This essay offers seven intersectional feminist principles for equitable and actionable COVID-19 data, drawing from the authors' prior work on data feminism. Our book, Data Feminism (D'Ignazio and Klein, 2020), offers seven principles which suggest possible points of entry for challenging and changing power imbalances in data science. In this essay, we offer seven sets of examples, one inspired by each of our principles, for both identifying existing power imbalances with respect to the impact of the novel coronavirus and its response, and for beginning the work of change.

中文翻译：

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公平且可操作的 COVID-19 数据的七项交叉女权主义原则


本文借鉴了作者之前关于数据女权主义的工作，为公平且可操作的 COVID-19 数据提供了七项交叉女权主义原则。我们的书《数据女权主义》（D'Ignazio 和 Klein，2020 年）提供了七个原则，为挑战和改变数据科学中的权力失衡提出了可能的切入点。在本文中，我们提供了七组示例，其中一组示例受到我们每一项原则的启发，用于识别在新型冠状病毒及其应对措施方面现有的权力失衡，并开始变革工作。