Millions of people now use non-clinical Large Language Model (LLM) tools like ChatGPT for mental well-being support. This paper investigates what it means to design such tools responsibly, and how to operationalize that responsibility in their design and evaluation. By interviewing experts and analyzing related regulations, we found that designing an LLM tool responsibly involves: (1) Articulating the specific benefits it guarantees and for whom. Does it guarantee specific, proven relief, like an over-the-counter drug, or offer minimal guarantees, like a nutritional supplement? (2) Specifying the LLM tool’s “active ingredients” for improving well-being and whether it guarantees their effective delivery (like a primary care provider) or not (like a yoga instructor). These specifications outline an LLM tool’s pertinent risks, appropriate evaluation metrics, and the respective responsibilities of LLM developers, tool designers, and users. These analogies—LLM tools as supplements, drugs, yoga instructors, and primary care providers—can scaffold further conversations about their responsible design.

Generative AI technologies are reshaping everyday environments by enabling multimodal interaction. As their ubiquity and agentic capacities grow, there is a pressing need to understand how these systems reshape human–computer interaction in relational, social, and systemic terms. We introduce a scenario-based design pack for investigating Human–GenAI relations. Grounded in assemblage theory and structured around a three-stage process—Prepare, Make, Reflect—the pack supports the prototyping, analysis, and critical reflection of emergent sociotechnical configurations. We evaluated the pack across three deployments: an ACM workshop (n=22), a multidisciplinary design session (n=20), and a university HCI class (n=260). Participants generated scenarios that surfaced relational issues of power, agency, visibility, and care. We contribute the design pack alongside an exploratory framework to advance relational enquiry into multimodal Human–GenAI relations, support more inclusive and socially responsive GenAI practices, and complement FATE approaches by grounding fairness, accountability, and transparency in lived, multimodal configurations.

Participatory machine learning (ML) encourages the inclusion of end users and people affected by ML systems in design and development processes. We interviewed 18 participation brokers—individuals who facilitate such inclusion and transform the products of participants’ labour into inputs for an ML artefact or system—across a range of organisational settings and project locations. Our findings demonstrate the inherent challenges of integrating messy contextual information generated through participation with the structured data formats required by ML workflows and the uneven power dynamics in project contexts. We advocate for evolution in the role of brokers to more equitably balance value generated in Participatory ML projects for design and development teams with value created for participants. To move beyond ‘fitting’ participation to existing processes and empower participants to envision alternative futures through ML, brokers must become educators and advocates for end users, while attending to frustration and dissent from indirect stakeholders.

HCI researchers have been gradually shifting attention from individual users to communities when engaging in research, design, and system development. However, our field has yet to establish a cohesive, systematic understanding of the challenges, benefits, and commitments of community-collaborative approaches to research. We conducted a systematic review and thematic analysis of 47 computing research papers discussing participatory research with communities for the development of technological artifacts and systems, published over the last two decades. From this review, we identified seven themes associated with the evolution of a project: from establishing community partnerships to sustaining results. Our findings suggest that several tensions characterize these projects, many of which relate to the power and position of researchers, and the computing research environment, relative to community partners. We discuss the implications of our findings and offer methodological proposals to guide HCI, and computing research more broadly, towards practices that center communities.