Towards Responsible Governing AI Proliferation

TL;DR

This paper discusses the shift from large-scale AI to decentralized, open-source models, highlighting new risks and proposing governance strategies to manage these emerging challenges.

Contribution

It introduces the 'Proliferation' paradigm, emphasizing the need for new governance approaches for decentralized AI models beyond traditional 'Big Compute' assumptions.

Findings

Decentralized AI models are easier to augment and train.

Existing governance mechanisms may be insufficient for new AI paradigms.

Strategies like access control and oversight have limitations.

Abstract

This paper argues that existing governance mechanisms for mitigating risks from AI systems are based on the `Big Compute' paradigm -- a set of assumptions about the relationship between AI capabilities and infrastructure -- that may not hold in the future. To address this, the paper introduces the `Proliferation' paradigm, which anticipates the rise of smaller, decentralized, open-sourced AI models which are easier to augment, and easier to train without being detected. It posits that these developments are both probable and likely to introduce both benefits and novel risks that are difficult to mitigate through existing governance mechanisms. The final section explores governance strategies to address these risks, focusing on access governance, decentralized compute oversight, and information security. Whilst these strategies offer potential solutions, the paper acknowledges their limitations and cautions developers to weigh benefits against developments that could lead to a `vulnerable world'.