changes. This paragraph examines how counties and cities lack the resources to properly implement data science, leading to outdated or inaccurate predictions. For example, even though text reminders can reduce court no-shows, predictive tools still use old data that doesn't account for these improvements. The speakers stress the need for forward-thinking solutions that break away from reliance on past data.

🔬 Moving Toward Interventions Instead of Predictions

The conversation here shifts to the idea of 'interventions over predictions,' where the focus is on determining which interventions work best rather than using predictive models to forecast criminal behavior. The speakers propose using new statistical techniques to create data on the effectiveness of interventions, such as whether a program helps someone avoid future interaction with the justice system. They also advocate for investing in social services like education and job creation in neighborhoods rather than increasing police presence.

📈 Reassessing Risk: A Call for Systemic Change

The speakers explore the broader issue of how concepts like 'risk' and 'danger' are applied in the justice system. They argue that these terms are often vague and subjective, complicating the use of data-driven tools. The discussion questions the validity of risk assessments and whether they can effectively predict future behavior. The speakers suggest that risk is a fundamental part of life and that trying to eliminate it entirely from the criminal justice system is impractical.

📚 Using Science for Sentencing and Decriminalization

This paragraph highlights how science and data can be useful for reforms, such as decriminalization, sentence reduction, and early release. The speakers advocate for using evidence-based practices that focus on reducing mass incarceration and improving rehabilitation efforts. They point out that while positive data exists, it is often ignored in favor of punitive measures. The focus should be on systemic change that prioritizes alternatives to incarceration, especially for minor offenses and non-violent crimes.

🧑‍⚖️ Addiction and Criminalization: Rethinking Rehabilitation

The final paragraph discusses how addiction is criminalized instead of being treated as a health issue. The speakers reflect on the idea that repeated attempts at rehabilitation, like trying to quit smoking, should be normalized rather than punished. They suggest that risk assessments could be recalibrated to measure the negative impact of pretrial detention on individuals. The goal is to create a system that incorporates restorative practices, addressing root causes like addiction rather than funneling people into the criminal justice system.

Keywords

💡Data-driven systems

Data-driven systems refer to the use of data to inform decisions and actions, often through algorithms or statistical models. In the context of the criminal justice system, these systems are seen as a way to reduce human error by relying on data for decision-making, such as pre-trial risk assessments or predictive policing. However, the video highlights skepticism around these tools, emphasizing that the data being used may be biased, reflecting historical racial and socioeconomic inequalities.

💡Bias in data

Bias in data refers to the distortion or unfairness present in the information collected and used in data-driven systems. In the context of the video, the concern is that the data relied upon in the criminal justice system is 'infected' with the country's history of racism and inequality. The result is that biased data can lead to biased outcomes, perpetuating systemic racism and inequality rather than solving it.

💡Risk assessment tools

Risk assessment tools are predictive systems used in the criminal justice system to estimate the likelihood of an individual committing a future crime or returning to court. These tools rely on past data, such as prior arrests, but the video argues that they can lead to flawed decisions because the data often reflects enforcement patterns rather than actual crime, and may not account for important social factors like poverty or fear of the police.

💡Predictive policing

Predictive policing is a technique that uses data to forecast where crimes might occur or who might commit them. In the video, speakers criticize predictive policing as being a flawed approach, arguing that it relies on biased data and reinforces existing inequalities. They propose that instead of sending more police to high-risk areas, resources should be used to address underlying social issues like education or employment.

💡Racial bias

Racial bias refers to the unfair treatment of individuals based on race, which is deeply woven into the history of many systems, including criminal justice. The video discusses how data-driven tools often reflect and reinforce racial biases, as historical data on arrests and policing tends to be skewed against marginalized communities. This bias can lead to disproportionate negative outcomes for these groups, such as harsher sentencing or higher likelihood of incarceration.

💡Pre-trial detention

Pre-trial detention is when an individual is held in custody before their trial because they are considered a flight risk or a danger to society. The video critiques the use of data-driven risk assessment tools to determine whether someone should be detained, arguing that these tools can be inaccurate and fail to account for social circumstances, such as an individual's inability to return to court due to lack of transportation or other life challenges.

💡Objective data

Objective data is often perceived as unbiased, factual information. However, the video challenges this notion, especially in the context of criminal justice, by explaining that data about crime and arrests is often not neutral. Instead, it reflects enforcement patterns and historical inequalities. For example, data about arrests might not represent actual crime rates but rather the focus of police efforts, which can vary across different communities and be influenced by racial bias.

💡Causal inference methods

Causal inference methods are statistical techniques used to identify the effect of interventions or actions, rather than merely predicting outcomes based on past data. The video suggests that instead of using risk assessment tools, which rely on flawed historical data, criminal justice systems should explore causal inference methods to understand which interventions actually help reduce crime or support individuals, creating new data that can guide policy reforms.

💡Social justice response

A social justice response emphasizes addressing the root causes of crime and inequality, such as poverty, lack of education, or unemployment, rather than solely focusing on punitive measures. In the video, speakers argue that instead of using predictive policing to arrest more people, efforts should be directed toward providing resources that uplift communities and reduce the conditions that lead to criminal behavior in the first place.

💡Addiction and criminalization

Addiction and criminalization refer to the way that behaviors related to substance use are often treated as crimes, rather than health issues. In the video, one speaker advocates for treating addiction through rehabilitation rather than incarceration, noting that addiction is a recurring issue that often requires multiple attempts to overcome. The speaker critiques the justice system for criminalizing addiction instead of offering consistent support and resources for recovery.

Highlights

The role of data in the criminal justice system has evolved, with a growing skepticism towards big data as it is applied to justice.

Concerns are raised about data-driven decision-making replicating historical biases, particularly racism and inequality embedded in the data.

Data in criminal justice is often misunderstood as objective, but it is shaped by enforcement patterns rather than representing actual crime.

Predictive policing tools, like pre-trial risk assessments, rely on data that can be statistically unreliable, such as underreported crimes like domestic violence.

Algorithms used in the criminal justice system are criticized for simply mirroring existing biases rather than providing objective predictions.

There is a call to shift focus from predicting criminal behavior to addressing the behaviors of police officers, judges, and attorneys to reduce biases.

Risk assessment tools often ignore important contextual factors, such as economic hardship or past experiences, that affect court appearances.

There is a disconnect between the rapid evolution of technology and the slow pace of policy reform, making it challenging to ensure fair application.

Interventions over predictions: Instead of predicting risks, some suggest focusing on interventions that address underlying issues, such as lack of resources or support.

Social justice responses, like providing more jobs or schools, are proposed as alternatives to using predictive tools to address crime in neighborhoods.

Terms like 'risk' and 'danger' are often ill-defined in the criminal justice system, complicating the use of data in risk assessment tools.

There is skepticism about whether any risk assessment tools, as currently designed, can effectively improve safety or justice outcomes.

Data showing positive outcomes from decriminalization or diversion from the criminal justice system is often underutilized in shaping policy.

Reframing risk assessments to consider the impact of detention on individuals’ lives, rather than just predicting future crimes, is suggested.

A paradigm shift is needed in criminal justice, moving away from efficiency-focused reforms to more fundamental changes that prioritize social equity.

Transcripts

thank you both for being here thank you

thank you as we've heard a bunch today

the role of data in the criminal justice

system has evolved a lot in the last

decade people tend to trust data-driven

systems they remove supposedly some of

the human errors that have you know laid

waste to the best parts of the criminal

justice system how are you seeing folks

balance the desire to combat you know

this injustice in the system with the

growing skepticism of big data as it's

applied to criminal justice so I mean I

think you know fundamentally one of the

problems with this kind of drive towards

data-driven and kind of algorithmic

decision-making is you know the concern

that the data that people are relying on

is infected with this country's history

of racism and inequality and you know

essentially what you're gonna do what

you're doing is replicating that

terrible history by using data that is

essentially garbage and kind of putting

it through a machine and getting garbage

out and getting garbage predictions and

so I think that's the kind of

fundamental problem I think people

becoming more and more aware of that

problem as there's been more of an

effort to rely on these types of tools

to try and help and improve criminalists

decision-making I think that that part

of it kind of the acknowledgment

understanding is a really good thing and

a positive development but I I remain

kind of incredibly skeptical about the

usefulness of using these approaches

Logan what are some of the the

misconceptions as you're looking at you

know data being heralded as one of these

really positive things for the criminal

justice system what are some

misconceptions about the way that it's

actually applied so I think one helpful

thing is I think frequently we think

that data is objective and it represents

the sort of like natural existence of

something occurring in the world but of

course when we're talking about the

criminal justice system the criminal

justice system you know criminologists

have long studied this and said that

really this underlying data is

representative of enforcement patterns

it's not necessarily representative of

quote-unquote underlying crime data in

the actual commission of crime and we

have to remember that this is

representative of how police officers

are responding to things in their

community and I think that's a

fundamental disconnect

like frequently I'm talking with

different individuals who are saying hey

if I want to adopt something like a

pre-trial risk assessment tool or

predictive policing system this is just

sort of objective data that's a rest

level data but of course we know that

different things are arrested at

different patterns for different groups

for example we know that domestic

violence is often not reported and so if

you're going to build a model that was

going to forecast where domestic

violence crimes might be committed or

forecast you'd have statistically

unreliable data simply because that's

not reported and you can go into various

different reasons why that might not be

reported for you know fear of the police

fear of reporting many different things

but I think there's just just this myth

that this this crime day is inherently

objective in some way can can that be

overcome i I think it can be a big

question I don't know if it can

necessarily be overcome because in some

sense all police departments will

inherently be constrained right there

going to be some number of units of

officers who can only respond to some

number of things right I think one thing

to think about is potentially there are

maybe some crimes that don't show sort

of underlying disparities and arrests

level data that communities may be truly

are shown to truly care about where

they're struggling with maybe saying

domestic violence or aggravated assault

things like that but I don't think I see

many strategies that are improving that

today yeah you know I'm not sure if we

can be able to come I don't think that

it can actually I think we can confront

it I think we may be able to account for

I'm not a kind of a data scientist or or

a technologist in that way but I think

there may be ways to try and try and

account for some measure of where have

the racism or the bias it's kind of

influencing the data but I think

fundamentally there's so much inequality

and so much racial bias woven into the

DNA of this country that kind of

whatever system you tie it back to

they're gonna have some racial impact

right and so and of what is whether

you're looking at housing education

employment age at first arrest where

police are deployed all those things are

tied in to race and so I think in order

to kind of scrub the data so to speak of

the racism I think is amazing

that'll be difficult if not impossible

task at least my view so how do we

account for it well you know my thing is

we should be just stop using these tools

you know we kind of go in a different

direction and and fundamentally right

now all the tools are being pointed at

the people who are being consumed by

these systems I would like to see tools

that are aimed at police officers judges

prosecutors defense attorneys

determinedly you know you know if we if

we have a concern about trying to change

the system itself and the behavior of

actors in the system why don't we trying

to change the behaviors of those who are

kind of replicating some of the biases

we see already why why are we trying to

trying to predict what people might do

the reality is as a public defender I

you know I there are some people who I

knew would come back to court some

people I thought would definitely come

back to court without without any

problem some people I thought would

never show up again and you know I was

surprised regularly about what would

happen you know the human condition is

really difficult to predict I think the

idea that we voted it better with

machines fundamentally misunderstands

how people work I see you nodding your

head yeah on that point I think I think

something that's been interesting is

sort of when we think about sort of big

data or algorithms a lot of this is

focused on prediction generally and of

course there's sort of prediction is you

know ubiquitous throughout the criminal

justice system and you know individuals

will be predicting things on their own

but I think hopefully many people did

not need this reminder but as new tools

have been developed you know you have

sort of wrote more actuarial base

regression statistics tools and then you

have more sort of machine learning based

tools I think as you sort of try and

refine this along the path what you're

really doing there's a great new paper

by sandy Mason

that'll be published in the Yale Law

Journal that basically says what we're

doing is really just sort of putting up

a mirror that is just more and more

accurate and the solution isn't so been

the mirror to your liking so it's not to

account for like okay how can we adjust

the model like what matters is what

you're seeing in the mirror right and

that is the underlying thing that you

should really be trying to adjust for

yeah I mean I think that can I guess is

a fundamental point that's what we need

to do is have kind of a paradigm shift

about the way we do criminal justice I'm

not kind of like taking around edge

and making a bad system work a little

bit more efficiently I think that's what

these tools often purport to do yeah it

sounds like you know both of you have

brought up that basically these

technologies are just kind of preserving

the same system under the guise of

scientific objectivity talk about how

that perceived objectivity is kind of

stifles progress well I mean I think in

many ways there's this sense that if

you're if you're kind of relying on a

tool and tool tells you that this

individual mate may or may not do this

this particular thing

you're never gonna kind of do any kind

of deeper inquiry into what the kind of

human condition is about and so you're

never gonna find out like so for example

I think we think about a risk assessment

tool that's designed to figure out

someone's gonna kind of appear in court

or not the type of data they were

usually looking at are kind of a two

first arrest prior warrant history the

things that made that they may think

correlate with fears to appear but there

are other data points that that are

never kind of examined you may not

return to court because you just don't

have the resources to get back to court

you may not return to court cuz they

have a terrible experience with court

before and you're worried about what's

gonna happen you may not return to court

because you're you have family

circumstances or other things that may

have may have interfere with your

ability get back to court a data point

that's not kind of considered at all is

never gonna be measured and never gonna

be part of your range of considerations

so I think that's that's problematic in

and of itself Logan we were talking a

little bit backstage about kind of the

slow moving policy reform how quickly

and rapidly technology changes and how

that you know the dissonance between

those timelines makes it really

difficult for policy to catch up talk a

little bit about that yeah so I think

one sort of interesting paradox is sort

of if you look at sort of the current

movement of bail reform as its

conceptualized with certain policymakers

I think there's a sort of unanimous

understanding that money bail system

needs to go away and a lot of

policymakers are looking towards risk

assessment tools to sort of reform away

an unjust system and along the way

they're adopting good policy so money

bail that means you know if you are a

person who can't pay $500 to get out of

jail there are plenty of studies of the

show that your risk of RIA rest will be

increased because

and the 72 hours you've been detained

your life might have fallen apart you

could lose your job we discussed you of

your children things like that there are

other important policies like text

reminders can reduce failure to appear a

lot but the problem is when we implement

these new technologies they're

inherently looking to the Past so if

you're looking to something that doesn't

incorporate the actual new beneficial

policies you're just going to be

forecasting the likelihood of someone's

risk based on to the previous system so

it's really hard to generate these

positive feedback loops because

frequently these counties and cities

don't have the budget to do

quote-unquote data science properly and

it's it's a it's a really hard task to

get right and I think that's an

underappreciated element in the current

debate so how do we you know cast the

our lenses forward what are the

challenges in trying to do something

that's forward-looking instead of

something that uses kind of historical

data that doesn't work so one thing

that's interesting is there's a great

paper that sort of sort of trying to

reframe this entire debate it's called

interventions over predictions and

really what its postulating is instead

of looking at the sort of risk

assessment stuff that's trying to look

at historical data to project things for

it it's saying let's look at something

called causal inference methods so

that's basically trying to do new

statistical techniques where you're

trying to say what invent interventions

really actually work so by doing that

you're creating new data that can

actually say okay this actual new change

help someone get back to court this new

change help this class of defendants

never get touched by the criminal

justice system again and I think that's

one thing that's sort of more

forward-looking and to me I mean to me

that sounds a little bit kind of like a

like more of a needs assessment

intervention than anything else and so

like you know we think about pretty good

policing and the notion that is gonna

predict where where a crime might take

place or who might be involved in crime

rather than send if you think a

neighborhood is gonna be the site of

some criminal activity why are we

sending police officers into that

neighborhood why don't we why don't we

figure out like okay that neighborhood

needs more schools and there needs more

jobs than neighborhood to do social

no to me I'm oh I'm always struck by the

fact that our response to some of these

inequities as a criminal justice

response rather than kind of a social

justice response rather than a response

that actually lifts up people and helps

them avoid capacity now and so I you

know

Hardwell what I can see is that type of

methodology instilled as well absolutely

before I take it to the audience for a

question I wanted to ask one bigger

broader question we talked about

pretrial risk tools and predictive

police say and we're often using these

big terms like risk and danger and

they're often ill-defined and

subjectively applied how does that

complicate the use of data obviously it

sounds like you both think that that

neither of those tools are a step in the

right direction but how does one even

begin to calculate a tolerable amount of

risk in a community or a tolerable

amount of danger I would just note that

when we think about bail it wasn't legal

necessarily for judges to predict future

dangerousness until about 1987 when the

Supreme Court codified it so this is a

new sort of legal regime that we've

established so there's nothing

necessarily I mean we can have a

spirited debate about the underlying

sort of Court's decisions leading to the

predictions of future dangerousness but

I just I I just sort of question how I I

mean from one perspective we have sort

of REO rests data standing in for the

proposition of sort of public safety but

so many things that people are really

say nothing about violence to a

community or Public Safety at all and I

I just colored me skeptical that any

risk assessment tools as sort of

currently proposed and implemented will

sort of succeed in in their hands and

and you know I'll just say very quickly

I think fundamentally risk is part of

life you know I think you can't we could

walk outside get hit by bus you know but

you don't stop crossing the street

because of that like that's that's part

like risk is what the promotion system

has to accept as part of the cost of

doing business and so I think notion

will be able to predict who's riskier

who's not to me just doesn't it just

doesn't kind of really make any sense

do we have a question in the audience I

think we have one down here in the front

hi it's me again

Vivian mixer so I want to ask what roles

do you think using science that actually

would help like science about sentencing

reform and decriminalization to do to to

eliminate some of the problems we're

facing so I mean I think there there is

like - except you're looking at data and

think about science I think so you're

holding up kind of good results from

from things like the criminalization

from things like the incarcerating

places from things like releasing people

early competin Singh from sentences from

diverting people from the criminal

justice and I think that data for some

reason is never kind of the stuff that

people cite or think about we're

thinking about kind of evidence-based

practices right and so I think those are

all data points that would be very

helpful in informing kind of

decision-making about the way in which

we should kind of shape our criminal

justice policy more generally I'm all

for using data in that way I think to me

it's just problematic when you're

deciding how long a sentence somebody

for or if we gonna release them on

parole even in the bill context I mean I

think there's a place to say here's an

entire class of people and by the way

the vast majority people who are

released from from from pretrial

incarceration return to court so it's

not as though we have people kind of

just skipping court willy-nilly all the

time but here's an entire population of

folks or a cohort folks who can be

released without ever even going through

the court system at all and so I think

there are probably ways you can use it

in a positive way but unfortunately

those don't seem to be the wins that are

kind of advanced that often

hi annoying umm so really quickly Thank

You number one I just read this book by

James Forman jr. called locking up our

own which was amazing if you guys

haven't read it but he really just talks

about how these innate community

barriers that should be more of a human

services issue have been criminalized so

when you deal with things like addiction

we've turned addiction into a

criminalization so when you have someone

who commits crimes as a result of their

addiction we're we want to lock them up

and instead of sending them back to

rehab and his whole statement was why

don't we view why don't we view rehab

the same way that we view put the prison

system like the first time you try to

quit smoking you're probably not going

to get it so why don't we send them back

there as opposed to putting them into

prison because they didn't kick the

habit the first time so my question is

in what way if possible do you think

that we can start to it could possibly

incorporate these community conditions

and these restorative types of practices

so that everyone who suffers from

everyone who's not just a hardened

criminal isn't caught up in that

whirlpool if you live I think one

interesting idea to kind of combine both

questions is I think you could rican

septa lies risk assessments to include

another factor so for example the risk

of any pretrial detention greater than

24 hours to that person what impact

would that have on their life wasn't it

you know we know of all these sort of

negative outcomes actually sort of

formalizing that for the process could

really I think recalibrate and get us

closer at motive of course when we're

thinking of addiction you know your

first time trying to struggle through

that it's not necessarily going to sort

of latch your first time you have to try

and try and try I think sort of maybe

potentially having this sort of if we

were to put this person inside the

system what how would that impact them

in negative way I think that that might

be one potential solution Vincent Logan

thank you so much thank you

[Applause]