https://youtu.be/OPemyipJzAM
On Tue, Jul 19, 2016 at 3:14 PM, Travis <[email protected]> wrote: > > > > > > > > > > > > > http://www.bloomberg.com/features/2016-richard-berk-future-crime/ > > > > *This Guy Trains Computers to Find Future Criminals* > > *Richard Berk says his algorithms take the bias out of criminal justice. > But could they make it worse?* > > *by Joshua Brustein* > > *July 18, 2016* > > > > > > > > > > When historians look back at the turmoil over prejudice and policing in > the U.S. over the past few years, they’re unlikely to dwell on the case of > Eric Loomis. Police in La Crosse, Wis., arrested Loomis in February 2013 > for driving a car that was used in a drive-by shooting. He had been > arrested a dozen times before. Loomis took a plea, and was sentenced to six > years in prison plus five years of probation. > > The episode was unremarkable compared with the deaths of Philando Castile > and Alton Sterling at the hands of police, which were captured on camera > and distributed widely online. But Loomis’s story marks an important point > in a quieter debate over the role of fairness and technology in policing. > Before his sentence, the judge in the case received an automatically > generated risk score that determined Loomis was likely to commit violent > crimes in the future. > > > > Risk scores, generated by algorithms, are an increasingly common factor in > sentencing. Computers crunch data—arrests, type of crime committed, and > demographic information—and a risk rating is generated. The idea is to > create a guide that’s less likely to be subject to unconscious biases, the > mood of a judge, or other human shortcomings. Similar tools are used to > decide which blocks police officers should patrol, where to put inmates in > prison, and who to let out on parole. Supporters of these tools claim > they’ll help solve historical inequities, but their critics say they have > the potential to aggravate them, by hiding old prejudices under the veneer > of computerized precision. Some people see them as a sterilized version of > what brought protesters into the streets at Black Lives Matter rallies. > > Loomis is a surprising fulcrum in this controversy: He’s a white man. But > when Loomis challenged the state’s use of a risk score in his sentence, he > cited many of the fundamental criticisms of the tools: that they’re too > mysterious to be used in court, that they punish people for the crimes of > others, and that they hold your demographics against you. Last week the > Wisconsin Supreme Court ruled against Loomis, but the decision validated > some of his core claims. The case, say legal experts, could serve as a > jumping-off point for legal challenges questioning the constitutionality of > these kinds of techniques. > > To understand the algorithms being used all over the country, it’s good to > talk to Richard Berk. He’s been writing them for decades (though he didn’t > write the tool that created Loomis’s risk score). Berk, a professor at the > University of Pennsylvania, is a shortish, bald guy, whose solid stature > and I-dare-you-to-disagree-with-me demeanor might lead people to mistake > him for an ex-cop. In fact, he’s a career statistician. > > His tools have been used by prisons to determine which inmates to place in > restrictive settings; parole departments to choose how closely to supervise > people being released from prison; and police officers to predict whether > people arrested for domestic violence will re-offend. He once created an > algorithm that would tell the Occupational Safety and Health Administration > which workplaces were likely to commit safety violations, but says the > agency never used it for anything. Starting this fall, the state of > Pennsylvania plans to run a pilot program using Berk’s system in sentencing > decisions. > > As his work has been put into use across the country, Berk’s academic > pursuits have become progressively fantastical. He’s currently working on > an algorithm that he says will be able to predict at the time of someone’s > birth how likely she is to commit a crime by the time she turns 18. The > only limit to applications like this, in Berk’s mind, is the data he can > find to feed into them. > > *“The policy position that is taken is that it’s much more dangerous to > release Darth Vader than it is to incarcerate Luke Skywalker” * > > > > This kind of talk makes people uncomfortable, something Berk was clearly > aware of on a sunny Thursday morning in May as he headed into a conference > in the basement of a campus building at Penn to play the role of least > popular man in the room. He was scheduled to participate in the first panel > of the day, which was essentially a referendum on his work. Berk settled > into his chair and prepared for a spirited debate about whether what he > does all day is good for society. > > The moderator, a researcher named Sandra Mayson, took the podium. “This > panel is the *Minority Report* panel,” she said, referring to the Tom > Cruise movie where the government employs a trio of psychic mutants to > identify future murderers, then arrests these “pre-criminals” before their > offenses occur. The comparison is so common it’s become a kind of joke. “I > use it too, occasionally, because there’s no way to avoid it," Berk said > later. > > For the next hour, the other members of the panel took turns questioning > the scientific integrity, utility, and basic fairness of predictive > techniques such as Berk’s. As it went on, he began to fidget in > frustration. Berk leaned all the way back in his chair and crossed his > hands over his stomach. He leaned all the way forward and flexed his > fingers. He scribbled a few notes. He rested his chin in one hand like a > bored teenager and stared off into space. > > Eventually, the debate was too much for him: “Here’s what I, maybe > hyperbolically, get out of this,” Berk said. “No data are any good, the > criminal justice system sucks, and all the actors in the criminal justice > system are biased by race and gender. If that’s the takeaway message, we > might as well all go home. There’s nothing more to do.” The room tittered > with awkward laughter. > > > > Berk’s work on crime started in the late 1960s, when he was splitting his > time between grad school and a social work job in Baltimore. The city > exploded in violence following the assassination of Martin Luther King Jr. > Berk’s graduate school thesis examined the looting patterns during the > riots. “You couldn’t really be alive and sentient at that moment in time > and not be concerned about what was going on in crime and justice,” he > said. “Very much like today with the Ferguson stuff.” > > In the mid-1990s, Berk began focusing on machine learning, where computers > look for patterns in data sets too large for humans to sift through > manually. To make a model, Berk inputs tens of thousands of profiles into a > computer. Each one includes the data of someone who has been arrested, > including how old they were when first arrested, what neighborhood they’re > from, how long they’ve spent in jail, and so on. The data also contain > information about who was re-arrested. The computer finds patterns, and > those serve as the basis for predictions about which arrestees will > re-offend. > > To Berk, a big advantage of machine learning is that it eliminates the > need to understand what causes someone to be violent. “For these problems, > we don’t have good theory,” he said. Feed the computer enough data and it > can figure it out on its own, without deciding on a philosophy of the > origins of criminal proclivity. This is a seductive idea. But it’s also one > that comes under criticism each time a supposedly neutral algorithm in any > field produces worryingly non-neutral results. In one widely cited study, > researchers showed that Google’s automated ad-serving software was more > likely to show ads for high-paying jobs to men than to women. Another found > that ads for arrest records show up more often when searching the web for > distinctly black names than for white ones. > > Advertisement > > Computer scientists have a maxim, “Garbage in, garbage out.” In this case, > the garbage would be decades of racial and socioeconomic disparities in the > criminal justice system. Predictions about future crimes based on data > about historical crime statistics have the potential to equate past > patterns of policing with the predisposition of people in certain > groups—mostly poor and nonwhite—to commit crimes. > > Berk readily acknowledges this as a concern, then quickly dismisses it. > Race isn’t an input in any of his systems, and he says his own research has > shown his algorithms produce similar risk scores regardless of race. He > also argues that the tools he creates aren’t used for punishment—more often > they’re used, he said, to reverse long-running patterns of overly harsh > sentencing, by identifying people whom judges and probation officers > shouldn’t worry about. > > > > Berk began working with Philadelphia’s Adult Probation and Parole > Department in 2006. At the time, the city had a big murder problem and a > small budget. There were a lot of people in the city’s probation and parole > programs. City Hall wanted to know which people it truly needed to watch. > Berk and a small team of researchers from the University of Pennsylvania > wrote a model to identify which people were most likely to commit murder or > attempted murder while on probation or parole. Berk generally works for > free, and was never on Philadelphia’s payroll. > > A common question, of course, is how accurate risk scores are. Berk says > that in his own work, between 29 percent and 38 percent of predictions > about whether someone is low-risk end up being wrong. But focusing on > accuracy misses the point, he says. When it comes to crime, sometimes the > best answers aren’t the most statistically precise ones. Just like > weathermen err on the side of predicting rain because no one wants to get > caught without an umbrella, court systems want technology that > intentionally overpredicts the risk that any individual is a crime risk. > The same person could end up being described as either high-risk or not > depending on where the government decides to set that line. “The policy > position that is taken is that it’s much more dangerous to release Darth > Vader than it is to incarcerate Luke Skywalker,” Berk said. > > *“Every mark of poverty serves as a risk factor” * > > > > Philadelphia’s plan was to offer cognitive behavioral therapy to the > highest-risk people, and offset the costs by spending less money > supervising everyone else. When Berk posed the Darth Vader question, the > parole department initially determined it’d be 10 times worse, according to > Geoffrey Barnes, who worked on the project. Berk figured that at that > threshold the algorithm would name 8,000 to 9,000 people as potential > pre-murderers. Officials realized they couldn’t afford to pay for that much > therapy, and asked for a model that was less harsh. Berk’s team twisted the > dials accordingly. “We’re intentionally making the model less accurate, but > trying to make sure it produces the right kind of error when it does,” > Barnes said. > > The program later expanded to group everyone into high-, medium-, and > low-risk populations, and the city significantly reduced how closely it > watched parolees Berk’s system identified as low-risk. In a 2010 study, > Berk and city officials reported that people who were given more lenient > treatment were less likely to be arrested for violent crimes than people > with similar risk scores who stayed with traditional parole or probation. > People classified as high-risk were almost four times more likely to be > charged with violent crimes. > > Since then, Berk has created similar programs in Maryland’s and > Pennsylvania’s statewide parole systems. In Pennsylvania, an internal > analysis showed that between 2011 and 2014 about 15 percent of people who > came up for parole received different decisions because of their risk > scores. Those who were released during that period were significantly less > likely to be re-arrested than those who had been released in years past. > The conclusion: Berk’s software was helping the state make smarter > decisions. > > Laura Treaster, a spokeswoman for the state’s Board of Probation and > Parole, says Pennsylvania isn’t sure how its risk scores are impacted by > race. “This has not been analyzed yet,” she said. “However, it needs to be > noted that parole is very different than sentencing. The board is not > determining guilt or innocence. We are looking at risk.” > > Sentencing, though, is the next frontier for Berk’s risk scores. And using > algorithms to decide how long someone goes to jail is proving more > controversial than using them to decide when to let people out early. > > > > Wisconsin courts use Compas, a popular commercial tool made by a > Michigan-based company called Northpointe. By the company’s account, the > people it deems high-risk are re-arrested within two years in about 70 > percent of cases. Part of Loomis’s challenge was specific to Northpointe’s > practice of declining to share specific information about how its tool > generates scores, citing competitive reasons. Not allowing a defendant to > assess the evidence against him violated due process, he argued. (Berk > shares the code for his systems, and criticizes commercial products such as > Northpointe’s for not doing the same.) > > As the court was considering Loomis’s appeal, the journalism website > *ProPublica* published an investigation looking at 7,000 Compas risk > scores in a single county in Florida over the course of 2013 and 2014. It > found that black people were almost twice as likely as white people to be > labeled high-risk, then not commit a crime, while it was much more common > for white people who were labeled low-risk to re-offend than black people > who received a low-risk score. Northpointe challenged the findings, saying > *ProPublica* had miscategorized many risk scores and ignored results that > didn’t support its thesis. Its analysis of the same data found no racial > disparities. > > > > > > Even as it upheld Loomis’s sentence, the Wisconsin Supreme Court cited the > research on race to raise concerns about the use of tools like Compas. > Going forward, it requires risk scores to be accompanied by disclaimers > about their nontransparent nature and various caveats about their > conclusions. It also says they can’t be used as the determining factor in a > sentencing decision. The decision was the first time that such a high court > had signaled ambivalence about the use of risk scores in sentencing. > > Sonja Starr, a professor at the University of Michigan’s law school and a > prominent critic of risk assessment, thinks that Loomis’s case foreshadows > stronger legal arguments to come. Loomis made a demographic argument, > saying that Compas rated him as riskier because of his gender, reflecting > the historical patterns of men being arrested at higher rates than women. > But he didn’t frame it as an argument that Compas violated the Equal > Protection Clause of the 14th Amendment, which allowed the court to > sidestep the core issue. > > Loomis also didn’t argue that the risk scores serve to discriminate > against poor people. “That’s the part that seems to concern judges, that > every mark of poverty serves as a risk factor,” Starr said. “We should very > easily see more successful challenges in other cases.” > > Officials in Pennsylvania, which has been slowly preparing to use risk > assessment in sentencing for the past six years, are sensitive to these > potential pitfalls. The state’s experience shows how tricky it is to create > an algorithm through the public policy process. To come up with a > politically palatable risk tool, Pennsylvania established a sentencing > commission. It quickly rejected commercial products like Compas, saying > they were too expensive and too mysterious, so the commission began > creating its own system. > > *“If you want me to do a totally race-neutral forecast, you’ve got to tell > me what variables you’re going to allow me to use, and nobody can, because > everything is confounded with race and gender” * > > > > Race was discarded immediately as an input. But every other factor became > a matter of debate. When the state initially wanted to include location, > which it determined to be statistically useful in predicting who would > re-offend, the Pennsylvania Association of Criminal Defense Lawyers argued > that it was a proxy for race, given patterns of housing segregation. The > commission eventually dropped the use of location. Also in question: the > system’s use of arrests, instead of convictions, since it seems to punish > people who live in communities that are policed more aggressively. > > Berk argues that eliminating sensitive factors weakens the predictive > power of the algorithms. “If you want me to do a totally race-neutral > forecast, you’ve got to tell me what variables you’re going to allow me to > use, and nobody can, because everything is confounded with race and > gender,” he said. > > Starr says this argument confuses the differing standards in academic > research and the legal system. In social science, it can be useful to > calculate the relative likelihood that members of certain groups will do > certain things. But that doesn’t mean a specific person’s future should be > calculated based on an analysis of populationwide crime stats, especially > when the data set being used reflects decades of racial and socioeconomic > disparities. It amounts to a computerized version of racial profiling, > Starr argued. “If the variables aren’t appropriate, you shouldn’t be > relying on them," she said. > > Late this spring, Berk traveled to Norway to meet with a group of > researchers from the University of Oslo. The Norwegian government gathers > an immense amount of information about the country’s citizens and connects > each of them to a single identification file, presenting a tantalizing set > of potential inputs. > > Torbjørn Skardhamar, a professor at the university, was interested in > exploring how he could use machine learning to make long-term predictions. > He helped set up Berk’s visit. Norway has lagged behind the U.S. in using > predictive analytics in criminal justice, and the men threw around a few > ideas. > > Berk wants to predict at the moment of birth whether people will commit a > crime by their 18th birthday, based on factors such as environment and the > history of a new child’s parents. This would be almost impossible in the > U.S., given that much of a person’s biographical information is spread out > across many agencies and subject to many restrictions. He’s not sure if > it’s possible in Norway, either, and he acknowledges he also hasn’t > completely thought through how best to use such information. > > Caveats aside, this has the potential to be a capstone project of Berk’s > career. It also takes all of the ethical and political questions and > extends them to their logical conclusion. Even in the movie *Minority > Report*, the government peered only hours into the future—not years. > Skardhamar, who is new to these techniques, said he’s not afraid of making > mistakes: They’re talking about them now, he said, so they can avoid future > errors. “These are tricky questions,” he said, mulling all the ways the > project could go wrong. “Making them explicit—that’s a good thing.” > > > > > ------------------------------ > [image: Avast logo] <https://www.avast.com/antivirus> > > This email has been checked for viruses by Avast antivirus software. > www.avast.com <https://www.avast.com/antivirus> > > > > __._,_.___ > ------------------------------ > Posted by: "Beowulf" <[email protected]> > ------------------------------ > > > Visit Your Group > <https://groups.yahoo.com/neo/groups/grendelreport/info;_ylc=X3oDMTJmcDc4azdmBF9TAzk3MzU5NzE0BGdycElkAzIwMTk0ODA2BGdycHNwSWQDMTcwNTMyMzY2NwRzZWMDdnRsBHNsawN2Z2hwBHN0aW1lAzE0Njg5NTIxNDA-> > > > [image: Yahoo! 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