Ai | ISSUES | The Ohio State University

No Bright Line: How AI-driven Technology Blurs Legal Boundaries Between Prevention and Punishment in Family Regulation

Wed, 22 Oct 2025 00:00:00 +0000

This new article by Dr. Gia E. Barboza-Salerno argues that Predictive Risk Models (PRMs) can be effective when focused on prevention. The argument is grounded in two core principles. The first is a rights-based balancing approach that extends beyond privacy concerns to encompass due process protections tied to legally recognized status.
The second emphasizes that PRMs, rather than serving solely as detection or risk assessment tools, can help identify structural risk factors, guide equitable public investment, and promote accountability, without expanding surveillance or punitive measures that compound systemic harm.

Together, these principles challenge the false dichotomy between prevention and punishment that underlies much of AI’s deployment in family regulation. By integrating a legal and ethical framework rooted in rights protection and structural accountability, the article redefines what responsible and justice-oriented AI looks like in the context of family welfare systems.

Read the full article here: “Barboza-Salerno, G. E. (2025). No bright line: How AI-driven technology blurs legal boundaries between prevention and punishment in family regulation. Family Advocate, 47(2), 00–00. American Bar Association.”

Improving surveillance of IPV-related head and neck injuries using AI-enhanced ED narratives

Mon, 02 Nov 2026 10:30:00 +0000

Session: Injury Epidemiology, Trauma Systems, and Health Care Utilization Posters
Program: Injury Control and Emergency Health Services
Date: Monday, November 2, 2026 | 10:30 AM – 11:30 AM

A Multilevel Analysis of Individual and Community Factors Associated With Case Dispositions Following Child Maltreatment Investigations

Tue, 18 Jun 2024 00:00:00 +0000

Plain-Language Summary

This study investigates how individual case characteristics and county-level social vulnerabilities influence the decisions made by child protective services (CPS) investigators when recommending services for families. Analyzing over 12,000 cases across New Mexico, the study looks at factors such as child age, abuse type, prior history, and county-level deprivation (e.g., housing and transportation deficits).

The results show that county-level characteristics significantly influence whether a family receives support services. Even when families present with similar risk levels, those living in counties with higher structural vulnerabilities are subject to different service disposition outcomes. The findings highlight the need for standardized decision-making tools to ensure equitable treatment and service distribution across different geographic regions.