Computer-supported cooperative work (CSCW) researchers have plenty to say about designing through texts. However, the relationships between implementation and design are often misunderstood, by engineering project systems developers who design collaborative systems and others. This problem is not new; how to utilize CSCW insights effectively and correctly in engineering projects has long been a concern in the CSCW community. By reviewing year-long, multiple-site ethnographic studies in the maritime domain conducted since 2015, this paper reflects on the “reflexivity of account” and “professional vision” as complementary concepts for ensuring that actors’ actions and reasonings are designed in such a way that actors – in this case, maritime operators, systems developers, educators, policymakers and shipowners – are accountable in and through their member groups. Rather than generalizing my findings and my role in the maritime domain as explicit knowledge to help other CSCW researchers in studying engineering projects, the goal of this paper is to seek intersubjective knowledge of my fieldwork to trigger the utilization of CSCW insights as a fundamental basis for facilitating systems design. The aim is to shape and reshape systems in line with what those actors do in reality.
https://doi.org/10.1145/3479514
Investigative data journalist work with a variety of data sources to tell a story. Though prior work have indicated that there is a close relationship between journalists’ data work practices and that of data scientists. However, these relationships and data work practices are not empirically examined, and understanding them is crucial to inform design of tools that are used by different groups of people including data scientist’s and data journalist’s. Thus, to bridge this gap, we studied investigative reporters’ data work practices with one non-profit investigative newsroom. Our study design includes two activities: 1) semi-structured interviews with journalists, and 2) a sketching activity allowing journalists to depict examples of their work practices. By analyzing these data and synthesizing across related prior work, we propose the major phases in datadriven investigative journalism story idea generation process. Our study findings show that the journalists employ a collection of multiple, iterative, cyclic processes to identify journalistically “interesting” story ideas.These processes both significantly resemble and show subtle nuanced differences with data science work practices identified in prior research. We further verified our proposal through a member check with key informants. This work offers three primary contributions. First, it provides a close glimpse into the main phases of investigative journalists’ data-driven story idea generation technique. Second, it complements prior work studying formal data science practices by examining data-driven investigative journalists, whose primary expertise lies outside computing. Third, it identifies particular points in the data exploration processes that would benefit from design interventions and suggests future research directions.
Preparing a new dance performance involves more than asking dancers to learn individual steps. We are interested in understanding how dancers collaborate as they rehearse a new dance piece, with a particular emphasis on how they use physical and digital artifacts to support this process. We conducted a 12-month longitudinal observation study with a dance company that re-staged a dance piece, taken from the contemporary repertoire and unknown to the dancers. This study focuses on the role that artifacts, used during the rehearsal process, play in shaping learning. We show how dancers produced an ecology of artifacts with the aim of distributing their knowledge and sharing it with other learners. We show how artifacts serve to decompose the choreography into simpler components, independent and complementary, with the objective to reduce the difficulty of the learning task. We found that dancers compile artifacts to create a common structure among the group, allowing for improving the learning process. We conclude with design opportunities for technologies supporting long-term dance learning processes.
https://doi.org/10.1145/3449182
Studies have shown that interpersonal relationships such as families and friends are an important source of support and encouragement to those who seek to engage in healthier habits. However, challenges related to geographic distance may hinder those relationships from fully collaborating and engaging in healthy living together. To explore this domain, we developed and deployed a lightweight photo-based application called PhamilySpace to examine family members and friends engagement and awareness on healthy behaviors while living apart during a week-long intervention. Our analysis of the semi-structured interviews, pre/post-intervention instruments, and application logs suggests three main benefits of interventions for health promotion in this context: (1) increased awareness on acts of health; (2) reciprocal sharing of health information supports social accountability over distance; and (3) positive dialogue around health enhances support on healthy living. By providing insights into distributed family/friends interactions and experiences with the application, we identify benefits, challenges, and opportunities for future design interventions that promote healthy behaviors.
https://doi.org/10.1145/3449198
Extremely impoverished people (known as Beggars or Homeless, depending on where they live), are a group of vulnerable citizens that are deprived of necessary healthcare support, consequences of which can be minor to severe, and in some cases, fatal. Bangladesh, having a significant number of them, is no different. One noticeable difference of these beggars compared to similar communities in other parts of the world (e.g., homeless people in the USA) is that technology penetration is near-to-zero for beggars in Bangladesh, which we confirm through our field study. Thus, technology-based (such as app-based, mHealth, etc.) solutions for providing healthcare support, which maybe possible in advanced countries, is not possible in lower-income countries like Bangladesh. However, there does exist multiple healthcare services in Bangladesh intended for beggars and similar communities, which mostly remain underused by the intended population. This scenario presents a unique challenge, where there is a geographical gap between healthcare services and their intended recipients (beggars in our context). We tackle this problem through a carefully-crafted solution Dakter Bari (meaning ``Home of a doctor" in English), that is tailored to the application ecosystem in this context. We extract critical insights from our field study with (N=70) beggars, and from findings, create a pathway for availing lower-cost healthcare solutions using intermediaries. We also conduct field studies with (N=71) possible intermediary partners and (N=10) hospitals to identify the challenges and possibilities of such intermediary based solutions. With insights gained through these field studies, we then design, iteratively develop, deploy, and user-test such a solution in real cases. To penetrate the system further, we design and deploy posters that are easy to understand for the beggar community and report the findings from the system usage data. The usage of the system for more than six months registers 255 service requests and demonstrates its efficacy in bridging the gap we identified through our study.
https://doi.org/10.1145/3449118
As job-seeking and recruiting processes transition into digital spaces, concerns about hiring discrimination in online spaces have developed. Historically, women of color, particularly those with marginalized religious identities, have more challenges in securing employment. We conducted 20 semi-structured interviews with Muslim-American women of color who had used online job platforms in the past two years to understand how they perceive digital hiring tools to be used in practice, how they navigate the US job market, and how hiring discrimination as a phenomenon is thought to relate to their intersecting social identities. Our findings allowed us to identify three major categories of asymmetries (i.e., the relationship between the computing algorithms' structures and their users' experiences): (1) process asymmetries, which is the lack of transparency in data collection processes of job applications; (2) information asymmetries, which refers to the asymmetry in data availability during online job-seeking; and (3) legacy asymmetries, which explains the cultural and historical factors impacting marginalized job applicants. We discuss design implications to support job seekers in identifying and securing positive employment outcomes.
Online community moderators often rely on social signals like whether or not a user has an account or a profile page as clues that users are likely to cause problems. Reliance on these clues may lead to ``over-profiling'' bias when moderators focus on these signals but overlook misbehavior by others. We propose that algorithmic flagging systems deployed to improve efficiency of moderation work can also make moderation actions more fair to these users by reducing reliance on social signals and making norm violations by everyone else more visible. We analyze moderator behavior in Wikipedia as mediated by a system called RCFilters that displays social signals and algorithmic flags and estimate the causal effect of being flagged on moderator actions. We show that algorithmically flagged edits are reverted more often, especially edits by established editors with positive social signals, and that flagging decreases the likelihood that moderation actions will be undone. Our results suggest that algorithmic flagging systems can lead to increased fairness but that the relationship is complex and contingent.
https://doi.org/10.1145/3449130
Twitter uses machine learning to crop images, where crops are centered around the part predicted to be the most salient. In fall 2020, Twitter users raised concerns that the automated image cropping system on Twitter favored light-skinned over dark-skinned individuals, as well as concerns that the system favored cropping woman's bodies instead of their heads. In order to address these concerns, we conduct an extensive analysis using formalized group fairness metrics. We find systematic disparities in cropping and identify contributing factors, including the fact that the cropping based on the single most salient point can amplify the disparities because of an effect we term 'argmax bias'. However, we demonstrate that formalized fairness metrics and quantitative analysis on their own are insufficient for capturing the risk of representational harm in automatic cropping. We suggest the removal of saliency-based cropping in favor of a solution that better preserves user agency. For developing a new solution that sufficiently address concerns related to representational harm, our critique motivates a combination of quantitative and qualitative methods that include human-centered design.