Cervantes

Hoy es el día más hermoso de nuestra vida, querido Sancho; los obstáculos más grandes, nuestras propias indecisiones; nuestro enemigo más fuerte, el miedo al poderoso y a nosotros mismos; la cosa más fácil, equivocarnos; la más destructiva, la mentira y el egoísmo; la peor derrota, el desaliento; los defectos más peligrosos, la soberbia y el rencor; las sensaciones más gratas, la buena conciencia, el esfuerzo para ser mejores sin ser perfectos, y sobretodo, la disposición para hacer el bien y combatir la injusticia dondequiera que esté.

MIGUEL DE CERVANTES
Don Quijote de la Mancha.

8 de octubre de 2015

Minority Report PreCrime Test Claims Success; Seeks Expansion Worldwide

nsa-ops-centerBy Nicholas West
Well, it sounds like PreCrime policing is officially set to expand across the nation, and perhaps the world.
The pace of technological advancement is now quickening to the point where the gap between science fiction and reality is being greatly reduced. Philip K. Dick explored the concept of PreCrime in his short story “The Minority Report” in 1956, but it wasn’t until Steven Spielberg offered it on the big screen as Minority Report in 2002 that the audience got a true look at a potential day-to-day existence under corporate and government data management and control.
The problem is that it can’t be called fiction if it’s actually happening; and among the vast array of surveillance and Big Brother intrusion nothing will likely affect the average person as much as the concept of PreCrime taking hold in modern policing.

PreCrime Internet systems already continuously scour and collect data for potentially incriminating patterns, and this is merging in ever greater frequency with police activity on the streets. I’ve previously reported on the following states which have field-tested and in some cases implemented PreCrime without feedback from the citizens of these areas. Referred to as “hot spots,” the concept of predicting outbreaks of criminality and violence are being sold to a general public who is rightly concerned about their physical safety, but may not realize the slippery slope being tread upon.
Chicago’s “Heat List” is an index of approximately 400 people who have been identified by a computer algorithm as being future threats to commit violent crime. Without having actually committed a crime, some of those on the list have actually been visited by Chicago police warning them that they are being watched.
In California, a sociologist at the University of California, Riverside has been working with the Indio Police Department to offer a computer dragnet that can predict where burglaries are going to happen in the future. Prof. Robert Nash Parker has developed  a “computer model that predicts, by census block group, where burglaries are likely to occur.” Notably, Indio only has a population of 75,000, indicating that no area is to be considered off the radar of the technocratic police state.
And in Arizona, mental health PreCrime systems are searching for people “near the breaking point.” The system can harvest everything from medical records to gun purchases to online posts. Citing the crimes of Jared Loughner and Elliot Rodger, these units are being given the green light with new legislation to involuntarily detain those who are flagged.
But it was a Miami Herald article from April of this year which detailed the extent of these systems. Known locally as Miami’s “HunchLab” the article revealed plans for a nationwide rollout backed by federal grants. My emphasis.
The probability program is a geographical version of “predictive policing” software, which more departments are using — even if, in the words of one supportive cop, it’s “kind of scary.”
Similar algorithm-based programs have been credited with lowering crime rates in cities around the country, and some South Florida departments recently have adopted their own systems. In Miami’s case, the department is funding the implementation of HunchLab and other software programs with a $600,000 federal grant doled out by the Bureau of Justice Assistance to encourage smart policing tactics.
[…] Police, of course, have always tried to use data to identify crime trends, and for years Miami police have done that with the data-crunching system known as COMPSTAT. Except, now, instead of identifying where crime hot spots have occurred, they’re looking at where crime will occur.
“It goes beyond just looking at crime data,” MacDonald said.
Just how much — and whether — predictive software really works remains somewhat of a question. But officers in Los Angeles say a program known as PredPol developed by the department and college professors, and now sold to departments around the country, has helped prevent and stop property crimes, and is now being tested on gun crimes.
[…] “We all thought it was somewhat hocus pocus and Minority Report,” she said, referring to the Steven Spielberg sci-fi film in which police used psychic powers to stop murders before they happened. “We could see if PredPol was predicting fairly well. It’s kind of scary, because they were.”
Scary, more like, because the scope is expanding and these programs continue to be improved upon:
In Miami, MacDonald said that the software the department is using is a more elaborate version of PredPol, which uses only crime data. Miami police also are punching in everything from paydays to school calendars, weather reports and social media. The department also is using the federal grant to establish an offender database.
And there it is: the admission of full-scale integration to social media and the establishment of databases directed from the federal level.
So, seeing the writing on the wall, perhaps one might choose to duck out of the announced totalitarian tech takeover of the United States. Not so fast. A new study from UCLA under the press release title “Predictive policing substantially reduces crime in Los Angeles during months-long test” carries this subtitle: UCLA-led study suggests method could succeed in cities worldwide.
So, what are the citizens of planet earth going to be exposed to?  First off, it is notable – and frightening – where the funding originates: the military.
The research was funded by the National Science Foundation (grant DMS-0968309), the Air Force Office of Scientific Research (grant FA9550-10-1-0569), Office of Naval Research (grants N000141010221 and N000141210838) and the Army Research Office (grants W911NF1010472 and W911NF1110332).
Moreover, there appears to be a conflict of interest that is often seen in this type of security tech. The author of the study, a UCLA professor, is actually the founder of the aforementioned PredPol PreCrime software. That conflict would normally render all assertions mere wishful thinking, but we certainly have seen even our share even of government officials directly profit off of the legislation they have created (airport body scanners anyone?)
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My emphasis added:
Can math help keep our streets safer?
A new study by a UCLA-led team of scholars and law enforcement officials suggests the answer is yes. A mathematical model they devised to guide where the Los Angeles Police Department should deploy officers, led to substantially lower crime rates during a recent 21-month period.
“Not only did the model predict twice as much crime as trained crime analysts predicted, but it also prevented twice as much crime,” said Jeffrey Brantingham, a UCLA professor of anthropology and senior author of the study. A paper about the work, which was also tested in Kent, England, was published online today by the Journal of the American Statistical Association.
The model was so successful that the LAPD has adopted it for use in 14 of its 21 divisions, up from three in 2013.
Developed using six years of mathematical research and a decade of police crime data, the program predicts times and places that serious crimes will occur based on historical crime data in a given area. A key to its success, Brantingham said, is that the algorithm behind the model effectively “learns” over time.
“In much the same way that your video streaming service knows what movie you’re going to watch tomorrow, even if your tastes have changed, our algorithm is constantly evolving and adapting to new crime data,” he said.
Beginning in 2011, the researchers analyzed crime trends in the LAPD’s Southwest division and in two Kent divisions to determine whether their model could predict, in real time, when and where major crimes would occur. Their analysis in Los Angeles focused on burglaries, theft from cars and theft of cars. In Kent, they studied patterns for those crimes as well as violent crimes including assault and robbery.
The researchers tested the computer model by pitting it against professional crime analysts, seeing which could more accurately predict where crimes would occur. On each of 117 days in Los Angeles, they gave the human analysts a map of the entire police district and asked them to identify one precise location — only about half-a-block in size — where a crime would be most likely to occur within a specific 12-hour period. The algorithm was programmed to answer the same question. (In this phase of the experiment, police officers did not act on the model’s predictions.)
In Los Angeles, the mathematical model correctly predicted the locations of crimes on 4.7 percent of its forecasts, while the human analysts were correct just 2.1 percent of the time. In Kent’s two divisions, the model predicted 9.8 percent and 6.8 percent of the crimes; the analysts were correct 6.8 percent and 4 percent of the time. (Although those success rates might not appear to be dramatic, it’s important to note that the predictions were focused on minuscule target locations: The predicted hot spots represented less than 1 percent of Los Angeles’ land area, and an even smaller percentage of Kent.)
In the next phase of the study, police officers in each of three LAPD divisions — North Hollywood, Southwest and Foothill (in the northeastern San Fernando Valley) — were deployed to 20 half-block areas based on the predictions of either the model or the human analysts, on random days for between four and eight months. Neither the officers nor their commanders knew whether the assignments came from the computer or the professional analysts.
Officers were instructed to go to the specified areas, which were marked on maps as red boxes, to respond as they saw fit and to stay in the locations as long as they deemed necessary. Across the three divisions, the mathematical model produced 4.3 fewer crimes per week, a reduction of 7.4 percent, compared with the number of crimes that the police would have expected had officers not patrolled the “red box” areas. Crime was reduced when officers patrolled the areas selected by the human analysts as well, but only by two crimes per week in each division.
Based on those results, the researchers estimated that using the algorithm would save $9 million per year in Los Angeles, taking into account costs to victims, the courts and society.
Brantingham said the mathematical model’s success rate could be improved even further as the researchers enhance the algorithm it uses.
Based on its own test run, the Kent police now are rolling out the mathematical model to other divisions throughout the county.
“We have worked closely with counterparts in Los Angeles from the moment we became interested in predictive policing and the benefits it brings to keeping communities safe,” said Mark Johnson, head of analysis for the Kent police.
Brantingham thinks the mathematical model would be effective in cities worldwide. He is a co-founder of PredPol, a company that markets predictive policing software to cities including Atlanta and Tacoma, Washington.
Brantingham also emphasized that the algorithm cannot replace police work; it’s intended to help police officers do their jobs better.
“Our directive to officers was to ‘get in the box’ and use their training and experience to police what they see,” said Cmdr. Sean Malinowski, the LAPD’s chief of staff.  “Flexibility in how to use predictions proved to be popular and has become a key part of how the LAPD deploys predictive policing today.”
Many social scientists have said human behavior and criminal behavior are too complex to be explained with a mathematical model, but Brantingham strongly disagrees.
“It’s not too complex,” he said. “We’re not trying to explain everything, but there are many aspects of human behavior that we can understand mathematically.”
Other co-authors are Andrea Bertozzi, UCLA professor of mathematics and director of applied mathematics; George Mohler, a former UCLA mathematics postdoctoral scholar; Martin B. Short, a former UCLA assistant adjunct professor of mathematics, who is currently an assistant professor at the Georgia Institute of Technology; and George Tita, an associate professor at UC Irvine.
Source
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It appears to be a matter of time before these algorithms increase in speed and accuracy to the point where it will become too tempting not to implement this program on a worldwide scale, especially if cost-cutting can be cited as well. What it does to the basic concept of being innocent until proven guilty that most democratic countries abide by remains to be seen.
And while we are trying to eliminate human weakness, keep in mind that there is a very receptive robot police force being groomed to receive and carry out these new commands should human police not be up to the task.

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