Enterprise AI Analysis
Water On My Block: Reflections on Building A Participatory Artificial Intelligence System For Precision Weather With Scientists and An Urban Community
Cities require precise, neighborhood-level weather data to combat extreme climate events like flooding and heat islands. Traditional city-scale AI models fall short. This paper introduces 'precision weather' – hyper-local climate AI powered by community engagement and data collection. We present a three-year case study involving climate scientists and an urban community, showcasing a participatory approach to co-designing AI systems that deliver tangible benefits.
Key Takeaways for Enterprise Leaders
Our analysis of "Water On My Block" highlights critical insights for organizations looking to implement AI solutions with a strong community-centric approach, emphasizing mutual benefit and actionable outcomes.
3
Years in Case Study
15
Scientists & Community Members Interviewed
62
Community Cafe Participants
94%
Chatham Residents Identified as Black
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Participatory AI as Sociotechnical Interface
Our project initially aimed to build a participatory AI system but evolved into creating a 'climate service' from the community's perspective. The Schedule Consultation app, named 'Water On My Block,' functioned as a boundary object, serving scientists' need for ground truth data and the community's need for actionable information and advocacy tools. True participation required us to relinquish preconceived technical outcomes, prioritizing community needs and benefits. Building a functional hyper-local climate AI pipeline demands significant time, dedicated relationship building, robust neighborhood infrastructure, and continuous attention to community benefit. Specifying what 'true participation' means for each stakeholder, including data ownership, requires ongoing dialogue and resource investment, going beyond mere feedback mechanisms.
Engaging Stakeholders with A Community Cafe Model
The 'Community Cafe' model proved effective for safe, productive stakeholder interaction, minimizing harm in underserved communities, and ensuring regular communication. These events are crucial but demand substantial planning and incentives for community members to attend and engage. The model highlighted participatory AI as a governance mechanism, redistributing decision-making authority. It revealed the diversity within community residents, each bringing unique histories and comfort levels. However, it also exposed the technical knowledge gap between scientists and communities, underscoring the need for 'data ambassador' programs to foster understanding and control over data and AI model outputs. Such programs, requiring their own time and investment, could bridge this gap and improve participation.
Community Data Partners For Participatory AI Pipelines
Our study advocates for 'community data partners' – groups at the community level that provide data, fostering a more authentic and impactful engagement than individual crowdsourcing. Key requirements for such partnerships include a direct, tangible benefit for the community, established trust, and clear data ownership and governance policies. The project revealed a 'sustainability paradox': achieving genuine public participation often clashes with prevailing funding models that rarely support the multi-year engagement truly necessary. This necessitates institutional changes in funding structures and recognition that these projects unfold over years, not weeks. Balancing democratic participation with respect for community time and labor, especially in marginalized communities, is paramount, recognizing existing power dynamics and avoiding 'parachute science'.
Enterprise Process Flow
Phase 1: Relationship Building
→
Phase 2: Interviews & Secondary Research
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Phase 3: Community Cafes
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Phase 4: App Development & Transfer
Quantifying the Impact of Community-Driven AI
Precision weather AI, supported by community insights, translates directly into measurable benefits for urban environments and the organizations serving them. Use our calculator to estimate potential efficiencies and resource savings.
Strategic Roadmap for Precision Weather AI Integration
Our phased approach ensures sustainable development and community integration of hyper-local climate AI. Each stage builds on strong partnerships and iterative feedback, culminating in impactful, actionable solutions.
01. Relationship Building & Trust (Sept 2022 - Ongoing)
Establishing strong foundational relationships with community organizations and scientists. This phase involved extensive participant observation and initial feedback gathering to understand diverse needs and build mutual trust.
02. In-depth Research & Needs Assessment (April 2023 - Dec 2023)
Conducting semi-structured interviews with scientists and community engagement specialists, coupled with secondary research, to deeply understand stakeholder goals, data requirements, and potential benefits/challenges of precision weather AI.
03. Co-design & Prototyping (Sept 2024 - Jan 2025)
Facilitating 'Community Cafes' – co-design workshops with Chatham residents. This iterative process involved storyboarding, mock-up evaluation, and feedback to refine the design of the 'Water On My Block' app for flood reporting and data advocacy.
04. App Development & Ownership Transfer (Dec 2024 - April 2025)
Developing the flood reporting app based on community feedback. Crucially, this phase included formal agreements for ownership transfer to the community partner (GCI), ensuring long-term sustainability and control over the data and tool.
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