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.

The under-reporting of public safety incidents is a long-standing issue. In this paper, we propose a computational cognitive modeling approach to understand and design a mobile crowdsourcing system for improving campus safety reporting. In particular, we adopt drift-diffusion models (DDMs) from cognitive psychology to investigate the effect of various factors on users' reporting tendency for public safety. Our lab experiment and online study show consistent results on how location context impacts people's reporting decisions. This finding informs the design of a novel location-based nudge mechanism, which is tested in another lab experiment with 84 participants and proved to be effective in changing users' reporting decisions. Our follow-up interview study further suggests that the influence of people's mobility patterns (e.g., expected walking distance) could explain why the nudge design is effective. Our work not only informs the design of mobile crowdsourcing for public safety reporting but also demonstrates the value of applying a computational cognitive modeling approach to address HCI research questions more broadly.

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Academic scholars have leveraged crowd work platforms such as MTurk to conduct research and collect data. Though prior studies have discussed data quality and validity issues in crowd work via surveys and experiments, they kind of neglected to explore the scholars' and particularly the IRB's ethical concerns in these respects. In this study, we interviewed 17 scholars from six disciplines and 15 IRB directors and analysts in the U.S. to fill this research gap. We identified common themes among our respondents but also discovered distinctive and even opposing views regarding the approval rate, rejection, internal and external research validity. Based on the findings, we discussed a potential Tragedy of the Commons regarding the data quality deterioration and the disciplinary differences regarding validity in crowd work-based research. Finally, we advocated that the IRB's ethical concerns in crowd work-based research should be heard and respected further.

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A number of papers have surveyed mobile crowdsourcing systems and, to a lesser extent, situated crowdsourcing systems. These surveys have either contributed a comprehensive taxonomy of the diverse application domains where the systems have tapped into or characterized different components of the system platforms. In this paper, we present a survey of mobile and situated crowdsourcing systems by addressing fundamental questions about user contributions that system designers pose when building new systems or evaluating existing ones. We select and analyze 40 mobile and situated crowdsourcing systems that had prototypes deployed and studied in the real world. Inspired by the MIT's genetic model of collective intelligence, we propose our genetic model and new genes for mobile and situated crowdsourcing systems by examining user contributions to the selected systems. We present the observed patterns of different genes and discuss how the model can be used to create new applications and design ideas. We also provide examples to illustrate how the unique genes inspire new design suggestions for encouraging user contributions in the context of mobile and situated crowdsourcing by reviewing the existing design principles of online communities.

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