Oct 23, 2023
Machine learning and gene editing present both benefits and risks that range from ethics to national security. These complex technologies have implications for many different sectors, and are further complicated by differences in advancements and policies in multiple geographic regions. This study focuses on how forward-looking policies can be developed in the US, UK, China and the EU to help manage beneficial use of technology advancements.
The integration of artificial intelligence (AI) and biotechnology, whilst in its infancy, presents significant opportunities and risks, and proactive policy is needed to manage these emerging technologies. Whilst AI continues to have significant and broad impact, its relevance and complexity magnify when integrated with other emerging technologies. The confluence of Machine Learning (ML), a subset of AI, with gene editing (GE) in particular can foster substantial benefits as well as daunting risks that range from ethics to national security. These complex technologies have implications for multiple sectors, ranging from agriculture and medicine to economic competition and national security. Consideration of technology advancements and policies in different geographic regions, and involvement of multiple organisations further confound this complexity. As the impact of ML and GE expands, forward looking policy is needed to mitigate risks and leverage opportunities. Thus, this study explores the technological and policy implications of the intersection of ML and GE, with a focus on the United States (US), the United Kingdom (UK), China, and the European Union (EU). Analysis of technical and policy developments over time and an assessment of their current state have informed policy recommendations that can help manage beneficial use of technology advancements and their convergence, which can be applied to other sectors. This report is intended for policymakers to prompt reflection on how to best approach the convergence of the two technologies. Technical practitioners may also find it valuable as a resource to consider the type of information and policy stakeholders engage with.
State of the art and history of gene editing
Trends in gene editing policies
State of the art and history of machine learning technology
Trends in machine learning policies
Convergence of technologies
Future of technology and policy: maximising the gains and minimising the risks of technology convergence out to 2045
Conclusion: risks, opportunities and policy considerations
Figures of timelines for GE and AI/ML