综合数据和公共政策:用算法生成的数据支持现实世界的决策者

IF 0.1 Q4 POLITICAL SCIENCE
Kevin Jenkins
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引用次数: 1

摘要

良好的政策最好是通过借鉴大量高质量的证据来制定的。数据科学的快速发展和大数据集的出现极大地推动了定量证据的提供和使用。然而,一些关键的限制仍然存在,包括可用的数据集对于某些分析目的来说仍然不够大。此外,还存在隐私和数据安全风险。合成数据是数据科学的一个新兴领域,它可以通过使研究工作更快、更少错误,同时确保隐私和安全,从而潜在地支持政策决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthetic Data and Public Policy: supporting real-world policymakers with algorithmically generated data
Good policy is best developed by drawing on a wide array of high-quality evidence. The rapid growth of data science and the emergence of big datasets has materially advanced the supply and use of quantitative evidence. However, some key constraints remain, including that available datasets are still not big enough for some analytical purposes. There are also privacy and data security risks. Synthetic data is an emerging area of data science that can potentially support policy decision making through enabling research to work faster and with fewer errors while also ensuring privacy and security.
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Turkish Policy Quarterly
Turkish Policy Quarterly POLITICAL SCIENCE-
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