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Capstone high school fee structure order write for me what does it mean to be one of the seven free online phd thesis raishin 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 or 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 protect no risk at all but had been sentenced a 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 so what this prediction model does is it predicts using information that the probation department largely had available already the likely conduct of any probation or 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 it includes those offenders who are forecasted to commit a serious offense which is generally defined as murder attempted murder aggravated assault rape 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 offences or relatively minor offenses get a decreased level of supervision and I think that gets an important point difference between the researcher side of what we're doing 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 there doesn't have to be the case that was their decision to making that that's the policy half of this of this equation of this partnership yeah 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 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 you know we couldn't have built the model without knowing from them or from our from our partners at probation exactly how many people could they possibly deal with being labeled as high-risk without knowing their capacity to supervises 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 18 percent we could never have built the model with in the first place without their data we could never have built the model in the first place 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 we're very pleased with the results one of the things is that the amount of information that we can use don't necessarily need to go into the modeling process knowing well we think this is important and so we must absolutely must go get this lots of different things got 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 including them 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 if the random forest modeling you can 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 just put this they put this information in as a matter of course as part of their day to day routine and never really realizing how enormous lis powerful it could be with just a few edits just just a few manipulations of it to convert it into a set of numbers that could forecast future behavior well look all this all this forecasting technology can look overwhelming in a lot of ways I think if you look at our report you see something like oh well the model makes nine million different decision at eight point four or something knowing decisions do you know you look at you say oh you know how could we ever get to that it looks so complicated but I really think that the reality is 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 particulars diction and your particular needs at the particular phase of the criminal justice system that you are interested in 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 their 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 so probation officers are anybody in criminal justice people are making judgments about the relative risk that an offender or 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 model modeling we can be sure that we are identifying the most dangerous offenders in the most accurate way possible and then 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 and 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 a system fairer it if not only is more a more accurate but at least you know that however you got put or how are you 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 to 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 the decision was made the same way for everybody you professional capstone project New York School of Urban Ministry.