https://issues-osu.github.io/ISSUESlab/tag/prevention/PreventionHugo Blox Builder (https://hugoblox.com)en-usWed, 22 Oct 2025 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/media/logo_hu2fedd529b9d9d7008e9f6968cd645fa1_199187_300x300_fit_lanczos_3.pnghttps://issues-osu.github.io/ISSUESlab/tag/prevention/https://issues-osu.github.io/ISSUESlab/presentations/ai-cps/Wed, 22 Oct 2025 00:00:00 +0000https://issues-osu.github.io/ISSUESlab/presentations/ai-cps/<p>This new article by <strong>Dr. Gia E. Barboza-Salerno</strong> argues that <strong>Predictive Risk Models (PRMs)</strong> can be effective when focused on <em>prevention</em>. 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.<br> The second emphasizes that PRMs, rather than serving solely as detection or risk assessment tools, can help <strong>identify structural risk factors</strong>, <strong>guide equitable public investment</strong>, and <strong>promote accountability</strong>, without expanding surveillance or punitive measures that compound systemic harm.</p> <p>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.</p> <p><strong>Read the full article here:</strong> <em>&ldquo;Barboza-Salerno, G. E. (2025). No bright line: How AI-driven technology blurs legal boundaries between prevention and punishment in family regulation. <em>Family Advocate, 47</em>(2), 00–00. American Bar Association.&rdquo;</em></p>