对今井Kosuke,蒋智超,D. James Greiner, Ryan Halen和Sooahn Shin关于“算法辅助人类决策的实验评估:在审前公共安全评估中的应用”的讨论表示感谢和贡献

IF 1.5 3区 数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
P. Dawid
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引用次数: 0

摘要

本文章由计算机程序翻译,如有差异,请以英文原文为准。
Seconder of the vote of thanks and contribution to the Discussion of “Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment” by Kosuke Imai, Zhichao Jiang, D. James Greiner, Ryan Halen and Sooahn Shin
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来源期刊
CiteScore
2.90
自引率
5.00%
发文量
136
审稿时长
>12 weeks
期刊介绍: Series A (Statistics in Society) publishes high quality papers that demonstrate how statistical thinking, design and analyses play a vital role in all walks of life and benefit society in general. There is no restriction on subject-matter: any interesting, topical and revelatory applications of statistics are welcome. For example, important applications of statistical and related data science methodology in medicine, business and commerce, industry, economics and finance, education and teaching, physical and biomedical sciences, the environment, the law, government and politics, demography, psychology, sociology and sport all fall within the journal''s remit. The journal is therefore aimed at a wide statistical audience and at professional statisticians in particular. Its emphasis is on well-written and clearly reasoned quantitative approaches to problems in the real world rather than the exposition of technical detail. Thus, although the methodological basis of papers must be sound and adequately explained, methodology per se should not be the main focus of a Series A paper. Of particular interest are papers on topical or contentious statistical issues, papers which give reviews or exposés of current statistical concerns and papers which demonstrate how appropriate statistical thinking has contributed to our understanding of important substantive questions. Historical, professional and biographical contributions are also welcome, as are discussions of methods of data collection and of ethical issues, provided that all such papers have substantial statistical relevance.
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