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.

The growing use of AI applications among freelance workers is reshaping trust and relationships with clients. This paper investigates how both workers and clients perceive AI use and disclosure in the freelance economy through a three-stage study: interviews with workers and two survey studies with workers and clients. Findings first reveal a key expectation gap around disclosure: Workers often adopt passive disclosure practices, revealing AI use only when asked, as they assume clients can already detect it. Clients, however, are far less confident in recognizing AI-assisted work and prefer proactive disclosure. A second finding highlights the role of unclear or absent client AI policies, which leave workers consistently misinterpreting clients’ expectations for AI use and disclosure. Together, these gaps point to the need for clearer guidelines and practices for AI disclosure. Insights extend beyond freelancing, offering implications for trust, accountability, and policy design in other AI-mediated work domains.

Large language models are increasingly used for both task-based assistance and social companionship, yet research has typically focused on one or the other. Drawing on a survey (N = 202) and 30 interviews with high-engagement ChatGPT and Replika users, we characterize digital companionship as an emerging form of human-AI relationship. With both systems, users were drawn to humanlike qualities, such as emotional resonance and personalized responses, and non-humanlike qualities, such as constant availability and inexhaustible tolerance. This led to fluid chatbot uses, such as Replika as a writing assistant and ChatGPT as an emotional confidant, despite their distinct branding. However, we observed challenging tensions in digital companionship dynamics: participants grappled with bounded personhood, forming deep attachments while denying chatbots “real” human qualities, and struggled to reconcile chatbot relationships with social norms. These dynamics raise questions for the design of digital companions and the rise of hybrid, general-purpose AI systems.

Designing public spaces requires balancing the interests of diverse stakeholders within a constrained physical and institutional space. Designers usually approach these problems through participatory methods but struggle to incorporate diverse perspectives into design outputs. The growing capabilities of image-generative artificial intelligence (IGAI) could support participatory design. Prior work in leveraging IGAI's capabilities in design has focused on augmenting the experience and performance of individual creators. We study how IGAI could facilitate participatory processes when designing public spaces, a complex collaborative task. We conducted workshops and IGAI-mediated interviews in a real-world participatory process to upgrade a park in Los Angeles. We found (1) a shift from focusing on accuracy to fostering richer conversations as the desirable outcome of adopting IGAI in participatory design, (2) that IGAI promoted more space-aware conversations, and (3) that IGAI-mediated conversations are subject to the abilities of the facilitators in managing the interaction between themselves, the AI, and stakeholders. We contribute by discussing practical implications for using IGAI in participatory design, including success metrics, relevant skills, and asymmetries between designers and stakeholders. We finish by proposing a series of open research questions.

Given the rising proliferation and diversity of AI writing assistance tools, especially those powered by large language models (LLMs), both writers and readers may have concerns about the impact of these tools on the authenticity of writing work. We examine whether and how writers want to preserve their authentic voice when co-writing with AI tools and whether personalization of AI writing support could help achieve this goal. We conducted semi-structured interviews with 19 professional writers, during which they co-wrote with both personalized and non-personalized AI writing-support tools. We supplemented writers' perspectives with opinions from 30 avid readers about the written work co-produced with AI collected through an online survey. Our findings illuminate conceptions of authenticity in human-AI co-creation, which focus more on the process and experience of constructing creators' authentic selves. While writers reacted positively to personalized AI writing tools, they believed the form of personalization needs to target writers' growth and go beyond the phase of text production. Overall, readers' responses showed less concern about human-AI co-writing. Readers could not distinguish AI-assisted work, personalized or not, from writers' solo-written work and showed positive attitudes toward writers experimenting with new technology for creative writing.

Safety-critical data, such as crash and near-crash records, are crucial to improving autonomous vehicle (AV) design and development. Sharing such data across AV companies, academic researchers, regulators, and the public can help make all AVs safer. However, AV companies rarely share safety-critical data externally. This paper aims to pinpoint why AV companies are reluctant to share safety-critical data, with an eye on how these barriers can inform new approaches to promote sharing. We interviewed twelve AV company employees who actively work with such data in their day-to-day work. Findings suggest two key, previously unknown barriers to data sharing: (1) Datasets inherently embed salient knowledge that is key to improving AV safety and are resource-intensive. Therefore, data sharing, even within a company, is fraught with politics. (2) Interviewees believed AV safety knowledge is private knowledge that brings competitive edges to their companies, rather than public knowledge for social good. We discuss the implications of these findings for incentivizing and enabling safety-critical AV data sharing, specifically, implications for new approaches to (1) debating and stratifying public and private AV safety knowledge, (2) innovating data tools and data sharing pipelines that enable easier sharing of public AV safety data and knowledge ; (3) offsetting costs of curating safety-critical data and incentivizing data sharing.

As generative artificial intelligence (genAI) applications gain popularity, there is a dearth of research examining how applications may transform social interactions. One possible application set to transform social interactions is the use of generative speech to power AI agents that can realistically represent people. Our work examines the potential implications of AI agents representing individuals in human conversations ("agent representation") as a way to begin filling this research gap. We take a multi-method approach, conducting formative interviews with developers, a co-design workshop with designers, a harm analysis among researchers, and interviews with the general public. Both technologists and potential users worry adopting agent representations might harm the quality, trust, and autonomy of human communication. Potential users are particularly concerned that agent representations could undermine the value of social interaction and threaten individuals’ ability to control their image. To avoid such potential consequences, future genAI-powered agents and speech applications should take into account user-defined red lines when considering applying these technologies in social settings.

From wearable health tracking to sensor-laden cities, AI-enhanced pervasive sensing platforms promise far-reaching benefits yet also introduce societal risks. How might designers of these platforms effectively navigate their complex ecology and sociotechnical dynamics? To explore this question, we interviewed designers building mental health technologies who undertook this challenge. They are hospital chief medical information officers and startup founders together striving to create new sensors/AI platforms and integrate them into the healthcare ecosystem. We found that, while all designers aspired to build comprehensive care platforms, their efforts focused on serving either consumers or physicians, delivering a subset of healthcare interventions, and demonstrating system effectiveness one metric at a time. Consequently, breakdowns in patient journeys are emerging; societal risks loom large. We describe how the data economy, designers’ mindsets, and evaluation challenges led to these unintended design consequences. We discuss implications for designing pervasive sensing and AI platforms for social good.

The present work explores the potential of leveraging a teamwork agent’s identity – signaled through its gendered voice – to support marginalized individuals in gender-imbalanced teams. In a mixed design experiment (N = 178), participants were randomly assigned to work with a female and a male voice agent in either a female-dominated or male-dominated team. Results show the presence of a same-gender voice agent is particularly beneficial to the performance of minority female members, such that they would contribute more ideas and talk more when a female agent was present. Conversely, minority male members became more talkative but were less focused on the teamwork tasks at hand when working with a male-sounding agent. The findings of the present experiment support existing literature on the effect of social presence in gender-imbalanced teams, such that gendered agents serve similar benefits as human teammates of the same gender identities. However, the effect of agents’ presence remains limited when participants have experienced severe marginalization in the past. Based on findings from the present study, we discuss relevant design implications and avenues for future research.

360-degree videos hold great potential as learning tools that can provide a sense of presence with instructors. However, much remains to be explored about how these videos should be designed. Across three within-subjects studies, we compared the effects of two different narrative styles, monologue and dialogue, on learning experiences in 360-degree videos filmed in an apple orchard. Study 1 presented the 360-degree videos using virtual reality (VR) with head-mounted displays. Study 2 presented the same content in web-based VR using computer screens. Replicating Study 2, Study 3 further examined users' interaction with the video content using on-screen mouse tracking. Across all three studies, participants preferred a monologue format, and also reported higher physical and social presence in this format. Furthermore, greater physical presence correlated with improved recall of informative content. This suggests that many of the benefits of 360-degree videos can be enjoyed even by students and teachers without access to VR headsets, providing more inclusive and accessible learning opportunities.

The present study investigates how human subjects collaborate with a computer-mediated chatbot in creative idea generation tasks. In three text-based between-group studies, we tested whether the perceived identity (i.e., whether a partner was believed to be a bot or as a human) or conversational style (human or robotic) of a teammate would moderate the outcomes of participants’ creative production. In Study 1, participants worked with either a chatbot or a human confederate. In Study 2, all participants worked with a human teammate but were informed that their partner was either a human or a chatbot. Conversely, all participants worked with a chatbot in Study 3, but their partner was described as either a chatbot or a human. We investigated differences in idea generation outcomes and found that participants consistently contributed more ideas and ideas of higher quality when they perceived their teamworking partner to be a bot. Furthermore, when the conversational style of the partner was robotic, participants with high anxiety in group communication reported greater creative self-efficacy in task performance. Finally, whether the perceived dominance of a partner and the pressure to come up with ideas during the task mediated positive outcomes of idea generation depended on whether the conversational style of the bot partner was robot- or human-like. Based on our findings, we discussed implications for future design of artificial agents as active team players in collaboration tasks.

In two studies, we investigate how users choose virtual backgrounds and how these backgrounds influence viewers' impressions. In Study 1, we created a web prototype allowing users to apply different virtual backgrounds to their camera views and asked users to select backgrounds that they believed would change viewers' perceptions of their personality traits. In Study 2, we then applied virtual backgrounds picked by participants in Study 1 to a subset of videos drawn from the First Impression Dataset. We then ran a series of three online experiments on Amazon Mechanical Turk (MTurk) to compare participants' personality trait ratings for subjects (1) with the selected virtual backgrounds, (2) with the original video backgrounds, and (3) with a gray screen as a background. The selected virtual backgrounds did not change the personality trait ratings in the intended direction. Instead, virtual background use of any kind results in a consistent "muting effect" that mitigates very high or low ratings (i.e., compressing ratings to the mean level) compared to the ratings of the video with the original background.