AI tools as science policy advisers? The potential and the pitfalls

被引：11

作者：

Tyler, Chris ^{[1
]}

Akerlof, K. L. ^{[2
]}

Allegra, Alessandro ^{[3
,4
]}

Arnold, Zachary ^{[5
]}

Canino, Henriette ^{[1
]}

Doornenbal, Marius A. ^{[6
]}

Goldstein, Josh A. ^{[7
]}

Pedersen, David Budtz ^{[8
]}

Sutherland, William J. ^{[9
]}

机构：

[1] UCL, Dept Sci Technol Engn & Publ Policy STEaPP, London, England

[2] George Mason Univ, Dept Environm Sci & Policy, Fairfax, VA USA

[3] UCL, Dept Sci & Technol Studies, London, England

[4] European Commiss, Gen Res & Innovat, Brussels, Belgium

[5] Georgetown Univ, Ctr Secur & Emerging Technol, Emerging Technol Observ, Washington, DC USA

[6] Elsevier, Amsterdam, Netherlands

[7] Georgetown Univ, Ctr Secur & Emerging Technol, Washington, DC USA

[8] Aalborg Univ, Dept Commun & Psychol, Sci Commun, Copenhagen, Denmark

[9] Univ Cambridge, Dept Zool, Conservat Sci Grp, Cambridge, England

来源：

NATURE | 2023年 / 622卷 / 7981期

关键词：

Machine learning; Policy; Computer science; Government;

D O I：

10.1038/d41586-023-02999-3

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Large language models and other artificial-intelligence systems could be excellent at synthesizing scientific evidence for policymakers - but only with appropriate safeguards and humans in the loop. Large language models and other artificial-intelligence systems could be excellent at synthesizing scientific evidence for policymakers - but only with appropriate safeguards and humans in the loop.

引用

页码：27 / 30

页数：4

共 15 条

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