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Geoff Barnes: The Probation Department had a one-size-fits-all
supervision strategy in place.
Every offender was getting roughly the same amount, coming in
approximately once a month, seeing their officer for 20 to 30
minutes, maybe.
It just wasn’t a whole lot of actual contact with the offenders.
And, clearly, their budget wasn’t going to go up.
They weren’t going to get any more officers.
They really wanted to utilize the resources they had available
at the time, which weren’t going to increase, but focus those
resources on the people that it made the most sense to focus
upon.
So identifying those that present the biggest risk to community
safety and focusing on them, and at the same time looking at
people who really present no risk at all, but had been sentenced
to community supervision.
Something had to be done with them, but maybe let’s not expend
so many resources at the lower end of the spectrum, and instead
take what would have been wasted on those people and put it on
the people that present the highest risk.
Jordan Hyatt: So what this prediction model does is it
predicts, using information that the probation department
largely had available already, the likely conduct of any
probationer for the first two years of their term of
probation supervision.
So there are three outcomes for this particular model.
The lowest level of risk suggests, or it says, that the offender
won’t commit any new offenses during that two-year forecasting
period.
The moderate level of supervision says that the offender will
commit a crime, but not a serious crime.
And the highest risk of supervision includes those offenders who
are forecasted to commit a serious offense, which is generally
defined as ***, attempted ***, aggravated assault, ***
and arson.
And so what the Probation Department has done is, based on
these forecasting outcomes, they supervise offenders in units
based on those risk classifications, so the highest risk
offenders, those most likely to pose a danger to the community,
are supervised most intensely, while the offenders who are
predicted to commit no new offenses or relatively minor offenses
get a decreased level of supervision.
Hyatt: And I think that gets an important point, though:
the difference between the researcher side
of what we’re doing and the practitioner side.
All that the model is doing is saying that based on the
information available about this individual, this is what
they’re likely to do over the next two years.
It’s up to the agency, in our case the Probation Department, to
decide what to do with that information.
So here they decided to supervise dangerous people more
intensely, but that didn’t have to be the case.
That was their decision to make and that’s the policy half of
this equation, of this partnership.
Barnes: I mean probably the most crucial thing, at least in
this project, has to have been that we had researchers and
practitioners, but it wasn’t that there was one group on one
side and one group on the other.
It all had to come together, and it all had to be a partnership,
and it had to happen in concert at every single step.
We couldn’t have built a model without knowing from them, from
our partners at Probation, exactly how many people could they
possibly deal with being labeled as high-risk without knowing
their capacity to supervise people, and exactly what they wanted
to do, and how many officers they could devote to that, without
knowing that it couldn’t go much above 15 or 18 percent,
we could never have built the model in the first place.
Without their data, we could never have built the model in the
first place.
Barnes: One thing that seems to be a very big improvement in
random forest modeling, as compared to stuff we maybe did in the
past, maybe stuff that other jurisdictions tried in the past and
weren’t very pleased with the results, one of the things is that
the amount of information that we can use, you don’t necessarily
need to go into the modeling process knowing, well, we think
this is important and so we absolutely must go get this.
Lots of different things, lots of different values can be used
to predict future behavior, even things,
which probably this sounds very strange,
but even things that don’t predict future
behavior very well, can be included in the model.
In traditional statistical procedures, what you typically would
have to say is, “Well, we can only have a limited number of
predictors to forecast future behavior.”
In random forest modeling,
you don’t have to be so choosy.
You can afford to put things in that maybe don’t work well for
older offenders, but work very well for younger offenders.
But I think the important thing is that each individual
jurisdiction has access to things probably that they haven’t
even thought about.
They put this information in as a matter of course, as part of
their day-to-day routine, and never really realizing how
enormously powerful it could be with just a few edits, with just
a few manipulations of it to convert it into a set of numbers
that could forecast future behavior.
Well, all this forecasting technology can look overwhelming in
a lot of ways.
I think if you look at our report, you see something like,
ah, well, the model makes nine million different decisions.
Hyatt: Yes, 8.4 something million decisions.
Barnes: Yeah, you know, you look at it and you say, “How
could we ever get to that? It looks so complicated.”
But I really think that the reality is different.
I think that with the exception of maybe the very smallest
jurisdictions, the data are available.
It’s just a question of making use of stuff that you already
have to build a customized model that fits your particular
jurisdiction and your particular needs at the particular phase
of the criminal justice system that you are interested in.
Barnes: We have to be very careful about how we allocate the
precious resource we have, and the most precious resource is
time.
Every public employee, whether they’re a probation officer or a
police officer or a corrections officer, they all only have a
certain amount of time that they can devote, and attention
that they can devote, to their jobs.
We have to make sure we allocate those resources in ways that
make sense.
But I think the other reason why we really have to focus on
prediction is that, chances are, it’s happening anyway.
Hyatt: So probation officers or anybody in criminal justice,
people are making judgments about the relative risk that an
offender or a probationer poses already.
What risk assessment, and specifically actuarial risk
assessment, lets us do is ensure that we’re making those
predictions in the most fair and equitable way possible.
So by using a prediction model like the one in Philadelphia
based on random forest modeling, we can be sure that we are
identifying the most dangerous offenders in the most accurate
way possible, and that we’re doing that in a consistent and fair
way.
And it ensures that we’re both preserving resources, but also
the people who are subject to the policy decisions based on
those risk assessments are being treated in a fair and
consistent way.
Barnes: So one of the reasons to really want to bring this
forward in the criminal justice system is that in a lot of ways,
it makes the system fairer.
It not only is more accurate, but at least you know that however
you got put, or if you’re coming into probation and you have to
come in once a week because you got put on high-risk probation,
at least you know that the decision that put you into that
situation was made the exact same way for you as it will be for
the guy that comes after you and the guy that came before you,
and everyone who comes into probation gets assessed on the basis
of the same criteria.
You may not like being on high-risk probation, but from a
procedural justice standpoint, you at least know that the
decision was made the same way for everybody.