Artificial Intelligence-Based Student Activity Monitoring for Suicide Risk: Considerations for K-12 Schools, Caregivers, Government, and Technology Developers.

In response to the widespread youth mental health crisis, some kindergarten-through-12th-grade (K-12) schools have begun employing artificial intelligence (AI)-based tools to help identify students at risk for suicide and self-harm. The adoption of AI and other types of educational technology to partially address student mental health needs has been a natural forward step for many schools during the transition to remote education. However, there is limited understanding about how such programs work, how they are implemented by schools, and how they may benefit or harm students and their families. To assist policymakers, school districts, school leaders, and others in making decisions regarding the use of these tools, the authors address these knowledge gaps by providing a preliminary examination of how AI-based suicide risk monitoring programs are implemented in K-12 schools, how stakeholders perceive the effects that the programs are having on students, and the potential benefits and risks of such tools. Using this analysis, the authors also offer recommendations for school and district leaders; state, federal, and local policymakers; and technology developers to consider as they move forward in maximizing the intended benefits and mitigating the possible risks of AI-based suicide risk monitoring programs.

Barred from employment: More than half of unemployed men in their 30s had a criminal history of arrest.

We investigate what portion of the pool of unemployed men in the United States have been arrested, convicted, or incarcerated by age 35. Using the National Longitudinal Survey of Youth, 1997, we estimate 64% of unemployed men have been arrested, and 46% have been convicted. Unexpectedly, these rates vary only slightly by race and ethnicity. Further investigation of other outcomes such as marriage, education, household net worth, and earnings shows large differences between unemployed men who have a criminal history record and those who do not. One major implication of these findings is that employment services should focus more on the special challenges facing unemployed men with criminal history records. A second implication is that statistical discrimination against unemployed members of racial minority groups, to avoid hiring those with criminal histories, is both illegal and ineffective.