Despite the expanding focus on ethics within computing curricula, research ethics – especially around the use of digital data about people – remains under-emphasized. Many computing students, faculty, and researchers report receiving little formal guidance on how to navigate the ethical complexities of collecting, analyzing, and sharing pervasive data such as public social media content, geolocation traces, or behavioral data from smart devices. To address this gap, we will demonstrate the PERVADE Data Ethics Decision Support Tool, a free, browser-based, interactive resource developed by a team of academic researchers as part of the NSF-funded PERVADE (Pervasive Data Ethics for Computational Research) project.

The rapid expansion of data science programs across a wide range of academic disciplines - including computer science, engineering, business, and other applied data domains - presents a challenge for standardizing curricula in line with established competencies. This paper critically examines whether university data science programs are aligned with the ACM Competencies for Undergraduate Data Science Curricula. Using a systematic review of 788 data science program offerings and 9,322 course titles, we assess levels of alignment with ACM's eleven competency areas. Additionally, we evaluate the inclusion of additional common skills course offerings, such as math/statistics, data analytics, and capstone courses. Our findings highlight significant variability in programs' adherence to the ACM competencies. This underscores the need for greater interdisciplinary collaboration towards integrating computing, statistics, and domain-specific coursework into the broad range of data science curricula, ensuring that data science graduates have a well-rounded, interdisciplinary skill set suited to the diverse applications of data science.

As ethical concerns continue to arise around the design of digital technologies, there is an increasing need to incorporate ethics education into project and capstone courses. While students frequently receive instruction in ethical theory and philosophy, they also need practical tools and strategies that they can apply to their class projects and carry with them into their careers. For example, ethical toolkits can provide frameworks and methods for identifying, discussing, and prioritizing ethical concerns in technology design and development. In this session we will invite participants to share their experiences with incorporating ethics into project-based learning. Participants will be able to review and discuss existing toolkits and methods for incorporating ethics into project courses.

Smart devices--including speakers, cameras, TVs, and sensors--are increasingly used to enhance comfort, efficiency, security, and entertainment in and outside the home. These devices collect significant audio, video, and environmental data, and that data may be shared with device owners, manufacturers, and/or third-parties. Social computing researchers have devoted significant attention to the privacy risks posed by these devices, although the focus has largely been on primary users who have access and control of devices and data. More recently, researchers have begun examining the privacy risks to incidental users --those who do not own or manage devices but still have their data collected due to their presence in a smart environment. In this paper, we present findings from 25 interviews with US-based adults who have encountered and/or used smart devices as incidental users in rental properties, as part of their work, at friends' and family's houses, and in shared living environments. We explore their reactions to these devices across different contexts, as well as their expectations for how devices should be disclosed and managed by primary users. We found a prevailing sense of resignation across our participants, as well behavioral and environmental modifications in response to smart devices. At the same time, participants were disinterested in gaining access and control to other people's devices, and they were hesitant or uncomfortable with the idea of initiating communication with device owners regarding their concern. Many wanted clearer and more prominent disclosures about device and data management that shifted responsibility from the incidental user to the device owner. Based on our findings, we argue for researchers to focus more closely on sociotechnical approaches to help incidental users navigate privacy risks in smart environments. Such approaches must account for interpersonal factors like trust, power dynamics, and social norms in addition to the many technical solutions that have been introduced in recent years.

We want to facilitate an interactive gathering of faculty from a variety of disciplines and institutions - including primary, secondary, and tertiary schools - who are interested in exchanging ideas related to the learning and teaching of ethical concepts and skills in a CS or related curriculum, and evaluating the success of those efforts. We understand "curriculum" broadly to include both course and lab instruction and out-of-class experiences. Participants will leave with a set of open-access teaching resources, guidance for how best to use those resources at their own institutions, and ideas for evaluating any courses, curriculum, or programs they develop as a result.

Reddit is one of the world's most prominent social media platforms, and also a valuable source of data for internet researchers. However, working with this kind of data also presents novel ethical complications for researchers, including issues around privacy, vulnerable populations, and unintended consequences. This paper describes an analysis of 134 papers that rely on Reddit data while also including some discussion of ethical implications and/or considerations by the researchers. Our analysis of these papers reveals common ethical issues and ethically motivated methodological decisions, as described by the researchers themselves, while also exposing some gaps for further ethical contemplation for researchers relying on Reddit data. Based on these findings, we close with a set of recommendations for ethically-informed methods and reflection for researchers working with social data.

Privacy is an important topic in HCI and social computing research, and the theory of contextual integrity (CI) is increasingly used to understand how sociotechnical systems-and the new kinds of information flows they introduce-can violate privacy. In empirical research, CI can serve as a conceptual framework for explaining the contextual nature of privacy as well as an analytical framework for evaluating privacy attitudes and behaviors. Analytical applications of CI in HCI primarily employ quantitative methods to identify appropriate information flows but rarely engage with the full CI framework to evaluate such flows. In this paper, we present a roadmap to guide HCI and social computing researchers on how to apply the full CI framework to qualitative projects. To help researchers envision what such an analysis can look like, each step includes an example analysis using interview data from projects on privacy and fitness tracking. We conclude by discussing how harnessing the full CI framework can address critiques of CI and identify opportunities for further theory development.

Femtech, a growing sector in mobile healthcare technology, caters to women's needs across various life stages with digital solutions like period tracking and pregnancy management apps. Maintaining robust data privacy is crucial due to the sensitive nature of the information involved, such as menstrual cycles and pregnancy status. Our research analyzes user feedback from platforms like the Apple App Store and Google Play Store to understand perceptions of femtech apps, covering accessibility, interface, features, and privacy concerns. Understanding user perspectives helps developers enhance usability and trust, driving further adoption. Prioritizing privacy also fosters industry advancement. This paper stresses the importance of dialogue among developers, users, and policymakers in femtech. Our findings aim to facilitate positive change within the femtech sector, leading to more inclusive, user-centric, and ethically driven advancements, benefiting both the industry and its users.

Data Science is a growing, interdisciplinary field that intersects with multiple academic, professional, and industry domains. Universities are increasingly offering data science programs across a wide variety of departments and program areas. It remains unclear, however, whether any standardization of missions, learning outcomes, or course offerings have emerged. Our research provides a systematic review of 788 data science program offerings, focusing on how these programs frame their missions and objectives; the types of courses offered within their programs; and whether issues of the ethical and social dimensions of data are addressed.

Fueled by Internet-of-Things technologies and spanning a wide range of sensors, speakers, and cameras, smart homes promise to make our lives easier and automate routine tasks. From speakers to security cameras, smart home devices (SHDs) answer our questions, monitor our home environment, and conserve energy. They also collect significant data, ranging from on/off commands to audio and video data, and they do this in some of our most private spaces. In this paper, we explore the privacy risks associated with SHDs by focusing on privacy-conscious smart home power users--those who spend significant time and money to research, install, and integrate devices throughout their homes and engage in advanced device and network management strategies to mitigate privacy concerns. Drawing on data from 10 focus groups with 32 privacy-conscious power users, we identify the key privacy risks they perceive from this technology, as well as how they mitigate those risks through increasingly complex strategies. Our findings reveal that navigating the technical landscape that makes up the smart home environment--including what data is collected, what options are available for managing or restricting data flows, and who has access to data collected by SHDs--is complex and often confusing, even for people who spend significant time researching devices and integration options. We use these findings to argue for further development of tools that are transparent, easy to use, and aligned with the privacy needs of a diverse userbase.

Many research communities routinely conduct activities that fall outside the bounds of traditional human subjects research, yet still frequently rely on the determinations of institutional review boards (IRBs) or similar regulatory bodies to scope ethical decision-making. Presented as a U.S. university-based fictional memo describing a post-hoc IRB review of a research study about social media and public health, this design fiction draws inspiration from current debates and uncertainties in the HCI and social computing communities around issues such as the use of public data, privacy, open science, and unintended consequences, in order to highlight the limitations of regulatory bodies as arbiters of ethics and the importance of forward-thinking ethical considerations from researchers and research communities.

In this position article, we synthesize various knowledge gaps in information privacy scholarship and propose a research agenda that promotes greater cross‐disciplinary collaboration within the iSchool community and beyond. We start by critically examining Westin's conceptualization of information privacy and argue for a contextual approach that holds promise for overcoming some of Westin's weaknesses. We then highlight three contextual considerations for studying privacy—digital networks, marginalized populations, and the global context—and close by discussing how these considerations advance privacy theorization and technology design.