Towards Responsible Governance of Biological Design Tools
This work tackles the regulatory challenge of preventing misuse of BDTs for public safety without hindering innovation, which is an incremental step in governance for domain-specific AI risks.
The paper addresses the dual-use risks of biological design tools (BDTs) enabled by generative machine learning, such as faster development of biological agents and evasion of screening techniques, and proposes measures like responsible development and access management to mitigate misuse while balancing innovation and safety.
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.