Algorithmic governancefood rescue logisticsParticipation documented
WeBuildAI
Pittsburgh, United States
Diverse stakeholders collaboratively designed food rescue matching rules and tradeoffs through workshops and pairwise ranking.
participatory governancecivic engagementproblem formulationmodel selectiondeployment_use_decisionsgovernance
- Region
- North America
- Lead organization
- 412 Food Rescue; Carnegie Mellon University; University of Texas at Austin
- Organization type
- nonprofit + university collaboration
- Technology group
- matching / optimization
- Activity status
- completed
- Start year
- 2019
- Last updated
- Mar 25, 2026
- Participation mode
- stakeholder workshops + pairwise ranking
- Participants
- donors; volunteers; recipient organizations; staff
- Methods
- interviews; workshops; pairwise ranking; rule specification
- AI lifecycle stages
- problem formulation; model selection; deployment_use_decisions; governance
- Evidence summary
- Diverse stakeholders collaboratively designed food rescue matching rules and tradeoffs through workshops and pairwise ranking.
- Atlas assessment
- Core · High confidence · evidence grade A
- Uncertainty
- Public evidence indicates that stakeholders directly shaped AI design, data, deployment, evaluation, or oversight; residual uncertainty concerns the exact degree of shared decision power rather than project existence.
- Participation group
- Co Design
- Technology description
- matching / optimization
- Funding
- Not documented
- Region of activity
- local
- Verification status
- Live Verified
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