Predictive Policing --- by the time the IDF finishes programming US police profiling data everyone other than zionists will be suspect. know the tactics of the enemy.
On Tuesday, April 26, 2016 at 6:49:23 AM UTC-5, Travis wrote: > > > > > > > > > > > > > http://www.wsj.com/articles/is-predictive-policing-the-law-enforcement-tactic-of-the-future-1461550190 > > Is Predictive Policing the Law-Enforcement Tactic of the Future?A Johns > Hopkins professor says it helps reduce crime and police profiling. An > attorney at Electronic Frontier Foundation says it will lead to more > bias.[image: > Description: Predictive policing’s supporters say it will make police > departments more effective and improve community relations. Critics say it > will result in more bias.]Predictive policing’s supporters say it will > make police departments more effective and improve community relations. > Critics say it will result in more bias. Photo: iStockphoto/Getty Images > April > 24, 2016 10:09 p.m. ET As big data transforms industries ranging from > retailing to health care, it’s also becoming a more important tool for > police departments, which are turning to data and analysis in an effort to > boost their effectiveness.Known as predictive policing, the practice > involves analyzing data on the time, location and nature of past crimes, > along with things such as geography and the weather, to gain insight into > where and when future crime is most likely to occur and try to deter it > before it happens.Jennifer Bachner, director of the master of science in > government analytics program at Johns Hopkins University, says giving > police the ability to make data-driven decisions will help reduce biases > that result in unfair discrimination, resulting in better relations between > police and the communities they serve. Jennifer Lynch, senior staff > attorney at the Electronic Frontier Foundation, says predictive policing is > flawed and will only serve to focus more law-enforcement surveillance on > communities that are already overpoliced.YES: Police Can Be in the Right > Place at the Right TimeBy Jennifer Bachner In an era of tight budgets, > police departments across the country are being asked to do more with less. > They must protect the public, but often have to do it with limited > personnel, equipment and training resources.To address this problem, > law-enforcement agencies increasingly are turning to data and analytics to > improve their ability to fight crime without substantial increases in > operating costs. Known as predictive policing, these technologies and > techniques empower police officers to take a more proactive approach to > both preventing crime and solving open cases.Predictive policing involves > crunching data on past crimes, along with information such as the weather, > the time of day and the presence of escape routes, to forecast where and > when future crime is most likely to occur. In cities such as Santa Cruz, > Calif., officers have access to maps > <http://www.businessofgovernment.org/sites/default/files/Management%20Predictive%20Policing.pdf>outlining > > “hot spots,” > <http://www.businessofgovernment.org/sites/default/files/Management%20Predictive%20Policing.pdf> > > or geographic areas most vulnerable to crime at a future point in time, and > they are encouraged to use the information along with their knowledge of > the community to decide where to allocate the most resources on a given > shift.The theory isn’t complicated—being in the right place at the right > time deters crime—and the approach has proved effective > <http://amstat.tandfonline.com/doi/abs/10.1080/01621459.2015.1077710?journalCode=uasa20>, > > particularly in places such as Santa Cruz, where the population is > dispersed over a large area.Some in law enforcement say predictive > policing is particularly helpful when it comes to identifying and halting > repeat criminals. The Baltimore County Police Department > <http://www.businessofgovernment.org/sites/default/files/Predictive%20Policing.pdf> > > says it used predictive methods to halt a string of convenience-store > robberies. Police had information about the locations of the robberies and > a suspected model of car used by the elusive offender, but no obvious next > target. By plotting the robbed locations on a map and employing an > iterative algorithm, police identified a suspected point of origin. Police > then analyzed the streets that would likely have been used to reach the > crime locations and detected one specific street that the offender had > likely used frequently (and would probably use again) to travel to crime > scenes. Officers staked out that street, rather than patrolling numerous > convenience stores, and were able to apprehend the suspect.Some critics > say that because not all crime is reported, predictive models based on past > crime data might miss future crimes that don’t fit historical patterns. But > today’s predictive models aren’t based solely on past crime data—they also > take into account some of the same things potential criminals do when > planning crimes, such as geographic information. [image: Description: > https://si.wsj.net/public/resources/images/FT-AA145_PREDIC_9U_20160420115714.jpg]To > > achieve positive results with predictive policing, some upfront costs are > required: Law-enforcement agencies must make an initial investment not only > in software, but also in training officers to understand the proper scope > and limitations of data-driven policing. The use of data, like the use of > any tool, leaves openings for misuse, but police departments can take steps > to protect civil liberties. There is a big difference, for example, between > predicting where crime is most likely to occur and developing lists of > potential future offenders without probable cause, a practice that > certainly raises serious ethical and legal concerns.Policy makers also > must grapple with the proper scope of data collection, retention and use > and be able to explain to the community how data is being used to enhance > public safety. That is why departments that adopt predictive-policing > programs must at the same time re-emphasize their commitment to community > policing. Officers won’t achieve substantial reductions in crime by holing > up in patrol cars, generating real-time hot-spot maps. Effective policing > still requires that officers build trust with the communities they serve. > With > proper implementation, monitoring and transparency, the trend toward > evidence-based policing should ultimately enhance the relationship between > communities and police officers. That’s because data-driven decision making > is a step away from decisions based on biases that can result in unfair > discrimination. Predictive models grounded in relevant data, including > everything from past crime to the weather, limit the influence of prejudice > or profiling by officers. The stakes are high, but predictive policing > offers an opportunity to make significant advances toward a safer and more > just society.Dr. Bachner is director of the master of science in > government analytics program at Johns Hopkins University. She can be > reached at [email protected] <javascript:>. NO: It Is Ineffective and Will > Increase Police BiasBy Jennifer Lynch Proponents of predictive policing > claim it will lead to unbiased policing and reduced crime. But in reality, > it will only further focus police surveillance on communities that already > are overpoliced and could threaten our constitutional protections and > fundamental human rights.There is little data to back up claims by makers > of predictive-policing systems that their products actually work. In fact, > one of the few independent studies > <http://www.rand.org/pubs/research_reports/RR531.html> available—by Rand > Corp.—found that predicting technology used in Shreveport, La., was > ineffective at reducing crime. This is likely due to the way predictive > systems work. All predictive-policing systems analyze historical crime data > to predict where crimes are likely to occur in the future. Some also rely > on weather data, consumer financial data, property records and even > information about family members or gathered from social-media posts to > predict who is likely to be involved in future crimes. But these systems > aren’t clairvoyant. Because algorithm-training models must rely on data > about known past crimes, they can only predict future incidents that > resemble the nature, time and location of prior crimes. Previous Cities > CoverageAtlanta’s Mayor on the Leadership Role of Cities > <http://www.wsj.com/articles/kasim-reed-on-the-future-of-cities-as-global-leaders-1404764926> > Tokyo’s > Plan for Hydrogen-Powered Olympics > <http://www.wsj.com/articles/tokyo-hopes-to-make-hydrogen-power-the-star-of-the-2020-olympics-1442174267> > A > Cartoonist’s Skewed Look at Manhattan’s Small Businesses > <http://www.wsj.com/articles/a-cartoonists-skewed-look-at-small-business-1448248054>A > > Garden Grows in Champaign > <http://www.wsj.com/articles/second-acts-a-garden-grows-in-champaign-1458525868> > That > means predictive-policing systems will miss at least 50% of crime because > we only have data on about half of the crime > <http://www.bjs.gov/index.cfm?ty=pbdetail&iid=5366> that occurs in the > U.S., according to government estimates; the other half is never reported. > The result is that systems will miss crimes that don’t fit patterns from > the past, and law-enforcement agencies will devote more resources to > looking for crimes they would already have found the old-fashioned way and > less on crimes that require longer and deeper > investigations.Predictive-policing > systems also are vulnerable to a feedback-loop problem: As data on arrests > and criminal activity reported as a result of predictive policing are fed > back into the system, they will justify initial crime prediction and ensure > police will continue to look for crime in the same places as they always > have.Putting aside concerns about effectiveness, using past crime as a > model for predicting future crime has a deeper problem: It will perpetuate > police bias. All of us commit crime, yet only some crimes are selected for > enforcement. This is due partly to departmental priorities but also to > well-documented racially biased policing > <http://www.civilrights.org/publications/reports/racial-profiling2011/the-reality-of-racial.html>. > > Police bias informs crime data fed into predictive-policing systems, > reinforcing existing inequalities in which neighborhoods and racial groups > are most targeted by police. This makes decisions to focus on certain areas > or groups appear impartial because the algorithm itself can’t be racist. It > also allows intentional racism to be disguised as an unintentional > byproduct of the system.Predictive-policing systems that rely on > information from social-media posts to predict whether a person may be more > likely to engage in crime or escalate the dangerousness of a situation also > raise free-speech issues. People limit what they say when they know they > are being watched, so models that rely on people’s speech have the very > real potential to chill free expression. Ultimately, we are fundamentally > uncomfortable with the notion that an algorithm can predict what we will do > before we even decide to do it—and tell the police about it. A system that > takes incomplete, unreliable and biased data and spits out a conclusion > that a particular person will commit a crime—or that crime will occur in a > particular community—doesn’t give people the opportunity to choose a > different path. Instead, by increasing police focus on certain people and > areas, the prediction that someone will commit crime or that some > communities will have more crime almost becomes a self-fulfilling prophecy, > because when the number of police is increased in a given area, it almost > always results in more arrests.Rather than relying on predictive models > to find crime, analytics could be used to address underlying societal > factors that can lead to criminal behavior. A pilot program > <http://www.nccdglobal.org/sites/default/files/publication_pdf/la_dpp_evaluation_report.pdf> > > in Los Angeles, for example, is using predictive models to find the most > at-risk children in the child-welfare system and provide them with services > designed to help them stay out of the juvenile-justice system. With > appropriate resources, these kinds of programs could do more to change the > cycle of crime than using yet another technology to put people behind bars. > > > > > > > > > __._,_.___ > ------------------------------ > Posted by: "Beowulf" <[email protected] <javascript:>> > ------------------------------ > > > Visit Your Group > <https://groups.yahoo.com/neo/groups/grendelreport/info;_ylc=X3oDMTJmcmpmaDNyBF9TAzk3MzU5NzE0BGdycElkAzIwMTk0ODA2BGdycHNwSWQDMTcwNTMyMzY2NwRzZWMDdnRsBHNsawN2Z2hwBHN0aW1lAzE0NjE2MjM0ODM-> > > > - New Members > > <https://groups.yahoo.com/neo/groups/grendelreport/members/all;_ylc=X3oDMTJnYWE0bjhuBF9TAzk3MzU5NzE0BGdycElkAzIwMTk0ODA2BGdycHNwSWQDMTcwNTMyMzY2NwRzZWMDdnRsBHNsawN2bWJycwRzdGltZQMxNDYxNjIzNDgz> > > 2 > > [image: Yahoo! 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