There have been increasing calls within HCI to build sustained partnerships with communities that go beyond surface-level engagement. However, little is known about how communities view such partnerships and their outcomes. In collaboration with a community-based organization, we co-analyzed a series of interviews to understand the impacts of university-led research initiatives and publicly deployed technologies on local communities, and to explore strategies for more equitable community-university partnerships. Our findings reveal that local communities often perceive technology companies and academic institutions as potential threats due to their shared role in a series of projects, including predictive policing, surveillance, and broader concerns on technological bias and exclusion against minoritized groups. While interviewees named material benefits, sustained relationships, and meaningful accountability as desirable from universities, they pointed to academia's institutional priorities that pose barriers to forming effective partnerships. Drawing from la paperson's concept of a Third University, we argue that researchers and academic institutions must contend with these complexities, while taking a decolonizing approach to community-university partnerships through the lens of revestment.

Generative AI platforms and features are permeating many aspects of work. Entrepreneurs from lean economies in particular are well positioned to outsource tasks to generative AI given limited resources. In this paper, we work to address a growing disparity in use of these technologies by building on a four-year partnership with a local entrepreneurial hub dedicated to equity in tech and entrepreneurship. Together, we co-designed an interactive workshops series aimed to onboard local entrepreneurs to generative AI platforms. Alongside four community-driven and iterative workshops with entrepreneurs across five months, we conducted interviews with 15 local entrepreneurs and community providers. We detail the importance of communal and supportive exposure to generative AI tools for local entrepreneurs, scaffolding actionable use (and supporting non-use), demystifying generative AI technologies by emphasizing entrepreneurial power, while simultaneously deconstructing the veneer of simplicity to address the many operational skills needed for successful application.

The AI Ethics community faces an imperative to empower stakeholders and impacted community members so that they can scrutinize and influence the design, development, and use of AI systems in high-stakes domains. While a growing chorus of recent papers has kindled interest in so-called “participatory ML” methods, precisely what form participation ought to take and how to operationalize these ambitions are seldom addressed. Our survey of the relevant literature shows that in many papers, participation is reduced to highly structured, computational mechanisms designed to elicit mathematically tractable approximations of narrowly-defined moral values. Of papers that actually engage with real people, these engagements typically consist of one-time interactions with individuals that are often unrepresentative of the relevant stakeholders. Motivated by these clear limitations, we introduce a consolidated set of axes to evaluate and improve participatory approaches. We use these axes to analyze contemporary work in this space and outline future AI research directions that could meaningfully contribute to operationalizing the ideal of participation.

Even entrepreneurs whose businesses are not technological (e.g., handmade goods) need to be able to use a wide range of computing technologies in order to achieve their business goals. In this paper, we follow a participatory action research approach and collaborate with various stakeholders at an entrepreneurial co-working space to design “Tech Help Desk”, an on-going technical service for entrepreneurs. Our model for technical assistance is strategic, in how it is designed to fit the context of local entrepreneurs, and responsive, in how it prioritizes emergent needs. From our engagements with 19 entrepreneurs and support personnel, we reflect on the challenges with existing technology support for non-technological entrepreneurs. Our work highlights the importance of ensuring technological support services can adapt based on entrepreneurs’ ever-evolving priorities, preferences and constraints. Furthermore, we find technological support services should maintain broad technical support for entrepreneurs’ long tail of computing challenges.

This article establishes and addresses opportunities for ethics integration into Machine-learning (ML) courses. Following a survey of the history of computing ethics and the current need for ethical consideration within ML, we consider the current state of ML ethics education via an exploratory analysis of course syllabi in computing programs. The results reveal that though ethics is part of the overall educational landscape in these programs, it is not frequently a part of core technical ML courses. To help address this gap, we offer a preliminary framework, developed via a systematic literature review, of relevant ethics questions that should be addressed within an ML project. A pilot study with 85 students confirms that this framework helped them identify and articulate key ethical considerations within their ML projects. Building from this work, we also provide three example ML course modules that bring ethical thinking directly into learning core ML content. Collectively, this research demonstrates: (1) the need for ethics to be taught as integrated within ML coursework, (2) a structured set of questions useful for identifying and addressing potential issues within an ML project, and (3) novel course models that provide examples for how to practically teach ML ethics without sacrificing core course content. An additional by-product of this research is the collection and integration of recent publications in the emerging field of ML ethics education.

Devising strategies to engage the public in discussions around the design and development of technology is critical to building a future that works for everyone. This paper presents a novel case study, an immersive theater experience, "Quantified Self," that combines aspects of design fiction and user enactments to construct a public engagement opportunity about technology ethics. Our audience supplied their social data (Facebook, Twitter...) and received a personalized experience where they interacted with a narrative and technology exhibits. We used a design model targeting goals of engagement, education, and discussion. Here we overview the design and production of Quantified Self and report on the results (240 participants over 6 performances) and findings from audience surveys (n=179/240) and cast/crew interviews (n=15/22). We found our approach attracted a wide audience interested in different elements of the show. Affordances and challenges of our model are discussed in detail.

One contributing factor to how people choose to use technology is their perceptions of associated risk. In order to explore this influence, we adapted a survey instrument from risk perception literature to assess mental models of users and technologists around risks of emerging, data-driven technologies (e.g., identity theft, personalized filter bubbles). We surveyed 175 individuals for comparative and individual assessments of risk, including characterizations using psychological factors. We report our observations around group differences (e.g., expert versus non-expert) in how people assess risk, and what factors may structure their conceptions of technological harm. Our findings suggest that technologists see these risks as posing a bigger threat to society than do non-experts. Moreover, across groups, participants did not see technological risks as voluntarily assumed. Differences in how people characterize risk have implications for the future of design, decision-making, and public communications, which we discuss through a lens we call risk-sensitive design.

Industry demand for novel forms of personalization and audience targeting paired with research trends in affective computing and emotion detection puts us on a clear path toward emotion-sensitive technologies. Written as API documentation for an AI marketing solution that provides "emotion-sensitive marketing decisions," this design fiction presents one possible future application of today's research. Offering a demonstrable grey area in technology ethics, Ad Empathy should help to ground debates around fair use of data, and the boundaries of ethical design.

Our paper offers several novel activities for teaching ethics in the context of a computer science (CS) class. Rather than approaches that teach ethics as an isolated course, we outline and discuss multiple ethics education interventions meant to work in the context of an existing technical course. We piloted these activities in an Human Centered Computing course and found strong engagement and interest from our students in ethics topics without sacrificing core course material. Using a pre/post survey and examples from student assignments, we evaluate the impact of these interventions and discuss their relevance to other CS courses. We further make suggestions for embedding ethics in other CS education contexts.

This paper presents Quantified Self: Immersive Data and Theater Experience (QSelf) as a case study in collaborative and interdisciplinary learning and toward a project-based education model that promotes technical art projects. 22 students from several departments engaged in a semester-long effort to produce an immersive theater show centered on ethical uses of personal data, a show that drew more than 240 people over 6 performances. The project was housed out of the computer science department and involved multiple computer science undergraduate and graduate students who had the chance to work with students from the department of theater and dance. By analyzing the technical artifacts students created and post-interviews, we found this project created a novel and productive space for computer science students to gain applied experience and learn about the social impacts of their work while the arts students gained a fluency and understanding around the technical issues presented.

Traditionally, the inverted (or flipped) classroom has students complete traditional, passive learning tasks (e.g., watching lectures) while at home and uses class time to actualize what is learned through labs, discussions, and exercises. In this paper, we present an instructional model for teaching computer science (CS) that compounds features of the flipped classroom with components of peer instruction and formative assessment. Outside of class, in lieu of a lecture, students worked collaboratively on learning challenges that introduced content through a series of hands-on exercises. During class time, we used hack-a-thons to create an active classroom environment to promote peer coding and cultivate the growth of relevant real-world technical skills. Class work was digitally synced to Google Drive in real-time to allow instructors the opportunity to customize on-the-spot feedback. Further, we used journals as a formative assessment measure to synthesize student interests and opinions into our continued design of the class. In this paper, we describe our pedagogical model and discuss the results and lessons learned from the class using mined data from Google Drive and student journal responses.