Abstract How scholars and IRBs perceive and apply the Belmont principles in crowd work‐based research was an open and largely neglected question. As crowd work becomes increasingly popular for scholars to implement research and collect data, such negligence, signaling a lack of attention to the ethical issues in crowd work‐based research more broadly, seemed alarming. To fill this gap, we conducted a qualitative study with 32 scholars and IRB directors/analysts in the United States to inquire into their perceptions and applications of the Belmont principles in crowd work‐based research. We found two dilemmas in applying the Belmont principles in crowd work‐based research, namely the dilemma between the dehumanization and expected autonomy of crowd workers, and the dilemma between the monetary incentive/reputationall risks and the conventional notion of research benefits/risks. We also compared the scholars' and IRBs' ethical perspectives and proposed our research implications for future work.

Drones pose privacy concerns such as surveillance and stalking. Many technology-based or policy-based mechanisms have been proposed to mitigate these concerns. However, it is unclear how drone controllers and bystanders perceive these mechanisms and whether people intend to adopt them. In this paper, we report results from two rounds of online survey with 169 drone controllers and 717 bystanders in the U.S. We identified respondents' perceived pros and cons of eight privacy mechanisms. We found that owner registration and automatic face blurring individually received most support from both controllers and bystanders. Our respondents also suggested using varied combinations of mechanisms under different drone usage scenarios, highlighting their context-dependent preferences. We outline a set of important questions for future privacy designs and public policies of drones.

Prior research has discovered various privacy concerns that bystanders have about drones. However, little is known about drone controllers' privacy perceptions and practices of drones. Understanding controllers' perspective is important because it will inform whether controllers' current practices protect or infringe on bystanders' privacy and what mechanisms could be designed to better address the potential privacy issues of drones. In this paper, we report results from interviews of 12 drone controllers in the US. Our interviewees treated safety as their top priority but considered privacy issues of drones exaggerated. Our results also highlight many significant differences in how controllers and bystanders think about drone privacy, for instance, how they determine public vs. private spaces and whether notice and consent of bystanders are needed.

The human library is an event intended to engage members of the community in sharing and learning from each other's experiences, and is growing in popularity internationally. Human libraries fall within the larger scope of community knowledge sharing but have received little study and remain largely unsupported by technology. In this study, we examine how community libraries organize and host these events. We present how libraries have attempted to utilize technologies and leverage community support to enable human library events. Our findings reveal inconsistencies in the purpose of human library events, as well as technology applications that are not sufficient to support fully collaborative community knowledge building. We highlight opportunities for increased community participation and technological innovation and also suggest a broader consideration of computer-supported collaborative work in the context of human libraries and experience sharing.

Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) are widely used by organizations, researchers, and individuals to outsource a broad range of tasks to crowd workers. Prior research has shown that crowdsourcing can pose privacy risks (e.g., de-anonymization) to crowd workers. However, little is known about the specific privacy issues crowd workers have experienced and how they perceive the state of privacy in crowdsourcing. In this paper, we present results from an online survey of 435 MTurk crowd workers from the US, India, and other countries and areas. Our respondents reported different types of privacy concerns (e.g., data aggregation, profiling, scams), experiences of privacy losses (e.g., phishing, malware, stalking, targeted ads), and privacy expectations on MTurk (e.g., screening tasks). Respondents from multiple countries and areas reported experiences with the same privacy issues, suggesting that these problems may be endemic to the whole MTurk platform. We discuss challenges, high-level principles and concrete suggestions in protecting crowd workers'; privacy on MTurk and in crowdsourcing more broadly.

Online Behavioral Advertising (OBA), which involves tracking people's online behaviors, raises serious privacy concerns. We present results from a scenario-based online survey study on American and Chinese Internet users' privacy preferences of OBA. Since privacy is context-dependent, we investigated the effects of country (US vs. China), activity (e.g., online shopping vs. online banking), and platform (desktop/laptop vs. mobile app) on people's willingness to share their information for OBA. We found that American respondents were significantly less willing to share their data and had more specific concerns than their Chinese counterparts. We situate these differences in the broader historical, legal, and social scenes of these countries. We also found that respondents' OBA preferences varied significantly across different online activities, suggesting the potential of context-aware privacy tools for OBA. However, we did not find a significant effect of platform on people's OBA preferences. Lastly, we discuss design implications for privacy tools.

Abstract Drones are unmanned aircraft controlled remotely or operated autonomously. While the extant literature suggests that drones can in principle invade people’s privacy, little is known about how people actually think about drones. Drawing from a series of in-depth interviews conducted in the United States, we provide a novel and rich account of people’s privacy perceptions of drones for civilian uses both in general and under specific usage scenarios. Our informants raised both physical and information privacy issues against government, organization and individual use of drones. Informants’ reasoning about the acceptance of drone use was in part based on whether the drone is operating in a public or private space. However, our informants differed significantly in their definitions of public and private spaces. While our informants’ privacy concerns such as surveillance, data collection and sharing have been raised for other tracking technologies such as camera phones and closed-circuit television (CCTV), our interviews highlight two heightened issues of drones: (1) powerful yet inconspicuous data collection, (2) hidden and inaccessible drone controllers. These two aspects of drones render some of people’s existing privacy practices futile (e.g., notice recording and ask controllers to stop or delete the recording). Some informants demanded notifications of drones near them and expected drone controllers asking for their explicit permissions before recording. We discuss implications for future privacy-enhancing drone designs.

Current campus communication regarding safety-related issues can be improved for both efficiency and accessibility. We observed a unique opportunity to develop a mobile crowdsourcing system, which allows university community members to report safety related incidents to the campus police department and to share their reports with other users of the system. To better inform the design of such a system, we applied drift-diffusion models in cognitive psychology to model the effect of various factors on users' sharing tendency. We conducted a laboratory experiment with 30 participants. We also ran an MTurk study with 230 participants to explore the feature of anonymous sharing in the application design. In this paper we report various results, including the findings that the time of day, location, and type of crime each affects the likelihood and timeliness of sharing safety reports in several different ways. We also discuss the implications for design of mobile crowdsourcing systems for public safety in general.