Rajan Vaish

Szu-Yu, Liu, Brian A. Smith et al.

In recent years, public discourse has blamed digital technologies for making people feel "alone together," distracting us from engaging with one another, even when we are interacting in-person. We argue that in order to design technologies that foster and augment co-located interactions, we need to first understand the context in which enjoyable co-located socialization takes place. We address this gap by surveying and interviewing over 1,000 U.S.-based participants to understand what, where, with whom, how, and why people enjoy spending time in-person. Our findings suggest that people enjoy engaging in everyday activities with individuals with whom they have strong social ties because it helps enable nonverbal cues, facilitate spontaneity, support authenticity, encourage undivided attention, and leverage the physicality of their bodies and the environment. We conclude by providing a set of recommendations for designers interested in creating co-located technologies that encourage social engagement and relationship building.

Ella Dagan, Ana Cárdenas Gasca, Ava Robinson et al.

We present Project IRL (In Real Life), a suite of five mobile apps we created to explore novel ways of supporting in-person social interactions with augmented reality. In recent years, the tone of public discourse surrounding digital technology has become increasingly critical, and technology's influence on the way people relate to each other has been blamed for making people feel "alone together," diverting their attention from truly engaging with one another when they interact in person. Motivated by this challenge, we focus on an under-explored design space: playful co-located interactions. We evaluated the apps through a deployment study that involved interviews and participant observations with 101 people. We synthesized the results into a series of design guidelines that focus on four themes: (1) device arrangement (e.g., are people sharing one phone, or does each person have their own?), (2) enablers (e.g., should the activity focus on an object, body part, or pet?), (3) affordances of modifying reality (i.e., features of the technology that enhance its potential to encourage various aspects of social interaction), and (4) co-located play (i.e., using technology to make in-person play engaging and inviting). We conclude by presenting our design guidelines for future work on embodied social AR.

Molly Jane Nicholas, Brian A. Smith, Rajan Vaish

We introduce Friendscope, an instant, in-the-moment experience sharing system for lightweight commercial camera glasses. Friendscope explores a new concept called a shared camera. This concept allows a wearer to share control of their camera with a remote friend, making it possible for both people to capture photos/videos from the camera in the moment. Through a user study with 48 participants, we found that users felt connected to each other, describing the shared camera as a more intimate form of livestreaming. Moreover, even privacy-sensitive users were able to retain their sense of privacy and control with the shared camera. Friendscope's different shared camera configurations give wearers ultimate control over who they share the camera with and what photos/videos they share. We conclude with design implications for future experience sharing systems.

Yan Chen, Andrés Monroy-Hernández, Ian Wehrman et al.

User-generated content platforms curate their vast repositories into thematic compilations that facilitate the discovery of high-quality material. Platforms that seek tight editorial control employ people to do this curation, but this process involves time-consuming routine tasks, such as sifting through thousands of videos. We introduce Sifter, a system that improves the curation process by combining automated techniques with a human-powered pipeline that browses, selects, and reaches an agreement on what videos to include in a compilation. We evaluated Sifter by creating 12 compilations from over 34,000 user-generated videos. Sifter was more than three times faster than dedicated curators, and its output was of comparable quality. We reflect on the challenges and opportunities introduced by Sifter to inform the design of content curation systems that need subjective human judgments of videos at scale.

Anhong Guo, Ilter Canberk, Hannah Murphy et al.

We introduce Blocks, a mobile application that enables people to co-create AR structures that persist in the physical environment. Using Blocks, end users can collaborate synchronously or asynchronously, whether they are colocated or remote. Additionally, the AR structures can be tied to a physical location or can be accessed from anywhere. We evaluated how people used Blocks through a series of lab and field deployment studies with over 160 participants, and explored the interplay between two collaborative dimensions: space and time. We found that participants preferred creating structures synchronously with colocated collaborators. Additionally, they were most active when they created structures that were not restricted by time or place. Unlike most of today's AR experiences, which focus on content consumption, this work outlines new design opportunities for persistent and collaborative AR experiences that empower anyone to collaborate and create AR content.

Snehalkumar, S. Gaikwad, Durim Morina et al.

Paid crowdsourcing platforms suffer from low-quality work and unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores, which make high-quality work and workers difficult to find, stem from social pressure to avoid giving negative feedback. We introduce Boomerang, a reputation system for crowdsourcing that elicits more accurate feedback by rebounding the consequences of feedback directly back onto the person who gave it. With Boomerang, requesters find that their highly-rated workers gain earliest access to their future tasks, and workers find tasks from their highly-rated requesters at the top of their task feed. Field experiments verify that Boomerang causes both workers and requesters to provide feedback that is more closely aligned with their private opinions. Inspired by a game-theoretic notion of incentive-compatibility, Boomerang opens opportunities for interaction design to incentivize honest reporting over strategic dishonesty.

Hana Habib, Neil Shah, Rajan Vaish

Public sharing is integral to online platforms. This includes the popular multimedia messaging application Snapchat, on which public sharing is relatively new and unexplored in prior research. In mobile-first applications, sharing contexts are dynamic. However, it is unclear how context impacts users' sharing decisions. As platforms increasingly rely on user-generated content, it is important to also broadly understand user motivations and considerations in public sharing. We explored these aspects of content sharing through a survey of 1,515 Snapchat users. Our results indicate that users primarily have intrinsic motivations for publicly sharing Snaps, such as to share an experience with the world, but also have considerations related to audience and sensitivity of content. Additionally, we found that Snaps shared publicly were contextually different from those privately shared. Our findings suggest that content sharing systems can be designed to support sharing motivations, yet also be sensitive to private contexts.

Taryn Bipat, Maarten Willem Bos, Rajan Vaish et al.

Camera glasses enable people to capture point-of-view videos using a common accessory, hands-free. In this paper, we investigate how, when, and why people used one such product: Spectacles. We conducted 39 semi-structured interviews and surveys with 191 owners of Spectacles. We found that the form factor elicits sustained usage behaviors, and opens opportunities for new use-cases and types of content captured. We provide a usage typology, and highlight societal and individual factors that influence the classification of behaviors.

Snehalkumar "Neil" S. Gaikwad, Nalin Chhibber, Vibhor Sehgal et al.

Low-quality results have been a long-standing problem on microtask crowdsourcing platforms, driving away requesters and justifying low wages for workers. To date, workers have been blamed for low-quality results: they are said to make as little effort as possible, do not pay attention to detail, and lack expertise. In this paper, we hypothesize that requesters may also be responsible for low-quality work: they launch unclear task designs that confuse even earnest workers, under-specify edge cases, and neglect to include examples. We introduce prototype tasks, a crowdsourcing strategy requiring all new task designs to launch a small number of sample tasks. Workers attempt these tasks and leave feedback, enabling the re- quester to iterate on the design before publishing it. We report a field experiment in which tasks that underwent prototype task iteration produced higher-quality work results than the original task designs. With this research, we suggest that a simple and rapid iteration cycle can improve crowd work, and we provide empirical evidence that requester "quality" directly impacts result quality.

Rajan Vaish, Andrés Monroy-Hernández

In this paper, we present CrowdTone, a system designed to help people set the appropriate tone in their email communication. CrowdTone utilizes the context and content of an email message to identify and set the appropriate tone through a consensus-building process executed by crowd workers. We evaluated CrowdTone with 22 participants, who provided a total of 29 emails that they had received in the past, and ran them through CrowdTone. Participants and professional writers assessed the quality of improvements finding a substantial increase in the percentage of emails deemed "appropriate" or "very appropriate" - from 25% to more than 90% by recipients, and from 45% to 90% by professional writers. Additionally, the recipients' feedback indicated that more than 90% of the CrowdTone processed emails showed improvement.

Mark E. Whiting, Dilrukshi Gamage, Snehalkumar S. Gaikwad et al.

Crowd workers are distributed and decentralized. While decentralization is designed to utilize independent judgment to promote high-quality results, it paradoxically undercuts behaviors and institutions that are critical to high-quality work. Reputation is one central example: crowdsourcing systems depend on reputation scores from decentralized workers and requesters, but these scores are notoriously inflated and uninformative. In this paper, we draw inspiration from historical worker guilds (e.g., in the silk trade) to design and implement crowd guilds: centralized groups of crowd workers who collectively certify each other's quality through double-blind peer assessment. A two-week field experiment compared crowd guilds to a traditional decentralized crowd work model. Crowd guilds produced reputation signals more strongly correlated with ground-truth worker quality than signals available on current crowd working platforms, and more accurate than in the traditional model.

Andreas Veit, Michael Wilber, Rajan Vaish et al.

When crowdsourcing systems are used in combination with machine inference systems in the real world, they benefit the most when the machine system is deeply integrated with the crowd workers. However, if researchers wish to integrate the crowd with "off-the-shelf" machine classifiers, this deep integration is not always possible. This work explores two strategies to increase accuracy and decrease cost under this setting. First, we show that reordering tasks presented to the human can create a significant accuracy improvement. Further, we show that greedily choosing parameters to maximize machine accuracy is sub-optimal, and joint optimization of the combined system improves performance.