This article uses the COVID-19 pandemic to demonstrate how our understanding of ethnic inequalities could be improved by greater use of algorithms that infer ethnic heritage from people’s names. It starts from two inter-connected propositions: the effectiveness of many public sector programs is hampered by inadequate information on how differently different ethnic groups behave, and anxiety over how to discuss matters to do with race inhibits proper evaluation of methodologies which would address this problem. This article highlights four mindsets which could benefit from challenge: the officially sanctioned categories by which ethnic data are tabulated are too crude to capture the subtler differences which are required for effective communications; while self-identification should continue to drive one-to-one communications, it should not preclude the use of more appropriate methods of recording ethnic heritage when analyzing data for population groups; public servants often fail to recognize the limitations of directional measures such as the Index of Multiple Deprivation as against “natural” classifications such as Mosaic and Acorn; and in their quest for predictive accuracy statisticians often overlook the benefit of the variables they use being “actionable,” defining population groups that are easy to reach whether geographically or using one-to-one communications.

中文翻译：

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COVID-19 和种族：保护数据还是拯救生命？

本文使用 COVID-19 大流行来展示如何通过更多地使用从人名推断种族遗产的算法来改善我们对种族不平等的理解。它从两个相互关联的命题开始：许多公共部门计划的有效性受到关于不同族群行为差异的信息不足的阻碍，以及对如何讨论与种族有关的问题的焦虑阻碍了对解决这一问题的方法的正确评估. 本文重点介绍了可以从挑战中受益的四种心态：官方认可的种族数据列表分类过于粗糙，无法捕捉有效沟通所需的更细微的差异；虽然自我认同应该继续推动一对一的交流，在分析人口群体数据时，不应排除使用更合适的记录民族遗产的方法；公务员往往无法认识到多重剥夺指数等定向措施的局限性，而不是像马赛克和橡子这样的“自然”分类；并且在他们寻求预测准确性的过程中，统计学家经常忽视他们使用的变量“可操作”的好处，即定义易于接触的人群，无论是地理上还是使用一对一交流。