GROUP '20- Companion of the 2020 ACM International Conference on Supporting Group Work

Design education finds itself at a crossroad, having to deal with an emerging industry 4.0 and global issues such as, sustainability. The engineering design curriculum needs to arm students with 21st century soft skills to be able to cope with an ever-evolving world. One of the most important soft skills is self-regulation, so that students can adapt to changing situations; know what skills they are lacking; and function efficiently in a multidisciplinary team. This application includes an introduction to self-regulation in design education. Additionally, it includes the setup and findings of a first pilot study the Ph.D. researcher conducted. The study used an online platform to assess students' involvement in a team and the group dynamics within the team. Afterwards the results were communicated and students were asked to reflect and propose solutions for group related challenges. Lastly, this application includes an overview of the reasons why the Ph.D. researcher would like to participate in the doctoral colloquium.

Broadening participation in computing is essential for involving a diversity of perspectives in creating new technologies. Despite attempts to involve diverse groups in computer science, underrepresented groups such as women; racial, gender, and sexual minorities; and people with disabilities are still under-involved in formal computing spaces such as education and industry. In contrast to examining how to bring underrepresented groups into these spaces, I propose examining examples of computational work already happening within marginalized communities, where participants who may be outsiders to computing culture still engage with computation to empower their own communities.

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.

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.

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.

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.

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.

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.

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.

The number of students studying internationally has grown significantly in recent years. Saudi Arabia comes among the top countries sending students to the United States. While settling overseas, Saudi international students face many challenges. In particular, student-mothers encounter the challenges of fulfilling their family and childcare responsibilities while pursuing their studies abroad. Despite the rich literature of information and technology use by international students, few studies considered international students from Saudi Arabia or student-mothers, who face dual expectations of their higher education and family roles. Therefore, my dissertation research aims to explore the information practices and technology uses among the community of international student Saudi mothers in the United States during their studies.

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.

In this paper, we describe our efforts to appropriate an autono-preneurial agent---in this case, the Amazon Locust\footnoteThe Amazon Locust is a fictional autonomous lawn mowing robot that we imagine to be widely available through Amazon by 2035. ---through the development of an API that enables equitable and socially aware entrepreneurial decision making on the part of the Locust. We present a new API and our intended vision for this system, along with our proposed deployment plan for implementing appropriated Locusts in Midwestern USA suburban communities. These appropriated Locusts will allow community provisioning decision-making that moves beyond consideration of profitability to also include decisions based on equity, equality, community, and interpersonal relationships. We discuss the broader implications of this work and point toward future areas of inquiry.

The proliferation of information and communication technologies (ICT) along with pressures of global capitalism has called in to question the role of public libraries in the future. At the same time, communities continue to rely on public libraries for social and civic functions. In this paper, the authors use research through design (RTD) and design fiction methods to explore alternative conceptions of public libraries and the role of ICTs in them The authors propose a design fiction in the form of a call for grant proposals issued by the Institute of Museum and Library Services. The design fiction focuses on the concept of conviviality to propose future design spaces for new kinds of interactions between library patrons and the world around them.

Within online communities, social norms that both set expectations for and regulate behavior can be critical to the overall welfare of the community--particularly in the context of the privacy and safety of its members. For communities where the cost of regulatory failure can be high, it is important to understand both the conditions under which norms might be effective, and when they might fail. As a case study, we consider transformative fandom, a creative community dedicated to reimagining existing media in subversive ways. Due to the vulnerability of many members, this community has strong, longstanding norms to keep its members safe. Through an interview study with 25 fandom participants, we investigate this complex array of implicit norms that have been largely effective over time, but have also begun to break down. Catalysts for these breakdowns include value tensions between sub-communities and an increasing presence of outsiders, though most prominently, we identify a disconnect between the norms the community needs to support and the design of the platforms they occupy.

Given the negative effects on individuals and society caused by trust in social media rumours, this research focuses on the trust-building process related to social media rumours. Using a structural equation model to analyze the data of 234 journalism students, we investigated the trust-building process, including trust belief and trust action, related to social media rumours. We studied the associations of trust belief and trust action with self-efficacy, which is an important psychological factor, and the relationship of the two levels of trust with the expressive and consumptive use of social media rumours. The results suggest that self-efficacy has a positive effect on trust belief but a negative effect on trust action. The different levels of trust have different effects on the use of rumours. The study provides insights into the trust-building process in relation to social media rumours and empirical support for media literacy training and computer ethics education.

The Internet of Things (IoT) has improved home security for users at home and away, however, most smart home notification systems fail to recognize whether a user has responded to threats in their home. Allowing multiple users to receive alerts can alleviate this issue, but some smart home owners would prefer to limit access controls to "in case of emergency" access. This study explores smart home hub features to co-monitor IoT device access through Role and Event Based Access Control (REBAC), leveraging a device owner's social network, i.e. neighbors and relatives, when homeowners are not available or have failed to respond to a potential threat. Our findings of user studies with two pilot groups demonstrated user's willingness to share access to their smart homes, provided access controls had limited permissions. By adding contextual information of an event to traditional access control models, we were able to create richer permission protocols.

Collaborative problem solving (CPS) capability has become more important recently since the complexity of the problems we face is often beyond the ability of any one talented individual. The PISA framework of CPS is a widely used tool for measuring CPS capability. However, the PISA framework has limitations in that it can only be applied in a virtual situation and on problems which have a correct answer. This study was conducted to find out a way of measuring CPS capability in real problem solving situations which demand creativity. In order to measure CPS capability, we combined an expert-assessed contribution score and peer-assessed CPS capability score. Results show that the combined score can predict CPS capability well.

Cardiovascular disease (CVD) is the leading causes of death in the United States and worldwide. While CVD risk factors are well-known and many can be changed with diet and exercise, more research is needed to understand how to design effective interventions that help patients reduce CVD risk. In this paper, we present the results of a content analysis of the Health Freedom Circle of Friends (COF) Walking Program, a community-based health program run by a public health non-profit that has been shown to reduce CVD risks. We examine the design to better understand the persuasive tools used as well as parts of the design that might benefit from a technological intervention.

ach 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 there is a widespread concern that these algorithms create a 'black box' of embedded bias, lack of human discretion, transparency or trust. For example, ADM is currently tested in public administration in job placement for prediction of a citizen's risk of long-term unemployment. This research project focus on bringing about research on citizens' 'trust' and 'transparency' from a practice-oriented perspective when algorithms are increasingly introduced in public services such as job placement. We propose a study of citizen-government relations to begin to uncover how computational systems and semi-automated decisions affect the relationship between citizens and caseworker, as they work through the collaborative processes around casework. In this context, our question is: What are citizens and caseworkers' different concepts of trust and transparency? How are casework processes affected as we are beginning to see a closer integration between legal guidelines and computational systems in casework? These questions are of huge importance to get a better understanding of how algorithms are changing the ways society makes decisions in core areas of public services in order to inform the responsible design of technologies in areas such as job placement.

Text classification using machine learning can be applied in various contexts such as in classifying research papers, identifying relevant news stories, and detecting fake reviews. Training an algorithm to perform such tasks generally requires a dataset with predefined labels. Valid labels for texts in a given domain can be predefined by domain experts. However, when it comes to free-form text from messaging applications and social networking sites it is difficult to predict what labels may be extracted from the text. Grounded theory provides a method by which concepts that emerge from data can be expressed as categories and properties. These categories and properties can then be arranged in a hierarchical class label structure that can be used to build a dataset for training models. This study focuses on text related to veterans with post traumatic stress disorder and identifies a hierarchical class label structure, with the future goal of applying this to prevent crisis situations.

This paper focuses on understanding the collaborative work of multi-disciplinary teams in the child welfare system (CWS). CWS workers participate in meetings mediated by policies in place, current child-welfare practice, as well as algorithms that offer recommendations. We conducted 25 observations of these meetings to assess how algorithms aid decision-making in a domain where decisions often come down to the policies and practices in place. Our findings suggest that the algorithm works fairly well at recommending placement settings, however, these recommendations are often overridden because of policy or legal requirements. Moreover, re-appropriation of the placement algorithm to prescribe the rates for foster parents has led to unintended consequences. This poster identifies uses cases of the algorithm in place, scenarios where conflicts arise between the algorithm and policy/practice, as well as how these conflicts are addressed. Our work identifies a need for human-centered algorithms that can better support child welfare practice.

About 40.3 million people across the world are victims to human trafficking. Approximately a quarter of them are children, averaging an age of 12 years old. To disrupt the cycle of human sex trafficking, non-profit organizations (NPO) provide educational training seminars to dispel common misconceptions and identify signs of human sex trafficking. In partnership with Paving the Way Foundation (PTW), one such NPO, we present evaluations from 159 children PTW's human sex trafficking prevention education program. We qualitatively analyzed a secondary data set of surveys to understand: 1) how this prevention education effects children's perception of their relationships, 2) how it effects the potential risk-taking behavior of children, and 3) who do children think should receive this human sex trafficking training. This research brings attention to the importance of educating children on human sex trafficking and empowering them to act against it.

We present an extension of the existing CoNavigator collaboration system that allows for persistence of in-person collaboration sessions through a digitally projected overlay and camera system. This augmented reality environment allows participants in a CoNavigator session to resume from a previous session without having to laboriously recreate the physical state of the space manually. By combining features traditionally found only in digital space with the intuitive nature of a tactile physical environment, we hope to produce a tool that builds on the work of CoNavigator and lowers barriers to adoption while increasing efficacy.

The collection of devices networked via the internet, also referred to as the Internet of Things, is poised to grow in adoption. With this rise has come equally increasing concerns for security and privacy. Considering Nissenbaum's framework of Contextual Integrity, we examined users' perceptions of IoT environmental and wearable devices to investigate acceptable norms surrounding privacy perceptions. We present results from a qualitative analysis of an interview study of 19 parent-young adult dyads to give insights on how privacy norms in context of two IoT environments were varying across two generations. We strongly believe understanding these variations can inform IoT system designs and government policies concerning the privacy and management of IoT devices.

Organizing and assigning papers into sessions within a large conference is a formidable challenge. Some conference organizers, who are typically volunteers, have utilized event planning software to ensure simple constraints, such as two people can not be scheduled to talk at the same time. In this work, we proposed utilizing natural language processing to find the topics within a corpus of conference submissions and then cluster them together into sessions. As a preliminary evaluation of this technique, we compare session assignments from previous conferences to ones generated with our proposed techniques.

CampusPartner is an assistive technology that we are developing to support people with mobility impairments in planning and view their walking routes in advance. By viewing the route in advance, users can see an overview and detailed information about it as well as turn-by-turn instructions. CampusPartner will be a mobile application that will integrate existing services to provide navigation functionality. Users will be able to create a profile, which will include information such as obstacles and road types they would like to avoid, as well as their favorite or most commonly used routes. Additionally, users will be able to correct missing or inaccurate information, such as the absence of stairs on the map or temporary obstacles.

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.

In conducting research, one typically considers the potential harms associated with the research and how to minimize or mitigate the negative impacts thus leaving less time to consider designing for optimized benefits. The definition of positive impact relies heavily on the situational power structures, culture, and social norms of the given community. This workshop will give researchers across the GROUP community the opportunity to explore how we might design or use technologies to maximize wider societal benefits.

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.