Towards Responsible Governance of Biological Design Tools

TL;DR

This paper discusses the dual-use risks of biological design tools enabled by machine learning, emphasizing the need for tailored regulation and responsible development to prevent misuse while fostering innovation.

Contribution

It highlights the limitations of current AI regulation approaches for biological design tools and proposes comprehensive measures for responsible governance and risk mitigation.

Findings

BDTs enable rapid biological design with dual-use risks.

Current regulations may be ineffective for open-source BDTs.

Proposed measures include responsible development, transparency, and cybersecurity.

Abstract

Recent advancements in generative machine learning have enabled rapid progress in biological design tools (BDTs) such as protein structure and sequence prediction models. The unprecedented predictive accuracy and novel design capabilities of BDTs present new and significant dual-use risks. For example, their predictive accuracy allows biological agents, whether vaccines or pathogens, to be developed more quickly, while the design capabilities could be used to discover drugs or evade DNA screening techniques. Similar to other dual-use AI systems, BDTs present a wicked problem: how can regulators uphold public safety without stifling innovation? We highlight how current regulatory proposals that are primarily tailored toward large language models may be less effective for BDTs, which require fewer computational resources to train and are often developed in an open-source manner. We propose a range of measures to mitigate the risk that BDTs are misused, across the areas of responsible development, risk assessment, transparency, access management, cybersecurity, and investing in resilience. Implementing such measures will require close coordination between developers and governments.