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Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
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AI-Augmented Prevention Science Needs Community-Engaged Prevention Science: a Framework for Greater Accountability.

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Prevention Science : the Official Journal of the Society for Prevention Research
|September 24, 2025
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Summary
This summary is machine-generated.

Community engagement is crucial for artificial intelligence (AI) in prevention science to prevent biased outcomes. Integrating community input into AI development ensures accountability and more equitable results for affected populations.

Keywords:
AI researchArtificial intelligenceCommunity-engaged researchPrevention scienceResearch framework

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Area of Science:

  • Prevention Science
  • Artificial Intelligence
  • Community Engagement

Background:

  • Artificial intelligence (AI) offers potential for addressing societal challenges but can yield biased and discriminatory results.
  • Existing AI research frameworks often lack accountability to the communities impacted by these technologies.

Purpose of the Study:

  • To present a model of community-engaged AI-augmented prevention science.
  • To integrate community input throughout the prevention science lifecycle to mitigate AI-related harms.
  • To address racial biases in AI algorithms used for social interventions.

Main Methods:

  • Developed a prevention science framework for community-engaged AI research.
  • Highlighted roles for community and AI at each stage of the prevention science lifecycle.
  • Integrated AI and community-engaged methods to correct racial biases in a predictive risk algorithm for housing interventions.

Main Results:

  • The proposed model emphasizes community input for critical decision-making moments in AI development.
  • Case study demonstrated successful correction of racial biases in a predictive risk algorithm.
  • Community-informed "critical retreats" led to more equitable and inclusive intervention outcomes.

Conclusions:

  • Community-engaged approaches are essential for developing responsible and equitable AI in prevention science.
  • Future research should focus on advancing methods for integrating community values into AI.
  • AI-augmented prevention science must prioritize accountability to the communities it serves.