COVID-19 Brings Data Equity Challenges to the Fore

COVID-19 Brings Data Equity Challenges to the Fore
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COVID-19 使数据公平性挑战凸显

DOI:
10.1145/3440889
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发表时间:
2021
期刊:
Digital Government: Research and Practice
影响因子:
--
通讯作者:
Howe, Bill
Howe, Bill
中科院分区:
--
文献类型:
--
作者:
Jagadish, H. V.;Stoyanovich, Julia;Howe, Bill

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COVID-19 大流行迫使我们快速做出重要的数据驱动决策,将多样化且不可靠的信息来源汇集在一起​​,而无需我们通常采用的质量控制机制。这些决策在多个层面上都具有重要意义:它们可以为地方、州和国家政府政策提供信息,用于安排对组织内电梯和工作空间等物理资源的访问,并为个人的接触者追踪和隔离行动提供信息。在所有这些情况下,都可能出现严重的不平等现象,并通过数据驱动的决策系统传播和强化。在本文中,我们提出了一个名为 FIDES 的框架,用于展示和推理这些系统中的数据公平性。
The COVID-19 pandemic is compelling us to make crucial data-driven decisions quickly, bringing together diverse and unreliable sources of information without the usual quality control mechanisms we may employ. These decisions are consequential at multiple levels: They can inform local, state, and national government policy, be used to schedule access to physical resources such as elevators and workspaces within an organization, and inform contact tracing and quarantine actions for individuals. In all these cases, significant inequities are likely to arise and to be propagated and reinforced by data-driven decision systems. In this article, we propose a framework, called FIDES, for surfacing and reasoning about data equity in these systems.
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