Rua M. Williams: "Newly available: "Only the Ol…"

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Rua M. Williams @FractalEcho@kolektiva.social

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"Only the Old and Sick will Die": reproducing 'eugenic visuality' in covid 19 data visualization

COVID-19 illness and death has disproportionately impacted marginalized groups the world over. In the United States, Black and Indigenous people have endured the largest risk of death. Disabled and chronically ill people have continued to isolate as their peers “return to normal”, bearing sole liability for their own safety in a society that deems their lives not worth the “sacrifice” of public health measures. While public and institutional policy makers bare personal responsibility for “survival of the fittest” approaches to public health, data science and visualization has contributed to and legitimized many of these eugenic policy decisions through design tropes I characterize as ‘eugenic visuality’. In this paper, I explore how inadequacies and obscurities in COVID-19 data visualization have contributed to and sustained public narratives that devalue marginalized lives for the comfort of white-supremacist and capitalist social norms. While I focus on visualizations and statements provided by the CDC, the implications extend beyond any individual or institution to our collective preconceptions and values. Namely, unexamined biases and unquestioned norms are embedded in data science and visualization, constraining how data is represented and interpreted. These assumptions limit how data can be leveraged in the pursuit of just social policy. Therefore, I propose guiding principles for a Just Visuality in data science and representation, supported by the work of disabled activists and scholars of color.

https://ieeexplore.ieee.org/document/10227111

https://www.researchgate.net/publication/373448965_Only_the_Old_and_Sick_Will_Die_-_Reproducing_'Eugenic_Visuality'_in_COVID-19_Data_Visualization

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ieeexplore.ieee.org“Only the Old and Sick Will Die” - Reproducing ‘Eugenic Visuality’ in COVID-19 Data VisualizationCOVID-19 illness and death has disproportionately impacted marginalized groups the world over. In the United States, Black and Indigenous people have endured the largest risk of death. Disabled and chronically ill people have continued to isolate as their peers “return to normal”, bearing sole liability for their own safety in a society that deems their lives not worth the “sacrifice” of public health measures. While public and institutional policy makers bare personal responsibility for “survival of the fittest” approaches to public health, data science and visualization has contributed to and legitimized many of these eugenic policy decisions through design tropes I characterize as ‘eugenic visuality’. In this paper, I explore how inadequacies and obscurities in COVID-19 data visualization have contributed to and sustained public narratives that devalue marginalized lives for the comfort of white-supremacist and capitalist social norms. While I focus on visualizations and statements provided by the CDC, the implications extend beyond any individual or institution to our collective preconceptions and values. Namely, unexamined biases and unquestioned norms are embedded in data science and visualization, constraining how data is represented and interpreted. These assumptions limit how data can be leveraged in the pursuit of just social policy. Therefore, I propose guiding principles for a Just Visuality in data science and representation, supported by the work of disabled activists and scholars of color.