Time will tell, but it’s likely that machine learning will make all our lives safer and better someday soon.

Please see the below for the primary source of your cited article. Thank you for a thought-provoking morning read. As I pondered your concluding statement regarding the machine learning industry.

Simply, the article found the following:

Previous Violent Behavior (Pearson Correlation = 0.80), Impulsivity (0.69), School Problems (0.64), and Negative Attitudes (0.61) were positively correlated with risk to others.

They simply put in the data from a school evaluation questionnaire that a trained human could perform. They entered all the data into a computer. The computer spits out an answer to the following question: Is the student ready to explode?

Here is the actual method of the study:

School risk evaluations were performed with each participant, and audio recordings from the evaluations were later transcribed and manually annotated. The BRACHA (School Version) and the School Safety Scale (SSS), both 14-item scales, were used. A template of open-ended questions was also used.

The novel machine learning algorithm achieved an AUC of 91.02% when using the interview content to predict risk of school violence, and the AUC increased to 91.45% when demographic and socioeconomic data were added.

What the author didn’t ask was if the machine AI battled with a trained, empathic school counselor, who would win? I wonder what the human’s AUC to predict the risk of school violence is? I wonder why we need machines to tell if a child is ready to blow?

Aren’t these the jobs of mothers, fathers, and the school? I forget we are stretched beyond our means. Parents and teachers alike because the machine language industry is becoming better parents and counselors who continue to ignore the obvious but silent pains of our adolescents, ready to explode.

I think machine learning to spy into people’s violent behavior can be useful but at the cost of relinquishing our humanity to machines. Perhaps, it’s better to be asleep at the wheel and let the self-driving cars lead the way to a better future for our children. Perhaps it’s better we let China lead the way into a surveillance state.

IMHO. God bless the teenagers of our mother Earth. Shanti.

Abstract

School violence has increased over the past ten years. This study evaluated students using a more standard and sensitive method to help identify students who are at high risk for school violence. 103 participants were recruited through Cincinnati Children’s Hospital Medical Center (CCHMC) from psychiatry outpatient clinics, the inpatient units, and the emergency department. Participants (ages 12–18) were active students in 74 traditional schools (i.e. non-online education). Collateral information was gathered from guardians before participants were evaluated. School risk evaluations were performed with each participant, and audio recordings from the evaluations were later transcribed and manually annotated. The BRACHA (School Version) and the School Safety Scale (SSS), both 14-item scales, were used. A template of open-ended questions was also used. This analysis included 103 participants who were recruited from 74 different schools. Of the 103 students evaluated, 55 were found to be moderate to high risk and 48 were found to be low risk based on the paper risk assessments including the BRACHA and SSS. Both the BRACHA and the SSS were highly correlated with risk of violence to others (Pearson correlations>0.82). There were significant differences in BRACHA and SSS total scores between low risk and high risk to others groups (p-values <0.001 under unpaired t-test). In particular, there were significant differences in individual SSS items between the two groups (p-value <0.001). Of these items, Previous Violent Behavior (Pearson Correlation = 0.80), Impulsivity (0.69), School Problems (0.64), and Negative Attitudes (0.61) were positively correlated with risk to others. The novel machine learning algorithm achieved an AUC of 91.02% when using the interview content to predict risk of school violence, and the AUC increased to 91.45% when demographic and socioeconomic data were added. Our study indicates that the BRACHA and SSS are clinically useful for assessing risk for school violence. The machine learning algorithm was highly accurate in assessing school violence risk.