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  • Conference Paper
    Using Stories to Understand Technology Needs and Technology Reuse by Rural Communities
    (Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Kotut, Lindah
    There is an increased sensitivity by people about how companies collect information about them, and how this information is packaged, used and sold. This perceived lack of control is highlighted by the helplessness of users of various platforms in managing or halting what data is collected from/about them. In a future where users have wrested control of their data and have the autonomy to decide what information is collected, how it is used and most importantly, how much it is worth, a new market emerges. This design fiction considers possible steps prescient companies would take to meet these demands, such as providing third-party subscription platforms offering personal data trade as a service. These services would provide a means for transparent transactions that preserve an owner's control over their data; allowing them to individually make decisions about what data they avail for sale, and the amount of compensation they would accept in trade.
  • Conference Paper
    Designing CMC Tools for Separated Families
    (Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Khojasteh, Negar
    Today, millions of people live in a country other than their home country, for example as international students, or as immigrants. Computer-mediated communication (CMC) tools such as video conferencing applications, help people stay in touch with their families and friends back home. Despite their use as a means of remote communication, CMC tools are very limited in affording users' needs that are beyond conversation, such as cooperative activities or enjoying a shared experience. For my dissertation, I am proposing the following aims. First, I will investigate the shortcomings of CMC tools in addressing people needs in remote communication. Secondly, based on this understanding, I will propose design implications that will address these shortcomings.
  • Conference Paper
    Understanding and Designing Sociotechnical Systems to Support the Impression Management Practices of Online Freelance Workers
    (Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Foong, Eureka
    A growing number of freelancers worldwide are involved in online, project-based knowledge work. Compared to employees in organizations, freelancers face serious challenges securing work and mitigate this by constructing favorable impressions on peers and clients. Sociotechnical systems present new opportunities and challenges to support impression management and two factors that influence it: 1) audience understanding and 2) gender role constraints. The goal of my dissertation is to understand and design sociotechnical systems that support freelancers' impression management. First, I will study and design online feedback exchange (OFE) systems that can help freelancers better understand their audiences by providing feedback on projects and portfolios. Second, I will investigate how gender role constraints influence freelancers' pricing behavior in online labor marketplaces. My research will contribute a novel OFE system for improving the quality of freelancers' portfolios and knowledge of gender differences in freelancers' pricing behavior to guide the design of sociotechnical systems that better support this emerging workforce.
  • Conference Paper
    Identity and User Behavior in Online Communities
    (Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Guo, Cheng
    In online communities, people share and discuss information at all levels of topic sensitivity. Identity policies within these communities range from real names to anonymity. The amount of user engagement, the quality of the information, disinformation behavior (e.g., trolling) may differ under different types of identity, which is currently unclear. Most of these online communities have a mechanism of content moderation. The relationship between identity and moderation is also unclear. Finally, yet little is known about how and why people make decisions of self-disclosure in online communities. My dissertation research aims to deepen our understanding of identity and user behavior in online communities. My research will benefit privacy researchers, online social network designers, policymakers, and researchers in the field of Human-Computer Interaction who study online identity and social media.
  • Conference Paper
    Understanding and Designing for Privacy in Wearable Fitness Platforms
    (Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Alqhatani, Abdulmajeed
    There has been increasing use of commercial wearable devices for tracking fitness-related activities. These devices sense and collect a variety of health and fitness data, which can be shared by users with other people and organizations. Yet, sharing personal data collected by these devices imposes several privacy concerns, ranging from private information exposure and repurposing, to aggregation and inferences. We do not fully understand people's sharing practices and privacy behaviors in the context of these ubiquitous devices. To address this limitation, my dissertation investigates the sharing of data collected by these devices in order to design solutions that support users' sharing needs and enhance their privacy. Preliminary findings indicate that users do not consider much of the data collected by these devices as sensitive, though they voice concerns about the possibility of abusing their data.
  • Conference Paper
    Deploying Human-Centered Machine Learning to Improve Adolescent Online Sexual Risk Detection Algorithms
    (Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Razi, Afsaneh
    As adolescents' engagement increases online, it becomes more essential to provide a safe environment for them. Although some apps and systems are available for keeping teens safer online, these approaches and apps do not consider the needs of parents and teens. We would like to improve adolescent online sexual risk detection algorithms. In order to do so, I'll conduct three research studies for my dissertation: 1) Qualitative analysis on teens posts on an online peer support platform about online sexual risks in order to gain deep understanding of online sexual risks 2) Train a machine learning approach to detect sexual risks based on teens conversations with sex offenders 3) develop a machine learning algorithm for detecting online sexual risks specialized for adolescents.
  • Conference Paper
    Algorithmic Decision Making in Public Administration: A CSCW-Perspective
    (Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Flügge, Asbjörn Ammitzböll
    In this paper, I propose a study of algorithmic decision making in public administration from a computer supported cooperative work (CSCW) perspective. Each day the public administration makes thousands of decisions with consequences for the welfare of its citizens. An increasing number of such decisions are supported or made by algorithmic decision making (ADM) systems, yet in the scientific and public sphere there is a growing concern that these algorithms become a 'black box' possibly containing hidden bias (Olsen et al., 2019), obstacles for human discretion (Rason, 2017), low transparency (Alkhatib and Bernstein, 2019) or trust (Mittelstadt et al. 2016). For example, ADM is currently tested in public administration in job placement for the prediction of a citizen's risk of long-term unemployment. Following prior research questioning the usefulness of the black box metaphor, my interest is to understand how caseworkers' and citizens understand ADM, as a basis for design of CSCW technologies employing ADM.
  • Conference Paper
    Beyond Transparency: Exploring Algorithmic Accountability
    (Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Cech, Florian
    Many of the ubiquitous algorithmic systems permeating society have come under scrutiny due to their lack of accountability. As algorithmic decision making increasingly affects our lives, calls to improve the transparency of these systems are met with social, legal and technical limitations that challenge whether transparency alone is the solution to algorithmic accountability. In my dissertation, I explore the role of algorithmic tranparency, algorithmic literacy and related issues as approaches towards holding algorithmic systems more accountable. Bridging HCI and STS communities, my work is grounded in a critical ethnography of algorithmic systems and their impact on its stakeholders. Through this approach, I aim to provide both theoretical insights and material solutions to the problem of accountability. By unpacking the complex socio-technical assemblage that make up these systems and employing both participatory and user-centred design principles, my goal is to co-design measures that support sense-making of algorithmic processes and allow holding these systems accountable.
  • Conference Paper
    Towards the Automatic Assessment of Student Teamwork
    (Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Ahuja, Rohan; Khan, Daniyal; Symonette, Danilo; Pan, Shimei; Stacey, Simon; Engel, Don
    Teamwork skills are crucial for college students, both at university and afterwards. At many universities, teams are increasingly using discussion platforms such as GroupMe and Slack to work virtually. However, little has been done so far to understand how to use the data these platforms generate to analyze student teamwork behaviors, and so to support or improve those behaviors. Furthermore, these data have not been exploited to determine whether effective student team members share any other traits. This project therefore attempts to determine (a) whether there are any characteristics common to the online discussion behaviors displayed by high-performing vs non high-performing student team members and (b) whether high-performing vs non high-performing student team members share any apparently teamwork-exogenous attributes. We find that the features of team member communication that best predict team member performance are sentence length and the number of words contributed to the team's discussion, with a range of other features playing a smaller role. We also find that teamwork-exogenous factors (such as pre-college ACT score, and number of credits attempted during the semester) were only moderately predictive of team member performance.
  • Conference Paper
    Mapping Out Human-Centered Data Science: Methods, Approaches, and Best Practices
    (Companion Proceedings of the 2020 ACM International Conference on Supporting Group Work, 2020) Kogan, Marina; Halfaker, Aaron; Guha, Shion; Aragon, Cecilia; Muller, Michael; Geiger, Stuart
    Social media platforms and social network sites generate a multitude of digital trace behavioral data, the scale of which often necessitates the use of computational data science methods. On the other hand, the socio-behavioral and often relational nature of the social media data requires the attention to context of user activity traditionally associated with qualitative analysis. Human-Centered Data Science (HCDS) attempts to bridge this gap by both harnessing the power of computational techniques and accounting for highly situated and nuanced nature of the social media activity. In this workshop we plan to consider the methods, pitfalls, and approaches of how to do HCDS effectively. Moreover, from collating and organizing these approaches we hope to progress to considering best (or at least common) practices in HCDS.