Enterprise AI Analysis
Seeing Like a Community: Public Perceptions of Data Use in Government
Eunmi (Ellie) Jeong, Information School, University of Wisconsin-Madison
Srijan Pandey, The Information School, University of Wisconsin-Madison
Corey Jackson, The Information School, University of Wisconsin-Madison
Kaiping Chen, University of Wisconsin-Madison
Data-driven policymaking has become central in public administration, leveraging datasets to optimize resource allocation and service delivery. Yet this trend raises critical questions about equity, representation, and the inclusion of marginalized communities in data governance. This paper examines the intersection of bureaucratic frameworks, data systems, and community needs, with a focus on disadvantaged groups. Drawing on a nationally representative survey (N = 754) and computational text analysis, we show that low-income respondents and residents of disadvantaged communities are more skeptical of data reliability and transparency, and place greater emphasis on community voice and ethical safeguards than their more advantaged counterparts. Our contribution lies in integrating intersectionality and place-based justice with HCI theories of data governance. We conclude with design recommendations for civic technologies and participatory data infrastructures that create accessible platforms, embed feedback loops, and support co-governance models fostering transparency, trust, and accountability.
Executive Impact
This research outlines critical areas where AI implementation in public administration can be optimized for equitable and effective outcomes.
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Enhanced Community Trust
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Public Perceptions & Trust
Understanding the attitudes of marginalized and vulnerable communities towards data use in public administration is crucial for building trust and ensuring equitable outcomes. This section highlights key areas of concern and differing perspectives.
Skepticism & Trust Gap
Disadvantaged groups exhibit higher skepticism regarding data reliability and transparency.
| Group | Key Priorities |
|---|---|
| Marginalized Communities |
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| Advantaged Counterparts |
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Intersectional Data Justice
Data justice requires addressing how overlapping identities and geographic disadvantages influence individuals' experiences and perceptions of data governance, ensuring policies account for systemic inequities.
Intersectional Concern Amplification
Concerns intensify significantly at the intersection of geography, income, and race.
Enterprise Process Flow (Ideal)
Inclusive Data Collection
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Transparent Analysis
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Community-Informed Decision
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Accountable Implementation
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Continuous Feedback Loop
Participatory Governance Design
Designing civic technologies and data infrastructures that are accessible, embed feedback loops, and support co-governance models can foster transparency, trust, and accountability for all communities.
Case Study: Barcelona's Gender Equity Task Force
The Gender Equity Task Force in Barcelona utilized intersectional frameworks to analyze urban data and address inequalities in public transportation. This initiative revealed how women, particularly those from low-income or immigrant backgrounds, faced disproportionate barriers to mobility due to safety concerns and inadequate access to transit routes. By incorporating these findings, the city restructured its transit system to prioritize safety and accessibility for underserved groups [20].
Need for Feedback Loops
Disadvantaged communities prioritize seeing tangible results from their input, signaling a need for robust feedback mechanisms.
Project ROI Calculator
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Our AI Implementation Roadmap
A structured approach to ensure a smooth transition and maximum impact for your public administration initiatives.
Phase 1: Discovery & Strategy
Conduct in-depth analysis of existing data practices, bureaucratic frames, and community needs. Define clear objectives for equitable data governance and AI integration.
Phase 2: Co-Design & Pilot
Engage marginalized communities in co-designing participatory data infrastructures and civic technologies. Develop pilot programs with feedback loops to test and refine solutions.
Phase 3: Implementation & Scaling
Deploy robust, transparent, and accountable AI systems. Scale successful pilot programs, ensuring ongoing community involvement and ethical oversight.
Phase 4: Monitoring & Adaptation
Continuously monitor AI system performance, biases, and community trust. Adapt governance frameworks and technologies based on real-world impact and evolving community needs.
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