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This qualitative study draws on interviews and observations with nurses working in a virtual intensive care unit and using algorithms to track patient progress. It overviews how health practitioners navi- gate algorithmic systems to build relationships with other providers and patients, with attention to strategies for accountability and ad- vocacy in virtual healthcare contexts. 

Data science has become an important topic for the CHI conference and community, as shown by many papers and a series of workshops. Previous workshops have taken a critical view of data science from an HCI perspective, working toward a more human–centered treatment of the work of data science and the people who perform the many activities of data science. However, those approaches have not thoroughly examined their own grounds of criticism. In this workshop, we deepen that critical view by turning a reflective lens on the HCI work itself that addresses data science. We invite new perspectives from the diverse research and practice traditions in the broader CHI community, and we hope to co-create a new research agenda that addresses both data science and human-centered approaches to data science. 

Policing decisions, allocations and outcomes are determined by mapping historical crime data geo- spatially using popular algorithms. In this extended abstract, we present early results from a mixed- methods study of the practices, policies, and perceptions of algorithmic crime mapping in the city of Milwaukee, Wisconsin. We investigate this differential by visualizing potential demographic biases from publicly available crime data over 12 years (2005-2016) and conducting semi-structured interviews of 19 city stakeholders and provide future research directions from this study. 

Many decisions about social, economic, and personal life are heavily data-driven. At the same time, data has become increasingly quantified, and available to people and institutions in positions of power, often with little introspection or reflection on its positive uses or harmful misuses. This panel will inspect CSCW’s role in identifying constructive and appropriate uses of data and its responsibility for protecting against harms and inequalities perpetuated by misuse. The panel will present a series of debates about quantification of data, data surveillance, organizational data use, and policy making. An overarching theme throughout the set of debates is interrogating CSCW’s role in extending critical scholarship on power and justice towards academic, policy, and industry impact. 

Assessing the impact of regional or statewide interventions in primary and secondary school (K-12) computer science (CS) education is difficult for a variety of reasons. Qualitative survey data provide only a limited view of impacts, but quantitative data can be notoriously difficult to acquire at scale from large numbers of classrooms, schools, or local educational authorities. In this paper, we use several publicly available data sources to glean insights into public high school CS enrollments across an entire U.S. state. Course enrollments with NCES course codes and local descriptors, school-level demographic data, and school geographic attendance boundaries can be combined to highlight where CS offerings persist and thrive, how CS enrollments change over time, and the ultimate quantitative impact of a statewide intervention. We propose a more appropriate level of data aggregation for these types of quantitative studies than has been undertaken in previous work while demonstrating the importance of a contextual aggregation process. The results of our disparate impact analysis for the first time quantify the impact of a statewide Exploring Computer Science (ECS) program rollout on economic groups across the region. Our blueprint for this analysis can serve as a template to guide and assess large-scale K-12 CS interventions wherever detailed project evaluation methods cannot scale to encompass the entire study area, especially in cases where attribute heterogeneity is a significant issue.