Using Artificial Intelligence to Predict Violence. . . or to Control It?

Dell EMC InFocus

A staple of science fiction is a future where artificial intelligence (AI) is used to control people's behavior, or, in which AI supplants the human element.

I have mixed feelings about AI, its uses, and our future. 

For one thing, it's questionable that the human brain can increase in size or complexity without causing the system to slow down.  The brain of a "genius" isn't larger than yours or mine, its simply organized differently.  The difference between early versions of humans and modern humans is in large part, size.  Yes, brain organization plays a role, but until the latest iteration of us, brain size was the critical factor.

If we as a species have survived based on brain size, and brain size has maxed out, how do we continue to evolve intellectually?  Better organization?  Yes, but can we ever hope to create and control this type of evolutionary activity?

One solution to maxed human brain size is creating an interface between AI and the human brain, something that scares most people.  But, look at it this way: would we have continued the development of motorized vehicles had we known about the many deaths that occur daily around the world from this technology?  There are other parallel examples.

It is the duty, so to speak, of writers in all genre's to examine these possibilities and questions, which, of course, means we must stay current with the latest science.

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Pilot study validates artificial

intelligence to help predict school violence

A pilot study indicates that artificial intelligence may be useful in predicting which students are at higher risk of perpetrating school violence. The researchers found that machine learning -- the science of getting computers to learn over time without human intervention -- is as accurate as a team of child and adolescent psychiatrists, including a forensic psychiatrist, in determining risk for school violence.

"Previous violent behavior, impulsivity, school problems and negative attitudes were correlated with risk to others," says Drew Barzman, MD, a child forensic psychiatrist at Cincinnati Children's Hospital Medical Center and lead author of the study. "Our risk assessments were focused on predicting any type of physical aggression at school. We did not gather outcome data to assess whether machine learning could actually help prevent school violence. That is our next goal."

Dr. Barzman and his colleagues evaluated 103 teenage students in 74 traditional schools throughout the United States who had a major or minor behavioral change or aggression toward themselves or others. The students were recruited from psychiatry outpatient clinics, inpatient units and emergency departments.

The team performed school risk evaluations with participants. Audio recordings from the evaluations were transcribed and manually annotated. The students, as it turned out, were relatively equally divided between moderate- to high-risk, and low-risk, according to two scales that the team developed and validated in previous research.

There were significant differences in total scores between the high-risk and low-risk groups. The machine learning algorithm that the researchers developed achieved an accuracy rate of 91.02 percent, considered excellent, when using interview content to predict risk of school violence. The rate increased to 91.45 percent when demographic and socioeconomic data were added.

"The machine learning algorithm, based only on the participant's interview, was almost as accurate in assessing risk levels as a full assessment by our research team, including gathering information from parents and the school, a review of records when available, and scoring on the two scales we developed," says Yizhao Ni, PhD, a computational scientist in the division of biomedical informatics at Cincinnati Children's and co-author of the study.

"Our ultimate goal, should research support it, is to spread the use of the machine learning technology to schools in the future to augment structures, professional judgment to more efficiently and effectively prevent school violence, adds Dr. Barzman."

The research was conducted in part through grants from the Park Foundation and the Center for Clinical and Translational Science and Training.

Story Source:  Materials provided by Cincinnati Children's Hospital Medical Center.  Drew Barzman, Yizhao Ni, Marcus Griffey, Alycia Bachtel, Kenneth Lin, Hannah Jackson, Michael Sorter, Melissa DelBello. Automated Risk Assessment for School Violence: a Pilot Study. Psychiatric Quarterly, 2018.