most citedToward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

219 citations · 293 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CY202171 cited

Institutionalising Ethics in AI through Broader Impact Requirements

Carina Prunkl, Carolyn Ashurst, Markus Anderljung +3

Turning principles into practice is one of the most pressing challenges of artificial intelligence (AI) governance. In this article, we reflect on a novel governance initiative by…

cs.CY2021

Ethics and Governance of Artificial Intelligence: Evidence from a Survey of Machine Learning Researchers

Baobao Zhang, Markus Anderljung, Lauren Kahn +3

Machine learning (ML) and artificial intelligence (AI) researchers play an important role in the ethics and governance of AI, including taking action against what they perceive to…

cs.CY2021

Skilled and Mobile: Survey Evidence of AI Researchers' Immigration Preferences

Remco Zwetsloot, Baobao Zhang, Noemi Dreksler +4

Countries, companies, and universities are increasingly competing over top-tier artificial intelligence (AI) researchers. Where are these researchers likely to immigrate and what a…

cs.CY2020219 cited

Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims

Miles Brundage, Shahar Avin, Jasmine Wang +56

With the recent wave of progress in artificial intelligence (AI) has come a growing awareness of the large-scale impacts of AI systems, and recognition that existing regulations an…

cs.CY20203 cited

Social and Governance Implications of Improved Data Efficiency

Aaron D. Tucker, Markus Anderljung, Allan Dafoe

Many researchers work on improving the data efficiency of machine learning. What would happen if they succeed? This paper explores the social-economic impact of increased data effi…