Most large police departments have a
system that's called an early
intervention system.  The problem is
that it's neither early nor does it
support interventions that are actually preventive.
Today's systems that most
police departments have are basically called
threshold based.  You know a few
people sit around a table and come up
with three or four different indicators
that they think are probably predictive.
So if in the last hundred eighty days you
have three complaints against you
it raises a flag. When these red flags
are raised it's often too late to do
anything preventive about that and it
raises these flags for a lot of people.
The goal is to take historical data
about these police officers, their
behaviour, other events, citations, arrest
dispatches, and use that data to assign
each officer a risk score and that risk
score is then used to predict which
officers are at risk of one of these
adverse incidents -- misconduct, unjustified
use of force -- and provide the police
department with this information so they can
then work on interventions to prevent
these.
So what we're finding in our
results so far at the Charlotte-Mecklenburg
police department is we can decrease the
number of these false flags
false-positives by about thirty percent
while increasing the number of correct
predictions by about fifteen to twenty percent
Any policy area, any government service,
any government agency, can use data to
improve the services they provide and be
more efficient and be more effective.
One of the things we've been doing
with Chicago Department of Public Health is
helping them predict which kids are
likely to get lead poisoning in the next
few months, and then targeting preventive
inspections to those homes.
I think we can do this for different policy areas, for governing, and that's what
we've been doing for the past few years.
