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AI-Augmented Prevention Science Needs Community-Engaged Prevention Science: a Framework for Greater Accountability.

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Τίτλος: AI-Augmented Prevention Science Needs Community-Engaged Prevention Science: a Framework for Greater Accountability.
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.
References: Ann Intern Med. 2018 Dec 18;169(12):866-872. (PMID: 30508424)
J Biomed Inform. 2020 Aug;108:103500. (PMID: 32622833)
Annu Rev Public Health. 2008;29:325-50. (PMID: 18173388)
PLOS Digit Health. 2024 Aug 23;3(8):e0000561. (PMID: 39178307)
J Am Med Inform Assoc. 2024 Feb 16;31(3):746-761. (PMID: 38070173)
Prev Sci. 2019 Jan;20(1):1-9. (PMID: 30362085)
Nat Mach Intell. 2023 May;5(5):476-479. (PMID: 37600144)
J Ambient Intell Humaniz Comput. 2023 May 27;:1-13. (PMID: 37360776)
Transl Behav Med. 2016 Mar;6(1):115-24. (PMID: 27012259)
PLoS One. 2022 Sep 21;17(9):e0274998. (PMID: 36129944)
Am J Prev Med. 2011 May;40(5 Suppl 2):S154-8. (PMID: 21521589)
Patterns (N Y). 2022 Mar 11;3(3):100449. (PMID: 35510187)
Am Psychol. 1993 Oct;48(10):1013-22. (PMID: 8256874)
J Adolesc Health. 2020 Apr;66(4):408-415. (PMID: 32057607)
Public Health. 2022 Apr;205:14-25. (PMID: 35219838)
Vital Health Stat 2. 2024 Nov;(211):1-33. (PMID: 40576483)
J Acquir Immune Defic Syndr. 2021 Dec 15;88(S1):S20-S26. (PMID: 34757989)
Psychol Med. 2019 Jul;49(9):1426-1448. (PMID: 30744717)
Am J Community Psychol. 2024 Dec;74(3-4):262-268. (PMID: 39279135)
Sci Rep. 2022 Jan 10;12(1):328. (PMID: 35013370)
Annu Rev Public Health. 1998;19:173-202. (PMID: 9611617)
Science. 2019 Oct 25;366(6464):447-453. (PMID: 31649194)
Sci Rep. 2024 Mar 19;14(1):6616. (PMID: 38503818)
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
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  Data: 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.<br /> (© 2025. The Author(s).)
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  Data: 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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  Data: Ann Intern Med. 2018 Dec 18;169(12):866-872. (PMID: <searchLink fieldCode="PM" term="%2230508424%22">30508424)</searchLink><br />J Biomed Inform. 2020 Aug;108:103500. (PMID: <searchLink fieldCode="PM" term="%2232622833%22">32622833)</searchLink><br />Annu Rev Public Health. 2008;29:325-50. (PMID: <searchLink fieldCode="PM" term="%2218173388%22">18173388)</searchLink><br />PLOS Digit Health. 2024 Aug 23;3(8):e0000561. (PMID: <searchLink fieldCode="PM" term="%2239178307%22">39178307)</searchLink><br />J Am Med Inform Assoc. 2024 Feb 16;31(3):746-761. (PMID: <searchLink fieldCode="PM" term="%2238070173%22">38070173)</searchLink><br />Prev Sci. 2019 Jan;20(1):1-9. (PMID: <searchLink fieldCode="PM" term="%2230362085%22">30362085)</searchLink><br />Nat Mach Intell. 2023 May;5(5):476-479. (PMID: <searchLink fieldCode="PM" term="%2237600144%22">37600144)</searchLink><br />J Ambient Intell Humaniz Comput. 2023 May 27;:1-13. (PMID: <searchLink fieldCode="PM" term="%2237360776%22">37360776)</searchLink><br />Transl Behav Med. 2016 Mar;6(1):115-24. (PMID: <searchLink fieldCode="PM" term="%2227012259%22">27012259)</searchLink><br />PLoS One. 2022 Sep 21;17(9):e0274998. (PMID: <searchLink fieldCode="PM" term="%2236129944%22">36129944)</searchLink><br />Am J Prev Med. 2011 May;40(5 Suppl 2):S154-8. (PMID: <searchLink fieldCode="PM" term="%2221521589%22">21521589)</searchLink><br />Patterns (N Y). 2022 Mar 11;3(3):100449. (PMID: <searchLink fieldCode="PM" term="%2235510187%22">35510187)</searchLink><br />Am Psychol. 1993 Oct;48(10):1013-22. (PMID: <searchLink fieldCode="PM" term="%228256874%22">8256874)</searchLink><br />J Adolesc Health. 2020 Apr;66(4):408-415. (PMID: <searchLink fieldCode="PM" term="%2232057607%22">32057607)</searchLink><br />Public Health. 2022 Apr;205:14-25. (PMID: <searchLink fieldCode="PM" term="%2235219838%22">35219838)</searchLink><br />Vital Health Stat 2. 2024 Nov;(211):1-33. (PMID: <searchLink fieldCode="PM" term="%2240576483%22">40576483)</searchLink><br />J Acquir Immune Defic Syndr. 2021 Dec 15;88(S1):S20-S26. (PMID: <searchLink fieldCode="PM" term="%2234757989%22">34757989)</searchLink><br />Psychol Med. 2019 Jul;49(9):1426-1448. (PMID: <searchLink fieldCode="PM" term="%2230744717%22">30744717)</searchLink><br />Am J Community Psychol. 2024 Dec;74(3-4):262-268. (PMID: <searchLink fieldCode="PM" term="%2239279135%22">39279135)</searchLink><br />Sci Rep. 2022 Jan 10;12(1):328. (PMID: <searchLink fieldCode="PM" term="%2235013370%22">35013370)</searchLink><br />Annu Rev Public Health. 1998;19:173-202. (PMID: <searchLink fieldCode="PM" term="%229611617%22">9611617)</searchLink><br />Science. 2019 Oct 25;366(6464):447-453. (PMID: <searchLink fieldCode="PM" term="%2231649194%22">31649194)</searchLink><br />Sci Rep. 2024 Mar 19;14(1):6616. (PMID: <searchLink fieldCode="PM" term="%2238503818%22">38503818)</searchLink>
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