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About the database ScholarIQanswers from OpenAlex & ORCID
How has Noemi Dreksler's publication output changed over time?
ScholarIQpublication output · 2019–2025
Output grew100% over the shown period — from 1 works in 2019 to 2 in 2025.
1
3
2
3
2
20192021202220232025
What are the most-cited papers on Noemi Dreksler?
ScholarIQmost cited works
Ethics and Governance of Artificial Intelligence: Evidence from a Survey of Machine Learning Researchers
Zhang Baobao, Markus Anderljung, Lauren Kahn, Noemi Dreksler, Michael C. Horowitz, Allan Dafoe
S139930977. 202177 CitationsOPEN ACCESS
A Critical Analysis of Colour–Shape Correspondences: Examining the Replicability of Colour–Shape Associations
Noemi Dreksler, Charles Spence
S135407459. 201946 CitationsOPEN ACCESS
Open-Sourcing Highly Capable Foundation Models: An Evaluation of Risks, Benefits, and Alternative Methods for Pursuing Open-Source Objectives
Elizabeth Seger, Noemi Dreksler, Richard Moulange, Emily Dardaman, Jonas Schuett, Kevin Wei, Christoph Winter, Mackenzie Arnold, Seán Ó hÉigeartaigh, Anton Korinek, Markus Anderljung, Ben Bucknall, Alan Chan, Eoghan Stafford, Leonie Koessler, Aviv Ovadya, Ben Garfinkel, Emma Bluemke, Michael Aird, Patrick Levermore, Julian Hazell, Abhishek Gupta
SSRN Electronic Journal. 202326 CitationsOPEN ACCESS
Forecasting AI Progress: Evidence from a Survey of Machine Learning Researchers
Baobao Zhang, Noemi Dreksler, Markus Anderljung, Lauren E. Kahn, Charlie Giattino, Allan Dafoe, Michael C. Horowitz
arXiv (Cornell University). 202222 CitationsOPEN ACCESS
Towards best practices in AGI safety and governance: A survey of expert opinion
Jonas Schuett, Noemi Dreksler, Markus Anderljung, David McCaffary, Lennart Heim, Emma Bluemke, Ben Garfinkel
arXiv (Cornell University). 202315 CitationsOPEN ACCESS
Related on ScholarIQ
Institute on Governance
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Ethics and Governance of Artificial Intelligence: Evidence from a Survey of Machine Learning Researchers
Paper
A Critical Analysis of Colour–Shape Correspondences: Examining the Replicability of Colour–Shape Associations
Paper
Open-Sourcing Highly Capable Foundation Models: An Evaluation of Risks, Benefits, and Alternative Methods for Pursuing Open-Source Objectives
Paper
Forecasting AI Progress: Evidence from a Survey of Machine Learning Researchers
Paper
Towards best practices in AGI safety and governance: A survey of expert opinion
Paper