Study Uses AI to Uncover a Hidden Link Between Legal Troubles and Suicide Risk - USC Viterbi | School of Engineering

Study Uses AI to Uncover a Hidden Link Between Legal Troubles and Suicide Risk - USC Viterbi | School of Engineering
When someone dies by suicide, the focus naturally turns to mental health: depression, isolation, a cry for help that went unanswered. But what if the warning signs were hiding somewhere less obvious, like a courtroom, a police report, or a run-in with the law?
That’s the question a team of USC researchers set out to answer. A new study co-led by the
Thomas Lord Department of Computer Scienc
e at
USC Viterbi
and the
USC School of Advanced Computing
and the
USC Suzanne Dworak-Peck School of Social Work
is using AI to sift through hundreds of thousands of anonymized death records, looking for a pattern the field has largely ignored: the role of legal interactions in the lead-up to suicide.
Uncovering Intervention Opportunities for Suicide Prevention with Language Model Assistants
”collects and analyzes third-party information about suicide deaths in an effort to reduce them. The research was led by a team including
Swabha Swayamdipta
, a WiSE Gabilan assistant professor of computer science at the
Thomas Lord Department of Computer Science,
as well as associate director of the
USC Center for AI in Society
. Swayamdipta worked alongside
Jonathan May
, a research associate professor of computer science at the
Thomas Lord Department of Computer Science
and the director for theArtificial Intelligence Division at the
Information Sciences Institute
The paper was recently accepted to the
main
conference at the
2026 Association for Computational Linguistics
annual meeting this July.
The Viterbi team worked in close collaboration with the
USC Suzanne Dworak-Peck School of Social Work
. Dworak-Peck’s role in the study was led by
John Blosnich
, an associate professor and director of the Center for LGBTQ+ Health Equity as well as interim co-director of the
USC Center for AI in Society
The Legal Interaction No One Was Tracking
Specifically, this study focused on legal interactions, which the Viterbi and Dworak-Peck team considered an underrepresented potential cause of eventual suicide. They used the unstructured data found in the NVDRS database to infer and label cases with legal interactions.
“We don’t typically consider all these life circumstances that the victim goes through in the days before the suicide or in the weeks or months leading up to the suicide,” Swayamdipta said. “A lot of these life disruptors that also lead to suicide have a legal interaction component. We wanted to determine, going through all this unstructured data, how often does that happen?” Eventually, the findings could inform the development of future interventions.
The study is based on  data collected by the
National Violent Death Reporting System
(NVDRS), a CDC program which compiles info regarding violent deaths in the United States. This program contains a lot unstructured data, and the Viterbi and Dworak-Peck team’s goal was to organize and analyze this data to help understand when suicides may occur.
The 10% No One Was Looking For
The team was able to find that of the 200,000+ cases in the NVDRS, about 10% of victims had either an implicit or explicit legal interaction before they died by suicide.  This opens up potential opportunities for research into building suicide interventions for legal professionals, especially for clients not getting mental health care.
Researching a sensitive subject like this requires a careful balance of human and AI-driven research analysis. The team settled on a system that featured a first round of human-driven research into a small sample of individual cases to find inferred examples of legal interaction. AI was then implemented based on their research, in order to prevent emotional harm to these researchers and to help sift through this massive pile of data. In other words: this was a three-phase process, going from wholly manual research to human-assisted AI research and finally to unassisted AI research.
“We also did not recommend that anybody on our team look at too many of these cases every single day, and we had checks and balances in place to prevent emotional harms that the study itself might cause to students,” Swayamdipta said. “So we went at a slower pace, but we were manually looking for these indicators of legal interaction in the reports. Once we had that set, then we could use AI to do the same task and then cross check how well it does against our own manual effort.”
This balance between AI and human checks maintained the safety of student researchers while staying focused on its central goal: identifying the link between legal interactions and those who died by suicide. With this efficient and safe system of data analysis, this study could be just the beginning.
“We’re just scratching the surface,” Swayamdipta said. “There is a lot more to do here with the help of AI.”
Published on April 21st, 2026
This article may feature some AI-assisted content for clarity, consistency, and to help explore complex scientific concepts with greater depth and creative range.
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