AI for Better-Informed Decisions
Using AI for Better-Informed Decisions🧠
A mini paper for community partners and program leads
Purpose and mission
Leverage modern AI tools to support communities and partners with data-to-action workflows—moving beyond dashboards toward earlier interventions and stronger, trust-based partnerships. Keep humans meaningfully in the loop for sensitive decisions, and publish transparency notes to invite community feedback.
Integration roadmap
- Phase 1 — Discover & design: Map outcomes, metrics, and constraints. Identify data sources and consent pathways.
- Phase 2 — Foundations (MVP): Stand up basic data flows, dashboards, and a pilot matching workflow.
- Phase 3 — Predictive decision support: Add pattern recognition, propensity models, and confidence bands for recommendations.
- Phase 4 — Scale & governance: Document safeguards, bias checks, role permissions, and maintenance plans.
Tools and layers
- Free sandbox tiers: Use no/low-cost environments for experiments and learning sprints.
- Pattern recognition: Identify signals in engagement, stress, or access to services.
- Propensity models: Estimate the likelihood that a person or cohort benefits from a program.
- Decision dashboards: Show recommended interventions with confidence ranges and “why” notes.
Interactive activities
- Case study: Rising stress signals in a neighborhood cohort—trace inputs, interventions, and outcomes.
- Mini lab: Build a simple matching model that routes participants to the most relevant program.
- Ethics circle: Discuss bias, consent, community safeguards, and transparency notes before deployment.
Key principles
- Humans in the loop: Require human review for sensitive or high-impact decisions.
- Tie to real metrics: Measure reach, engagement, and equity—not just model accuracy.
- Transparency by design: Publish what data is used, for what purpose, and known limitations.
- Story + metric: Close each cycle with one community story and one improved metric.
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