数据公平的许多方面

IF 1.5 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

ACM Journal of Data and Information Quality Pub Date : 2022-02-07 DOI:10.1145/3533425

H. Jagadish, Julia Stoyanovich, B. Howe

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引用次数: 5

摘要

数据驱动的系统可以以各种方式诱发、实施和放大系统性歧视。作为数据科学家，我们倾向于孤立和形式化公平问题，使其适用于狭隘的技术解决方案。然而，这种简化的方法在实践中是不充分的。在本文中，我们试图从广义上解决数据公平问题，确定数据驱动系统中体现数据公平的不同方式，并提出研究议程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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The Many Facets of Data Equity

Data-driven systems can induce, operationalize, and amplify systemic discrimination in a variety of ways. As data scientists, we tend to prefer to isolate and formalize equity problems to make them amenable to narrow technical solutions. However, this reductionist approach is inadequate in practice. In this article, we attempt to address data equity broadly, identify different ways in which it is manifest in data-driven systems, and propose a research agenda.

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来源期刊

ACM Journal of Data and Information Quality COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

4.10

自引率

4.80%

发文量