community engagement

This study examines how urban data intermediaries respond to community advocates’ demands for environmental sustainability and equity, focusing on three core needs: responsible data use, contextual interpretation, and relational infrastructure building. Method: Drawing on in-depth interviews with 17 advocates and participatory discourse analysis, the study introduces an original “near-data/far-data” analytical framework that distinguishes data types along quality dimensions (e.g., situatedness, accountability, epistemic authority), and identifies three practice-oriented pathways: integrating data exploration with narrative co-creation; explicitly foregrounding context and uncertainty; and decentralizing data materialization to foster trust-based relationships—thereby advancing localized data feminism. Contribution/Results: The work yields actionable design principles for equitable data intermediation, exposes structural power tensions embedded in data practices, and proposes a justice-sensitive, operationally viable data governance paradigm for realizing the “right to the city.” Findings are widely cited in ACM FAT* and ISOC communities.

This study addresses persistent challenges in governing adolescent delinquency in Turin, Italy, by proposing a community-centered collaborative governance approach. Employing participatory design, it integrates knowledge from municipal authorities, law enforcement agencies, nonprofit organizations, and residents to co-develop the civic tool “Sbocciamo Torino,” comprising a data visualization dashboard, multi-tiered stakeholder advisory committees, and structured collaborative workshops. Its primary contribution lies in establishing an inclusive, iterative co-design framework that bridges institutional practices and lived community experiences—thereby enhancing cross-sectoral trust and data-informed decision-making capacity. Evaluation findings indicate strong stakeholder acceptance; however, critical barriers—including limited data literacy, persistent communication silos, and insufficient institutional support—were identified. Crucially, sustained political commitment and systematic institutional embedding were confirmed as essential prerequisites for long-term scalability and impact.

Existing policy design approaches are predominantly top-down and disconnected from community contexts, resulting in low legitimacy and misalignment between policy provisions and actual stakeholder needs. Method: This paper introduces PolicyCraft—a human-computer interaction (HCI)-informed system that embeds real-world cases as negotiation anchors within the policy design workflow. It supports participatory governance through case annotation, structured deliberation, multi-round voting, and versioned collaborative editing, enabling stakeholders to co-author, critique, and iteratively refine policy proposals while dynamically aligning abstract rules with concrete situational constraints. Contribution/Results: Evaluated in two university courses focused on educational policy, groups using PolicyCraft achieved significantly higher consensus and produced policies substantiated by richer, more relevant case evidence—outperforming a baseline system lacking case-based scaffolding. The work advances participatory policy design by grounding abstraction in lived experience and demonstrates scalable HCI methods for democratic, context-sensitive governance.

This work addresses the persistent invisibility and undervaluation of labor in data annotation, where annotators are often treated as interchangeable tools rather than skilled collaborators exercising professional judgment. Through multilingual, iterative, and participatory annotation workshops co-designed with journalists and activists engaged in narratives of gender-based violence, the project integrates feminist epistemologies into data practices. It introduces novel conceptual frameworks—such as “marginalized contextualization” and “strategic consensus”—to develop an annotation approach grounded in care, reflexivity, and cross-cultural collaboration. This methodology not only reconfigures power dynamics inherent in annotation processes but also reconceptualizes AI development as a political space that acknowledges difference, affirms the value of labor, and enacts solidarity.

Contemporary requirements engineering (RE) practices—such as CrowdRE and AI-assisted elicitation—often neglect cultural, social, and political contexts, limiting their capacity to address authentic human values and the needs of marginalized communities. Method: This paper introduces REConnect, a human-centered, participatory framework grounded in three core principles: relationship building, co-design, and user empowerment. It operationalizes these through REActions—concrete, iterative practices embedding sustained participation and relational thinking across the entire RE lifecycle. Within generative AI contexts, it repositions humans as value stewards, ethical collaborators, and curators of AI outputs. The framework integrates participatory design, collaborative modeling, qualitative inquiry, and contextual analysis. Contribution/Results: Validated in Nepal, Indigenous communities in Canada, and women’s shelters, REConnect yields culturally embedded, socially legitimate, and sustainable requirements, demonstrably enhancing technology’s substantive impact on marginalized groups.

This study examines how the federally mandated Homeless Management Information System (HMIS) data infrastructure, implemented within a context of compulsory interorganizational collaboration in U.S. homeless services, simultaneously enhances cross-agency coordination and reinforces power asymmetries. Drawing on in-depth interviews with six domain experts, the analysis reveals that while standardization improves collaborative efficiency and knowledge sharing, disparities in resources, analytical capacity, and institutional authority render less powerful participants prone to passive compliance, thereby diminishing their influence in decision-making. The findings demonstrate that mandatory data-sharing regimes can entrench structural inequities, offering critical theoretical insights and practical guidance for designing more equitable public data infrastructures.

This study addresses critical challenges in social impact data sharing—including the absence of standardized metadata, weak interoperability, and low engagement by official statistical agencies—by proposing and implementing an intelligent metadata framework for Social Impact Data Commons. Grounded in FAIR principles, the framework integrates structured metadata standards (e.g., DCAT, Schema.org) with semantic interoperability techniques to enable automated metadata generation, dynamic quality assessment, and cross-domain collaborative management. Empirical validation across multiple use cases demonstrates significant improvements in data discoverability, accessibility, interoperability, and reusability. Its core contribution is the first incorporation of “executable metadata” into a data commons architecture, establishing an evaluable and extensible standardization pathway. This enhances official statistical agencies’ understanding of and participation in innovative data initiatives, thereby providing FAIR-compliant infrastructure for social impact research.

This study addresses why some community-based collaborative initiatives foster enduring cooperation networks while others leave minimal impact on local social structures. It introduces the concept of “participatory architecture” and develops the Project Intervention Response Analysis (PIRA) framework, integrating anthropological fieldwork, social network analysis, and counterfactual modeling. A novel egocentric network metric—“architectural alter”—is proposed to identify the role of non-hierarchical collaborative mechanisms. Empirical findings from Pomerini, Tanzania, demonstrate that lightweight, highly visible participatory architectures are more effective than those reliant on charismatic central individuals in promoting sustainable collaboration. This work offers both theoretical and methodological innovations for the design of community interventions.

This study addresses the challenges confronting data curation practices in the Global South—including a scarcity of high-precision tools, insufficient cross-lingual resources, constrained local knowledge sharing, and postcolonial ethical tensions—through a six-month ethnographic fieldwork in Bangladesh, combining in-depth interviews and participant observation. It is the first to situate data curation within frameworks of global data justice and infrastructure studies, systematically analyzing how practitioners mobilize external resources such as large language models and international technical forums. The findings reveal that data curation pricing reflects not only technical labor but also embedded affective value, while exposing a structural tension between mechanisms for protecting local knowledge and dependencies on global technologies. This work offers a novel perspective on “data poverty” for human-computer interaction research and advances a more ethically attuned agenda for data justice.

Farming plays a significant role in the economy by supporting related industries such as food, retail, and local services. Community-based small farms, while offering unique social and cultural benefits, face persistent challenges, including limited access to formal education and underdeveloped infrastructure, which have been discussed in prior research. This study focuses on community-driven factors, such as workarounds for recording critical information and practices for passing down farming knowledge across generations. Through 11 semi-structured interviews with farmers from a small ethnic community, the Hmong, we explore how bonding social capital, rooted in close family and community ties, supports informal knowledge exchange and creates pathways to bridging and linking capital. These relationships help farmers connect to broader networks, resources, and institutions. Our findings highlight opportunities for designing technologies that support and strengthen existing support systems. We discuss how technologies should be designed to reflect the cultural values, unique practices, and intergenerational relationships embedded in community-based farms.