219 citations · 641 across the 20 of their papers we have counts for
20 papers
Third-party compliance reviews for frontier AI safety frameworks
Aidan Homewood, Sophie Williams, Noemi Dreksler +11
Safety frameworks have emerged as a best practice for managing risks from frontier artificial intelligence (AI) systems. However, it may be difficult for stakeholders to know if co…
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…
Safety case template for frontier AI: A cyber inability argument
Arthur Goemans, Marie Davidsen Buhl, Jonas Schuett +4
Frontier artificial intelligence (AI) systems pose increasing risks to society, making it essential for developers to provide assurances about their safety. One approach to offerin…
Safety cases for frontier AI
Marie Davidsen Buhl, Gaurav Sett, Leonie Koessler +2
As frontier artificial intelligence (AI) systems become more capable, it becomes more important that developers can explain why their systems are sufficiently safe. One way to do s…
A Grading Rubric for AI Safety Frameworks
Jide Alaga, Jonas Schuett, Markus Anderljung
Over the past year, artificial intelligence (AI) companies have been increasingly adopting AI safety frameworks. These frameworks outline how companies intend to keep the potential…
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…
Risk thresholds for frontier AI
Leonie Koessler, Jonas Schuett, Markus Anderljung
Frontier artificial intelligence (AI) systems could pose increasing risks to public safety and security. But what level of risk is acceptable? One increasingly popular approach is…
Towards Publicly Accountable Frontier LLMs: Building an External Scrutiny Ecosystem under the ASPIRE Framework
Markus Anderljung, Everett Thornton Smith, Joe O'Brien +7
With the increasing integration of frontier large language models (LLMs) into society and the economy, decisions related to their training, deployment, and use have far-reaching im…
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 +19
Recent decisions by leading AI labs to either open-source their models or to restrict access to their models has sparked debate about whether, and how, increasingly capable AI mode…
Coordinated pausing: An evaluation-based coordination scheme for frontier AI developers
Jide Alaga, Jonas Schuett
As artificial intelligence (AI) models are scaled up, new capabilities can emerge unintentionally and unpredictably, some of which might be dangerous. In response, dangerous capabi…
Risk assessment at AGI companies: A review of popular risk assessment techniques from other safety-critical industries
Leonie Koessler, Jonas Schuett
Companies like OpenAI, Google DeepMind, and Anthropic have the stated goal of building artificial general intelligence (AGI) - AI systems that perform as well as or better than hum…
Frontier AI Regulation: Managing Emerging Risks to Public Safety
Markus Anderljung, Joslyn Barnhart, Anton Korinek +21
Advanced AI models hold the promise of tremendous benefits for humanity, but society needs to proactively manage the accompanying risks. In this paper, we focus on what we term "fr…
Towards best practices in AGI safety and governance: A survey of expert opinion
Jonas Schuett, Noemi Dreksler, Markus Anderljung +4
A number of leading AI companies, including OpenAI, Google DeepMind, and Anthropic, have the stated goal of building artificial general intelligence (AGI) - AI systems that achieve…
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…
How to design an AI ethics board
Jonas Schuett, Anka Reuel, Alexis Carlier
Organizations that develop and deploy artificial intelligence (AI) systems need to take measures to reduce the associated risks. In this paper, we examine how AI companies could de…
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…
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…
Risk management in the Artificial Intelligence Act
Jonas Schuett
The proposed EU AI Act is the first comprehensive attempt to regulate AI in a major jurisdiction. This article analyses Article 9, the key risk management provision in the AI Act.…
AI Certification: Advancing Ethical Practice by Reducing Information Asymmetries
Peter Cihon, Moritz J. Kleinaltenkamp, Jonas Schuett +1
As artificial intelligence (AI) systems are increasingly deployed, principles for ethical AI are also proliferating. Certification offers a method to both incentivize adoption of t…
Defining the scope of AI regulations
Jonas Schuett
The paper argues that the material scope of AI regulations should not rely on the term "artificial intelligence (AI)". The argument is developed by proposing a number of requiremen…