Towards Responsible Governing AI Proliferation
Edward Kembery
TL;DR
The paper argues that the traditional Big Compute paradigm may not capture future AI risks as development diversifies. It introduces the Proliferation paradigm and the SHADOW framework to map five pathways (Small, Hidden, Augmented, Decentralized, Open-Weight) that erode compute-centered governance. It then proposes governance strategies targeting algorithms, decentralized compute, and information inputs, emphasizing ethical trade-offs, empirical baselines, and stronger security. The work highlights the practical need for global coordination to mitigate irreversible harms while preserving potential benefits of proliferation.
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'.