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Award ID contains: 1850517

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  1. ; ; ; ; ; ; (, In Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems)
    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. 
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  2. ; ; ; ; ; ; (, CSCW'19 Companion)
    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. 
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  3. ; ; (, CSCW ’19 Companion)
    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. 
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