# Towards Responsible Governance of Biological Design Tools

arXiv: https://arxiv.org/abs/2311.15936 · HTML: https://mlsanity.com/paper/2311.15936/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 57 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Red-Teaming the Stable Diffusion Safety Filter](https://mlsanity.com/paper/2210.04610/) (2022)
- [Predictability and Surprise in Large Generative Models](https://mlsanity.com/paper/2202.07785/) (2022)
- [Model evaluation for extreme risks](https://mlsanity.com/paper/2305.15324/) (2023)
- [Thieves on Sesame Street! Model Extraction of BERT-based APIs](https://mlsanity.com/paper/1910.12366/) (2019)
- [Frontier AI Regulation: Managing Emerging Risks to Public Safety](https://mlsanity.com/paper/2307.03718/) (2023)
- [The Gradient of Generative AI Release: Methods and Considerations](https://mlsanity.com/paper/2302.04844/) (2023)
- [Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools](https://mlsanity.com/paper/2306.13952/) (2023)
- [Structured access: an emerging paradigm for safe AI deployment](https://mlsanity.com/paper/2201.05159/) (2022)
- [Can large language models democratize access to dual-use biotechnology?](https://mlsanity.com/paper/2306.03809/) (2023)
- [Open-Sourcing Highly Capable Foundation Models: An evaluation of risks, benefits, and alternative methods for pursuing open-source objectives](https://mlsanity.com/paper/2311.09227/) (2023)
- [Towards best practices in AGI safety and governance: A survey of expert opinion](https://mlsanity.com/paper/2305.07153/) (2023)
- [Will releasing the weights of future large language models grant widespread access to pandemic agents?](https://mlsanity.com/paper/2310.18233/) (2023)
- [What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring](https://mlsanity.com/paper/2303.11341/) (2023)
- [Censoring chemical data to mitigate dual use risk](https://mlsanity.com/paper/2304.10510/) (2023)

## Built on by (most cited first, 5 of 5)

- [Are large language models superhuman chemists?](https://mlsanity.com/paper/2404.01475/) (2024)
- [The Reality of AI and Biorisk](https://mlsanity.com/paper/2412.01946/) (2024)
- [From Turing to Tomorrow: The UK's Approach to AI Regulation](https://mlsanity.com/paper/2507.03050/) (2025)
- [Agentic BAIM-LLM Evaluation (ABLE): Benchmarking LLM Use of Protein Design Tools](https://mlsanity.com/paper/2609.05818/) (2026)
- [Know Your Scientist: KYC as Biosecurity Infrastructure](https://mlsanity.com/paper/2602.06172/) (2026)

## Abstract (arXiv)

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.