output
20212025
most citedCompute Trends Across Three Eras of Machine Learning

312 citations

9 papers

cs.CY2025★ 3 cited

On Regulating Downstream AI Developers

Sophie Williams, Jonas Schuett, Markus Anderljung

Foundation models - models trained on broad data that can be adapted to a wide range of downstream tasks - can pose significant risks, ranging from intimate image abuse, cyberattac…

cs.CY2024★ 2 cited

From Principles to Rules: A Regulatory Approach for Frontier AI

Jonas Schuett, Markus Anderljung, Alexis Carlier +2

Several jurisdictions are starting to regulate frontier artificial intelligence (AI) systems, i.e. general-purpose AI systems that match or exceed the capabilities present in the m…

cs.CY2024★ 72 cited

Black-Box Access is Insufficient for Rigorous AI Audits

Stephen Casper, Carson Ezell, Charlotte Siegmann +18

External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depends on the degree of access granted to a…

cs.CY2023★ 18 cited

Frontier AI developers need an internal audit function

Jonas Schuett

This article argues that frontier artificial intelligence (AI) developers need an internal audit function. First, it describes the role of internal audit in corporate governance: i…

cs.CL2023★ 219 cited

Auditing large language models: a three-layered approach

Jakob Mökander, Jonas Schuett, Hannah Rose Kirk +1

Large language models (LLMs) represent a major advance in artificial intelligence (AI) research. However, the widespread use of LLMs is also coupled with significant ethical and so…

cs.CY2022★ 36 cited

Three lines of defense against risks from AI

Jonas Schuett

Organizations that develop and deploy artificial intelligence (AI) systems need to manage the associated risks - for economic, legal, and ethical reasons. However, it is not always…