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Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives
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 models should be shared.
Model evaluation for extreme risks
Current approaches to building general-purpose AI systems tend to produce systems with both beneficial and harmful capabilities.
Democratising AI: Multiple Meanings, Goals, and Methods
Numerous parties are calling for the democratisation of AI, but the phrase is used to refer to a variety of goals, the pursuit of which sometimes conflict.
Exploring the Relevance of Data Privacy-Enhancing Technologies for AI Governance Use Cases
The development of privacy-enhancing technologies has made immense progress in reducing trade-offs between privacy and performance in data exchange and analysis.
Beyond Privacy Trade-offs with Structured Transparency
The copy problem is often amplified by three related problems which we term the bundling, edit, and recursive enforcement problems.
Federated Learning
Cryptography and Security
Computers and Society
The Windfall Clause: Distributing the Benefits of AI for the Common Good
As the transformative potential of AI has become increasingly salient as a matter of public and political interest, there has been growing discussion about the need to ensure that AI broadly benefits humanity.
The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation
This report surveys the landscape of potential security threats from malicious uses of AI, and proposes ways to better forecast, prevent, and mitigate these threats.
