Academic Journal
AI-Augmented Prevention Science Needs Community-Engaged Prevention Science: a Framework for Greater Accountability.
| Τίτλος: | AI-Augmented Prevention Science Needs Community-Engaged Prevention Science: a Framework for Greater Accountability. |
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| Συγγραφείς: | Rice E; Center for AI in Society, University of Southern California, Los Angeles, CA, 90089, USA.; Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, 90089, USA., Young LE; Center for AI in Society, University of Southern California, Los Angeles, CA, 90089, USA. lindsay.young@usc.edu.; Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, CA, 90089, USA. lindsay.young@usc.edu. |
| Πηγή: | Prevention science : the official journal of the Society for Prevention Research [Prev Sci] 2026 Oct; Vol. 27 (7), pp. 1216-1226. Date of Electronic Publication: 2025 Sep 24. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Kluwer Academic/Plenum Publishers Country of Publication: United States NLM ID: 100894724 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-6695 (Electronic) Linking ISSN: 13894986 NLM ISO Abbreviation: Prev Sci Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: New York, NY : Kluwer Academic/Plenum Publishers, c2000- |
| Ιατρικοί όροι (MeSH): | Artificial Intelligence* , Community Participation* , Social Responsibility* , Preventive Medicine*, Humans ; California |
| Περίληψη: | With the ability to approximate human decision-making and to parse through large and complex data, artificial intelligence (AI) technologies such as machine learning algorithms stand to play important roles in diagnosing, treating, and preventing society's most pressing social, health, and academic challenges. However, some AI applications for these ends have resulted in biased and discriminatory outcomes, demonstrating poor accountability to the communities these technologies impact and serve. The foundational premise of this paper is that AI-augmented prevention science needs community engagement to mitigate these harms. Drawing on a decade of work conducted at the University of Southern California's Center for AI in Society, we present a model of community-engaged AI-augmented prevention science. What sets our formalization apart from previous frameworks for community-engaged AI research is its prevention science orientation. We highlight potential roles for community and AI at each stage of the prevention science life cycle, from problem conceptualization to intervention implementation, and delineate when community input can compel moments of critical retreat in that life cycle to remain accountable to community needs and values. We then illustrate how we integrated AI and community-engaged methods in our work to correct racial biases in a predictive risk algorithm used to prioritize vulnerable people experiencing homelessness for housing interventions. Our case study highlights two moments of critical retreat-informed by the values, experiences, and contextual knowledge of our community partners-that led to more equitable and inclusive outcomes. We conclude with recommendations for how to advance community-engaged approaches in AI prevention science research. (© 2025. The Author(s).) |
| Competing Interests: | Declarations. Ethics Approval: Research presented in this manuscript was approved by the Institutional Review Board of the University of Southern California. Consent to Participate: All participants in the Have You Heard study were informed and provided written consent. The Coordinated Entry System Triage Tool Research and Refinement study was not a human subject study. Conflict of interest: The authors declare no competing interests. |
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| Contributed Indexing: | Keywords: AI research; Artificial intelligence; Community-engaged research; Prevention science; Research framework |
| Entry Date(s): | Date Created: 20250924 Date Completed: 20260907 Latest Revision: 20260910 |
| Update Code: | 20260910 |
| PubMed Central ID: | PMC13550034 |
| DOI: | 10.1007/s11121-025-01842-7 |
| PMID: | 40993344 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1573-6695 |
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| DOI: | 10.1007/s11121-025-01842-7 |