Responsible AI (RAI) tools—checklists, templates, and governance processes—often engage RAI champions, individuals intrinsically motivated to advocate ethical practices, but fail to reach non-champions, who frequently dismiss them as bureaucratic tasks. To explore this gap, we shadowed meetings and interviewed data scientists at an organization, finding that practitioners perceived RAI as irrelevant to their work. Building on these insights and theoretical foundations, we derived design principles for engaging non-champions, and introduced sticky stories—narratives of unexpected ML harms designed to be concrete, severe, surprising, diverse, and relevant, unlike widely circulated media to which practitioners are desensitized. Using a compound AI system, we generated and evaluated sticky stories through human and LLM assessments at scale, confirming they embodied the intended qualities. In a study with 29 practitioners, we found that, compared to regular stories, sticky stories significantly increased time spent on harm identification, broadened the range of harms recognized, and fostered deeper reflection.
Online petitions are a central tool of digital advocacy, but often collapse into “slacktivism”—minimal engagement that rarely leads to meaningful change. We use Psychological Ownership (PO) as a design lens to evaluate how the sense of “mineness” can deepen participation into richer relational engagement with a cause. Across six Research through Design (RtD) cycles, we embedded a lightweight watch-and-record video module into petition flows to map existing affordances against PO routes and motives and identify opportunities for design—grounding the programme in PO-theoretical constraints, cycle-specific design trade-offs, and structured multi-stage evaluation sessions with 13 campaigners from seven countries. We then ran a five-arm between-subjects experiment (N=499) comparing petition conditions (Read, Sign, Write, Watch, Create). Our findings show how video affordances can be deliberately structured to transform one-off participation into deeper relational engagement, positioning PO as a transferable lens for reimagining civic technologies and moving activism from shallow clicks to more sustained care.
When conducting research in community-based settings, it is natural for conflict to arise as the practices, motivations, and imaginaries of researchers and local stakeholders converge. As designers, the question, then, is: ``In what ways can we engage with conflict to arrive at constructive outcomes?" In this paper, we employ ethnography and a mapping workshop with an environmental research group facing conflicts at a contested ecological site. We unpack some of the ways in which local conflicts over data, mapping, and technologies around community-managed forests are intertwined with broader socio-political, historical, and value-based contestations. We find that conflict serves as a critical site for negotiating community engagement and configuring collaboration. Accordingly, we provide strategies for surfacing, navigating, and staying with conflict in contested settings.
For community-based researchers, this requires resisting the natural tendency to seek an immediate resolution to the conflict, thereby creating room to deepen attachments to matters of concern.
Gig workers increasingly rely on unofficial tools and peer networks to navigate opaque, algorithmic labor systems. This paper investigates the case of Avalon—a paid batch-filtering app used by Instacart shoppers—and its affiliated Telegram community. Drawing on a two-year mixed-methods study including a survey (N=178), interviews (N=20), and 51,764 Telegram messages, we examine how workers resist platform constraints through technological and relational practices. While prior research often frames such resistance as adversarial or economically driven, we apply a feminist HCI lens to highlight care, consent, and infrastructuring as central to workers’ strategies. We show how Instacart’s majority-female workforce builds informal systems to reveal hidden information, protect one another, and maintain dignity in precarious conditions. Our findings contribute empirical insights into gendered algorithmic labor, theoretical extensions of feminist infrastructuring, and design implications for worker-centered platforms that reflect relational labor values. We argue for platforms that honor refusal, transparency, and collective agency from below.
Urban well-being (UWB) is increasingly recognized as a complex, multidimensional construct influenced by sociocultural and geographic contexts. Yet, existing approaches to assessing local perceptions of UWB often depend on standardized indicators, limiting sensitivity to context-specific priorities. This paper presents the Urban Well-Being Deck (UWBdeck), a participatory, card-based method designed to explore citizens’ situated perception of UWB. We conducted eight workshops across multiple cities, engaging 140 participants and collecting 256 participant-generated proposals. A thematic analysis of collected data revealed 22 distinct themes of UWB, highlighting both cross-city variation and underrepresented dimensions in existing indexes. This work contributes a participatory method for context-sensitive user research and offers empirical insights that advance ongoing discussion on subjective and objective dimensions of UWB.
Community engagement processes in representative political contexts, like school districts, generate massive volumes of feedback that overwhelm traditional synthesis methods, creating barriers to shared understanding not only between civic leaders and constituents but also among community members. To address these barriers, we developed StoryBuilder, a human-AI collaborative pipeline that transforms community input into accessible first-person narratives. Using 2,480 community responses from an ongoing school rezoning process, we generated 124 composite stories and deployed them through a mobile-friendly StorySharer interface. Our mixed-methods evaluation combined a four-month field deployment, user studies with 21 community members, and a controlled experiment examining how narrative composition affects participant reactions. Field results demonstrate that narratives helped community members relate across diverse perspectives. In the experiment, experience-grounded narratives generated greater respect and trust than opinion-heavy narratives. We contribute a human-AI narrative synthesis system and insights on its varied acceptance and effectiveness in a real-world civic context.
Host countries' infrastructures often challenge the legitimacy of immigrants' data from their home country, resulting in limiting their access to civic services. Drawing on interviews with 32 immigrants in Canada, we conceptualize Data Migration as the socio-technical, political, and infrastructural process in which immigrants make their data legitimate in their host country. Our findings show how credit bureaus, credential evaluators, healthcare databases, immigration portals, and other digital governance systems gatekeep immigrants’ civic rights and opportunities. We also highlight the crucial role of community-mediated informal networks in sustaining access where formal infrastructures fail. Using Ribot and Peluso’s Theory of Access, we demonstrate that systemic biases in policies and technical standards often privilege some immigrants while constraining others. Our work calls on HCI to critically examine migration governance and design data ecosystems that are equitable, pluralistic, and capable of supporting immigrant agency.