Their Constitutional rights were violated, as Americans citizen; they did not receive a fair trial, a right to a due process, and a right to a competent attorney First, we will discuss evidence-based corrections, and why it is important.
Which will then lead us into a story about the Oakland Athletics and how it relates to evidence-based. After that we will discuss the creation the justice model for corrections and whether or not it achieved its designed goal. Followed by a discussion about the deterrence theory and the different types of evidence that is used to evaluate sanctions Good Essays words 5.
Bearing in mind that about 2 million people are convicted annually of various crimes, it is possible to see that at any one time there are about 20, people convicted wrongly in the U. While the justice system is effective most of the time in doing so, the cases where it is not effective to affect many lives. The courts work together to produce fair sentences for those criminals.
The question of justice being served comes at the end of every court case. Regardless of the severity of the crime or what the crime was, flaws and improper investigating can slip through the cracks Good Essays words 6. Some of the arguments made are regarding the age at which a juvenile can be tried as an adult, if juveniles should be given a life sentence without the possibility of parole and the neuroscience developments surrounding juveniles and their decision making skills. This article also discusses the strides that the federal and state governments are making to reform the criminal justice system The type of information being used was unknown to each officer, and known to only a few administrators.
On average, ETAS predicted nearly twice as much crime as the human analysts did. As officers increased patrol time on the ETAS hot spots, that correlated with an average 7 per cent decrease in crime. In contrast, patrol time spent on hot spots predicted by the analysts did not correlate with a statistically significant reduction in crime.
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These results carry caveats. If ETAS outdoes human analysts in predicting crime, that might be because it better integrates old and new data. But with only four human analysts of unknown effectiveness included in the study, the comparison is not wholly convincing. More patrol time on ETAS hot spots could indeed be reducing crime; then again, on days when there is little crime for whatever reason, officers could have more time to visit suspect areas.
The 7 per cent correlation suggests that ETAS is doing something right, but it is premature to assert that PredPol unquestionably reduces crime, and certainly not at the double-digit levels that PredPol reports. Nevertheless, PredPol is driven by data and should be judged by data. The results from this study in the field credibly show that the ETAS algorithm has some value, and that predictive policing is worth testing more fully to confirm its effectiveness in reducing crime.
E ven if that effectiveness were to become firmly established, questions remain about how the technology affects policing. After a US Department of Justice report pointed to distrust of police as a factor in the shooting of Brown and the resulting riots in Ferguson, police in St Louis similarly hope that HunchLab will improve community relations. Perhaps the Department of Justice report about the Baltimore police will inspire a similar reaction. Many activists, defenders of civil rights and legal experts see the opposite, that predictive technology stokes community resentment by unfairly targeting innocent people, minorities and the vulnerable, and threatens Fourth Amendment safeguards against unreasonable search and seizure.
Transparency is essential because much of the potential for abuse depends on the integrity of the data used by the predictive software.
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Skewed data would distort predictions and judgments about their value; but since police culture resists revealing its methods, crime data is generally closed to scrutiny. It is hard to determine if the incidence of crime has been underreported, as NYPD and LAPD have recently been caught doing, or if racial factors taint the data. PredPol and HunchLab state that they use no racial or ethnic information, but the data they do use might already embed racial bias that would carry forward in new crime predictions to further entrench the bias.
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Also unknown is how PredPol and HunchLab manipulate the data because their algorithms are proprietary, as are those used by other brands of predictive software. Though upheld by the US Supreme Court in , this practice is highly controversial today. A majority of those stopped but mostly not arrested are black or Latino. In a recent New York Times forum, experts in policing and the law disagreed about whether stop-and-frisk truly reduces crime in New York City and elsewhere, or rather is ineffective and merely worsens the already poor relations between police and minority communities so painfully seen in Dallas, St Louis and elsewhere.
Predictive policing enters into the debate because stop-and-frisk occurs mostly in high-crime areas HCAs in law-enforcement lingo , that is, the hot spots at the centre of predictive analysis. Even without a full stop-and-frisk approach, predictive policing means that more people, whether innocent or guilty, are scrutinised in hot zones.
Extending predictive policing to violent crime, as HunchLab does, greatly raises the stakes for trusting its recommendations. M any of the problems with predictive policing seen so far arise from giving algorithms too much weight compared with human judgment; blindly following a computer program that directs more officers to a certain location at a certain time does nothing to guarantee fair and effective use of that force.
Yet algorithms can augment human abilities rather than replace them. For officers on the street, we might find that combining personal experience with guidance from predictive software enables them to deal better with what they encounter daily. Therefore, besides conducting further testing to determine the true effectiveness of predictive policing, we need to train police officers in how best to blend Big Data recommendations with their own street knowledge. In that regard, the challenges of computer-guided policing are not fundamentally different from those of earlier statistical approaches, with one caveat: the actions of officers in the street responding to computer output in real time are harder to control than thoughtful long-term analysis of statistical trends.
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As Kipperman and Ferguson point out, independent review and broad protections are an essential part of rising to those challenges. He proposes measures such as an outside organisation to audit crime data, correct errors and bias, and acknowledge them when they are found; further studies of the social science and criminology behind predictive policing; accountability at the internal police level and the external community level; and an understanding that a focus on hot spots and statistics diverts attention from examining the root causes of crime.
Sensible and just as these recommendations are, however, it is difficult to see how the necessary new structures would be built and administered. The gathering and use of reliable national crime statistics, for instance, has itself long been an issue in the law enforcement and criminal justice communities.