自主性差距:对Citro等人和统计界的回应

IF 1.5 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS Statistics and Public Policy Pub Date : 2023-09-14 DOI:10.1080/2330443x.2023.2221324
Claire McKay Bowen
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引用次数: 0

摘要

虽然有偏见的人工智能的威胁受到了相当大的关注,但另一个对数据民主的无形威胁却没有得到科学或媒体的关注。这一威胁就是美国13个主要联邦统计机构缺乏自主权。这些机构收集数据,为美国联邦政府的关键决策提供信息,例如分配资源和提供基本服务。由于缺乏特定机构的法定自主权保护,这些机构很容易受到政治影响,而这种影响可能在公众不知情的情况下产生持久的影响。Citro等人对13个联邦统计机构的专业自主权进行了评估,发现由于缺乏法定保护(以及其他因素),这些机构缺乏足够的自主权。他们提出了三项建议,以加强联邦统计机构的领导能力及其自主权,以处理所有13个主要联邦统计机构的每一项自主权措施。实施这些建议是防止联邦统计系统未来受到侵蚀的初步和关键步骤。此外,统计人员必须发挥积极作用,发起并参与与各个科学领域的公开对话,以保护和促进联邦统计机构的重要工作。
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The Autonomy Gap: Response to Citro et al. and the statistical community
While the threat of biased AI has received considerable attention, another invisible threat to data democracy exists that has not received scientific or media attention. This threat is the lack of autonomy for the 13 principal United States federal statistical agencies. These agencies collect data that informs the United States federal government’s critical decisions, such as allocating resources and providing essential services. The lack of agency-specific statutory autonomy protections leaves the agencies vulnerable to political influence, which could have lasting ramifications without the public’s knowledge. Citro et al. evaluate the professional autonomy of the 13 federal statistical agencies and found that they lacked sufficient autonomy due to the absence of statutory protections (among other things). They provided three recommendations to enhance the strength of the federal statistical agency’s leadership and its autonomy to address each measure of autonomy for all 13 principal federal statistical agencies. Implementing these recommendations is an initial and crucial step toward preventing future erosion of the federal statistical system. Further, statisticians must take an active role in initiating and engaging in open dialogues with various scientific fields to protect and promote the vital work of federal statistical agencies.
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来源期刊
Statistics and Public Policy
Statistics and Public Policy SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
3.20
自引率
6.20%
发文量
13
审稿时长
32 weeks
期刊最新文献
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction Clusters of Jail Incarcerations in US Counties: 2010-2018 Comment on ‘What protects the autonomy of the Federal Statistics Agencies? An Assessment of the Procedures in Place That Protect the Independence and Objectivity of Official Statistics” by Pierson et al. On Coping in a Non-Binary World: Rejoinder to Biedermann and Kotsoglou Commentary on “Three-Way ROCs for Forensic Decision Making” by Nicholas Scurich and Richard S. John (in: Statistics and Public Policy)
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