GateHouse: Neurolaw: Discovering the past and the future

Matthew T. Mangino

GateHouse Media

September 13, 2019

The criminal justice system is not precise. The burden of proof in a criminal case is not guilt to a mathematical certainty or beyond all doubt. Proving an accused guilty of a crime does require a heavy burden - beyond a reasonable doubt - but it does not provide errorless outcomes.

As a result, there is a level of tolerance in criminal cases that an occasional innocent person will be convicted. Sure there are technological advances that have resulted in exonerations of people imprisoned for crimes they did not commit. Those advances, particularly DNA, when available, have provided a safety net for those falsely convicted.

How can science help investigators get it right the first time?

Eryn Brown wrote recently in Knowable Magazine, about Judge Morris B. Hoffman, of Colorado's 2nd Judicial District Court, a leader in neurolaw research. He predicted that neuroscientists are likely "in the next 10 to 50 years ... (to) be able to detect memories and lies, and to determine brain maturity."

If investigators were able to detect when someone is lying or recreate their memory, could that help insure that the guilty are convicted and the innocent are set free?

The primary element of most criminal statutes is intent. An actor's responsibility is often determined by whether their conduct was reckless or intentional. Stephen J. Morse,Professor of Law and the Associate Director of the University of Pennsylvania's Center for Neuroscience and Society, contends that his research "found that they were able to predict with a high degree of accuracy whether a person was in a knowing or reckless state and were also able to associate those mental states with unique functional brain patterns."

That finding is significant. Using science to determine intent would be a huge advancement in neurolaw. However, Morse's study was conducted in real time using MRI brain scans while the decisions were being made by the participants. Unfortunately, criminals don't wear devises that scan their brain when they act.

Morse and his colleagues admit as much when he wrote, "Even if several future studies confirm what we have observed here, that knowledge and recklessness are associated with different brain states, if human jurors cannot distinguish them behaviorally, then one may still ask whether they should be considered relevant to assessments of criminal liability."

The whole idea of neurolaw raises some concerns. Neurolaw is creeping into courtrooms across the country on an ever-increasing basis. Brown wrote, "In criminal courts, MRIs are most often used to assess brain injury or trauma ... (I)f a murder defendant's brain scan reveals a tumor in the frontal lobe, for instance, or evidence of frontotemporal dementia, that could inject just enough doubt to make it hard for a court to arrive at a guilty verdict."

That type of evidence is often used as mitigation, not that the accused didn't commit the crime, but that they are less responsible.

Science is not only looking back at what the brain can tell us about a criminal's state of mind at the time of a crime, but also what might happen in the future. In "Predicting violent behavior: What can neuroscience add?" the authors suggested that neuroprediction offers the potential to identify brain function that can distinguish the callous criminal from the immature or dysfunctional actor who might benefit from treatment or preventive programming.

Neuroscience is also being considered for use as a tool to predict who might be most likely to commit a crime in the future. What do we do with those individuals labeled as future criminals? Do we lock them up for crimes they haven't yet committed?

What role neurolaw plays in measuring culpability and predicting human behavior will best be left to ethicists, scientists and legal scholars.

Matthew T. Mangino is of counsel with Luxenberg, Garbett, Kelly & George P.C. His book The Executioner's Toll, 2010 was released by McFarland Publishing. You can reach him at www.mattmangino.com and follow him on Twitter @MatthewTMangino.

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Forecasting crime: Penn professor on the cutting edge

University of Pennsylvania criminology professor Richard Berk tackled Philadelphia’s probation-and-parole challenge with a computer-modeling technique called “random forests.” I had the pleasure of being a part of the Penn Criminology masters program when Professor Berk joined the Penn Faculty.  I also worked with Dr. Berk while he did some forecasting for the Pennsylvania Board of Probation and Parole.

Here’s how random forest works, according to the Pennsylvania Gazette:

Berk gathered a massive amount of data about 30,000 probationers and parolees who’d been free for at least two years. He fed it into an algorithm that randomly selected different combinations of variables, and fit the information to a known outcome: whether someone had been charged with homicide or attempted homicide in that time frame. The algorithm repeated this process hundreds of times, producing a “forest” of individual regression trees that took arbitrary paths through the data. For instance, one tree might begin by considering parolees’ ages, then the number of years that had passed since their last serious offense, then their current residential ZIP code, then their age at the time of their first juvenile offense, then ZIP code (again), then the total number of days they had been incarcerated, and so on, creating a sort of flow chart that sorts any given individual into a category: homicide, or no homicide. Another tree would follow the same procedure, but using different combinations of variables in a different order.

To test the predictive power of this forest, Berk then fed it data on 30,000 different cases—whose outcomes were also known, but which had not been used to build the model. Each was assessed by every tree in the forest, which cast a “vote” on the likelihood that the individual would try to kill again. Those votes were tabulated to generate a final forecast for each case. Importantly, the forest is a black box; there’s no way to know how—let alone why—it arrives at any given prediction.

Assessing a prediction’s value is tricky. Out of the 30,000 individuals in the test sample, 322 had actually been charged with homicide or attempted homicide within two years. So simply predicting that any given person would not kill again would make you right 99 percent of the time. But that would prevent no deaths. A standard logistic regression using the same data, by comparison, fingered two out of 30,000 subjects as likely to commit murder, and it was right about one of them. Not very impressive, but at least it might have saved one life.

Berk’s algorithm was in a different universe. It forecasted that 27,914 individuals would not attempt murder within two years, and it was right about 99.3 percent of them. It identified 1,764 as at risk for killing, 137 of whom in fact faced homicide charges. Generating a prediction for any given individual, using data already available to criminal-justice decision-makers, took “just 10 or 15 seconds,” according to a subsequent review.

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The Cautionary Instruction: Predicting crime is fine, predicting criminals … not so fast

Matthew T. Mangino
The Pittsburgh Post-Gazette/Ipso Facto
August 22, 2014
Predictive analytics has made its way into the criminal justice system through the use of assessments to predict future risk. U.S. Attorney General Eric Holder doesn’t think it’s a good idea.
Predictive analytics is the process by which analysts are able extract information from a huge amount of data in order to reveal patterns and make predictions about what might happen in the future. Predictive analytics is not a crystal ball, but it is a tool that looks into the future with an acceptable level of reliability.
Holder cautioned against the use of data in sentencing criminal defendants, saying judges should base punishment on the facts of a crime rather than on statistical predictions of future behavior that can be unfair to minorities.
"Criminal sentences must be based on the facts, the law, the actual crimes committed, the circumstances surrounding each individual case, and the defendant's history of criminal conduct. They should not be based on unchangeable factors that a person cannot control, or on the possibility of a future crime that has not taken place," Holder said.
The concept is not new. The Commonwealth of Virginia has used risk assessment in sentencing for 15 years. The higher the assessment score, the less likely the offender will be diverted from prison. The result has been fewer people in prison and a crime rate lower than the national average.
Risk forecasting is not just relegated to the courtroom. Police departments have been refining forecasting over the last two decades.
Five years ago, Holder’s justice department sponsored a National Institute of Justice Symposium on Predictive Policing. Then Assistant Attorney General Laurie O. Robinson told the conference attendees, “Eric Holder is thinking a great deal about where we are in the evolution of law enforcement. He knows, as all of you do, that we’re at a point where some very strategic, and collaborative, thinking is in order.”
Predictive policing is the use of analytical techniques to identify promising targets for police intervention with the goal of preventing crime, solving past crimes, and identifying potential offenders and victims. These techniques can help departments address crime problems more effectively and efficiently.
Jeremy Heffner of Azavea, a firm specializing in geographic information system mapping said, “You can kind of think of crime as a disease. If a crime happens, we can see how it affects the likelihood that another incident is going to happen within a certain area in a certain amount of time after that.”

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Matthew T. Mangino is of counsel with Luxenberg, Garbett, Kelly & George, P.C. He is the former district attorney of Lawrence County and just completed a six year term on the Pennsylvania Board of Probation and Parole. His weekly column on crime and punishment is syndicated by GateHouse New Service. You can read his musings on the criminal justice system at www.mattmangino.com and follow Matt on Twitter @MatthewTMangino. His new book The Executioner’s Toll, 2010: The Crimes, Arrests, Trials, Appeals, Last Meals, Final Words and Executions of 46 Persons in the United States is now available from McFarland & Company publishers.
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GateHouse: The perils of crime forecasting

Matthew T. Mangino

GateHouse News Service

November 8, 2013

 

Imagine a crime-fighting model that rushes police not to where a crime has just been committed, but to where a crime is going to be committed.

 

Does that sound like the plot of a futuristic sci-fi movie?  The concept is not only possible — it is a reality in a number of cities across the country. 

 

The idea of forecasting crime, in much the same way meteorologists forecast weather, has turned the law enforcement community on its head. The old model — dial 911, police dispatched, criminal gone — has been discarded for sophisticated computer generated models that predict were crime is going to occur.

 

“We’re entering a new era of police work where advances in technology are providing us with an additional tool to use in our crime prevention efforts,” Fort Lauderdale (Fla.) Police Chief Frank Adderley told Fast Company Magazine. “Integrating advanced data analysis into our operational strategies will help us maximize resources and stay one step ahead of the criminals.”

 

Jeremy Heffner of Azavea, a firm specializing in geographic information system mapping, told Temple University’s Philadelphia Neighborhoods, “You can kind of think of crime as a disease. If a crime happens, we can see how it affects the likelihood that another incident is going to happen within a certain area in a certain amount of time after that.”

 

Heffner suggests that if a residential burglary occurs within a specific neighborhood, the chances that another will occur in that neighborhood increases as a result of the first crime, much like a contained outbreak of disease in a given area.

 

Jeffrey Brantingham, co-founder of the predictive policing company PredPol, explained his company’s software to Government Technology magazine. PredPol takes information about crime being committed — when and where it happens — and applies mathematical algorithms, and uses it as the basis to forecast where crime will happen in the future.

 

The concept grew out of using crime mapping and hot spots to track where crime is occurring. Instead of push pins placed on a precinct cork board, a computer churns out data driven trends about a street, neighborhood or whole community.

 

Forecasting models are dynamic; they can change. As data is analyzed the forecast is updated in real time.  This allows police officers to adapt to the contours and patterns of the model and effectively utilize crime fighting resources.

 

Brantingham is quick to point out that while the forecasting models are about predicting crime, they are not a profiling tool to identify who is committing crimes.

 

“We’re actually not saying anything about who, we are saying something about where and when crime is most likely to occur regardless of who may or may not be prone to commit those crimes,” he told Government Technology.

 

And this is where it gets tricky. The United States Constitution protects people from unlawful searches and seizures. The Fourth Amendment provides that any search, arrest or detention will be based on reasonable suspicion or probable cause.

 

Can a computer loaded with data provide the requisite level of suspicion? Does American jurisprudence permit the sacrifice of the rights of an occasional outlier for the sake of the greater good? That is a fundamental question of justice.  Eighteenth-century English jurist William Blackstone said, "It is better that 10 guilty persons escape than that one innocent suffer.”

 

Andrew Guthrie Ferguson, a law professor at the University of the District of Columbia who has focused his research on crime forecasting software told National Public Radio that the departments using crime forecasting have told police not to use it as a basis for stops.

 

"The idea that you wouldn't use something that is actually part of the officer's suspicion and not put that in — [that] may come to a head when that officer is testifying," Ferguson added.

 

To what extent will liberty suffer to protect the public from crime or the potential of crime?

 

Matthew T. Mangino is of counsel with Luxenberg, Garbett, Kelly and George and the former district attorney for Lawrence County, Pa. You can read his blog at www.mattmangino.com and follow him on Twitter at @MatthewTMangino.

 

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Predicting crime: Police use cutting edge technology to stop crime

There is a movement afoot in the criminal justice system to look into the future.  Forecasting has been used for sentencing, community supervision and now what is known as “predictive policing.” Forecasting for police involves crunching data to determine where to send officers to thwart would-be thieves and burglars. 

Los Angeles Police Department is the largest agency to embrace the forecasting experiment. Early successes could serve as a model for other cash-strapped law enforcement agencies, but some legal observers are concerned it could lead to unlawful stops and searches that violate Fourth Amendment protections, reported the Oklahoman.

In the San Fernando Valley, where the program was launched late last year, officers are seeing double-digit drops in burglaries and other property crimes. The program has turned enough in-house skeptics into believers that there are plans to roll it out citywide by next summer.

“We have prevented hundreds and hundreds of people coming home and seeing their homes robbed,” police Capt. Sean Malinowski told the Oklahoman.

Crime mapping has long been a tool used to determine where the bad guys lurk. The idea has evolved from colored pins placed on a map to identifying “hot spots” via a computer database based on past crimes and possible patterns.

Over the past decade, many large police departments, including Los Angeles and New York City, have used CompStat, a system that tracks crime figures and enables police to send extra officers to trouble spots.

The new program used by LAPD and police in the Northern California city of Santa Cruz is more timely and precise, proponents said. Built on the same model for predicting aftershocks following an earthquake, the software promises to show officers what might be coming based on simple, constantly calibrated data — location, time and type of crime.

According to the Oklahoman, the software generates prediction boxes — as small as 500 square feet — on a patrol map. When officers have spare time, they are told to “go in the box.”

The goal is not to boost the number of arrests, a common police benchmark to reflect crime reduction. Officers want to either intercept a crime in progress or deter would-be criminals.

“I want to disrupt an activity before an arrest is made,” Malinowski said. “You can't arrest your way out of some of these problems.”

Jeff Brantingham, an anthropology professor at the University of California, Los Angeles, said the data also is derived from criminal behaviors — repeat victimization and the notion that criminals tend not to stray too far from areas they know best.

“If you are victimized today the risk that you'll be a victim again goes way up,” Brantingham told the Oklahoman.

So far, the program has been implemented in five LAPD divisions that cover 130 square miles and roughly 1.3 million people. In the valley's Foothill Division, where more than half of the crimes committed are property-related, about 170 patrol officers are spending a total of about 70 hours a week working in the boxes.
To read more: http://newsok.com/sci-fi-policing-predicting-crime-before-it-occurs/article/3689320#ixzz1zWD3bLDr