Peer Reviewed

Data collection purposes and their descriptions are presented on almost all privacy notices under the GDPR, yet there is a lack of research focusing on how effective they are at informing users about data practices. We fill this gap by investigating users’ perceptions of data collection purposes and their descriptions, a crucial aspect of informed consent. We conducted 23 semi-structured interviews with European users to investigate user perceptions of six common purposes (Strictly Necessary, Statistics and Analytics, Performance and Functionality, Marketing and Advertising, Personalized Advertising, and Personalized Content) and identified elements of an effective purpose name and description.

Companies' privacy policies and their contents are being analyzed for many reasons, including to assess the readability, usability, and utility of privacy policies; to extract and analyze data practices of apps and websites; to assess compliance of companies with relevant laws and their own privacy policies, and to develop tools and machine learning models to summarize and read policies. Despite the importance and interest in studying privacy policies from researchers, regulators, and privacy activists, few best practices or approaches have emerged and infrastructure and tool support is scarce or scattered. In order to provide insight into how researchers study privacy policies and the challenges they face when doing so, we conducted 26 interviews with researchers from various disciplines who have conducted research on privacy policies. We provide insights on a range of challenges around policy selection, policy retrieval, and policy content analysis, as well as multiple overarching challenges researchers experienced across the research process. Based on our findings, we discuss opportunities to better facilitate privacy policy research, including research directions for methodologically advancing privacy policy analysis, potential structural changes around privacy policies, and avenues for fostering an interdisciplinary research community and maturing the field.

As Extended Reality (XR) devices and applications become more mainstream, so too will XR advertising — advertising that takes place in XR mediums. Due to the defining features of XR devices, such as the immersivity of the medium and the ability of XR devices to simulate reality, there are fears that these features could be exploited to create manipulative XR ads that trick consumers into buying products they do not need or might harm them. Using scenario construction, we investigate potential future incarnations of manipulative XR advertising and their harms. We identify five key mechanisms of manipulative XR advertising: misleading experience marketing; inducing artificial emotions in consumers; sensing and targeting people when they are vulnerable; emotional manipulation through hyperpersonalization; and distortion of reality. We discuss research challenges and questions in order to address and mitigate manipulative XR advertising risks.

Abstract Internet of Things and smart home technologies pose challenges for providing effective privacy controls to users, as smart devices lack both traditional screens and input interfaces. We investigate the potential for leveraging interpersonal communication cues as privacy controls in the IoT context, in particular for smart speakers. We propose privacy controls based on two kinds of interpersonal communication cues – gaze direction and voice volume level – that only selectively activate a smart speaker’s microphone or voice recognition when the device is being addressed, in order to avoid constant listening and speech recognition by the smart speaker microphones and reduce false device activation. We implement these privacy controls in a smart speaker prototype and assess their feasibility, usability and user perception in two lab studies. We find that privacy controls based on interpersonal communication cues are practical, do not impair the smart speaker’s functionality, and can be easily used by users to selectively mute the microphone. Based on our findings, we discuss insights regarding the use of interpersonal cues as privacy controls for smart speakers and other IoT devices.

Early warning dashboards in higher education analyze student data to enable early identification of underperforming students, allowing timely interventions by faculty and staff. To understand perceptions regarding the ethics and impact of such learning analytics applications, we conducted a multi-stakeholder analysis of an early-warning dashboard deployed at the University of Michigan through semi-structured interviews with the system's developers, academic advisors (the primary users), and students. We identify multiple tensions among and within the stakeholder groups, especially with regard to awareness, understanding, access and use of the system. Furthermore, ambiguity in data provenance and data quality result in differing levels of reliance and concerns about the system among academic advisors and students. While students see the system's benefits, they argue for more involvement, control, and informed consent regarding the use of student data. We discuss our findings' implications for the ethical design and deployment of learning analytics applications in higher education. Early warning dashboards in higher education analyze student data to enable early identification of underperforming students, allowing timely interventions by faculty and staff. To understand perceptions regarding the ethics and impact of such learning analytics applications, we conducted a multi-stakeholder analysis of an early-warning dashboard deployed at the University of Michigan through semi-structured interviews with the system's developers, academic advisors (the primary users), and students. We identify multiple tensions among and within the stakeholder groups, especially with regard to awareness, understanding, access, and use of the system. Furthermore, ambiguity in data provenance and data quality result in differing levels of reliance and concerns about the system among academic advisors and students. While students see the system's benefits, they argue for more involvement, control, and informed consent regarding the use of student data. We discuss our findings' implications for the ethical design and deployment of learning analytics applications in higher education.

Undocumented immigrants in the United States face risks of discrimination, surveillance, and deportation. We investigate their technology use, risk perceptions, and protective strategies relating to their vulnerability. Through semi-structured interviews with Latinx undocumented immigrants, we find that while participants act to address offline threats, this vigilance does not translate to their online activities. Their technology use is shaped by needs and benefits rather than risk perceptions. While our participants are concerned about identity theft and privacy generally, and some raise concerns about online harassment, their understanding of government surveillance risks is vague and met with resignation. We identify tensions among self-expression, group privacy, and self-censorship related to their immigration status, as well as strong trust in service providers. Our findings have implications for digital literacy education, privacy and security interfaces, and technology design in general. Even minor design decisions can substantially affect exposure risks and well-being for such vulnerable communities.