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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.
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.
'Generative CI' through Collective Response Systems
How can many people (who may disagree) come together to answer a question or make a decision?
The Deepfake Detection Dilemma: A Multistakeholder Exploration of Adversarial Dynamics in Synthetic Media
Synthetic media detection technologies label media as either synthetic or non-synthetic and are increasingly used by journalists, web platforms, and the general public to identify misinformation and other forms of problematic content.
The tension between openness and prudence in AI research
This paper explores the tension between openness and prudence in AI research, evident in two core principles of the Montr\'eal Declaration for Responsible AI.
Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learning
The aim of this paper is to facilitate nuanced discussion around research norms and practices to mitigate the harmful impacts of advances in machine learning (ML).
