Creating accurate hyper-local climate Artificial Intelligence (AI) models requires neighborhood-level weather measurements and community partnerships. In this paper, we describe a three year case study of using a participatory approach to support the creation of hyper-local climate AI models, or what we term “precision weather.” Using participatory design to involve stakeholders in the climate AI pipeline design process i.e., “participatory AI,” we collaborated with a national laboratory and a community organization in a major metropolitan area in the United States, working with community members and scientists. We held interviews, co-design workshops (“Community Cafes”), and created an app for the community to collect flood reports in their neighborhood for advocacy and to contribute data to the AI model pipeline. We discuss our findings, lessons learned, and implications for future participatory projects to support hyper-local climate AI.

Communication technologies, from social media to video conferencing, are used by billions of people globally and contribute to shaping relationships between people. As these technologies become increasingly ubiquitous, the tech workers building them are increasingly making product decisions that can have far-reaching interpersonal ramifications. At the same time, few workplace tools and support exist to help tech workers understand and navigate these potential ramifications, and tech worker perspectives on such tools are not fully understood. In this work, we explore the needs, challenges, and opportunities encountered by tech workers in thinking through the interpersonal implications of their products. To do this, we ran a semi-structured interview study with 10 diverse tech workers. To ground the discussion, study participants interacted with a design probe prototype, InterAct, which provides research-grounded information about interpersonal implications of product features. Our findings suggest a desire by tech workers to consider the social implications of the technologies they build, and the potential for structured tooling to help provide the required knowledge and build organizational support. Based on these findings, we provide design considerations for creating future workplace tools to support thinking about the social implications of technologies.

With the rapid shift to remote learning in the early days of the COVID-19 pandemic, parents, teachers, and students had to quickly adapt to what scholars have called "emergency remote learning" (ERL). This transition required increased reliance on digital tools, exacerbating privacy and security threats associated with expanded data collection and new vulnerabilities. In this study, we adopt a sociotechnical and infrastructural perspective to understand how these threats emerged through breakdowns and tensions in elementary school ERL. Through interviews with 29 US-based teachers and parents of elementary school students (grades PreK-6), we identify two core findings related to privacy and security. First, we detail three breakdowns in the ERL sociotechnical infrastructure: (1) reduced attention to privacy and security issues as parents and teachers cobbled together a patchwork of tools needed to make ERL work; (2) privacy and security risks that emerged from ambiguous and shifting school policies; and (3) the failure to adapt standard authentication mechanisms (e.g., passwords) to be usable by young children. Second, we identify tensions between parents' and teachers' desire to help children advance in their education and their desire for children's privacy and security in ERL, as well as tensions resulting from the collapse of home and school contexts. These findings collectively suggest that ERL exacerbated existing--and created new--privacy and security challenges for young students, and we argue these challenges will carry beyond the pandemic due to the increasing use of technology to supplement traditional education. In light of these findings, we recommend researchers and educators use a framework of care to develop social and technical approaches to improving remote learning in order to protect children's privacy and security.

Machines, from artificially intelligent digital assistants to embodied robots, are becoming more pervasive in everyday life. Drawing on feminist science and technology studies (STS) perspectives, we demonstrate how machine designers are not just crafting neutral objects, but relationships between machines and humans that are entangled in human social issues such as gender and power dynamics. Thus, in order to create a more ethical and just future, the dominant assumptions currently underpinning the design of these human-machine relations must be challenged and reoriented toward relations of justice and inclusivity. This paper contributes the "social machine" as a model for technology designers who seek to recognize the importance, diversity and complexity of the social in their work, and to engage with the agential power of machines. In our model, the social machine is imagined as a potentially equitable relationship partner that has agency and as an "other" that is distinct from, yet related to, humans, objects, and animals. We critically examine and contrast our model with tendencies in robotics that consider robots as tools, human companions, animals or creatures, and/or slaves. In doing so, we demonstrate ingrained dominant assumptions about human-machine relations and reveal the challenges of radical thinking in the social machine design space. Finally, we present two design challenges based on non-anthropomorphic figuration and mutuality, and call for experimentation, unlearning dominant tendencies, and reimagining of sociotechnical futures.