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.

Cover: Machine Learning and gene editing at the helm of a societal evolution
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Machine Learning and gene editing at the helm of a societal evolution

by Sana Zakaria, Timothy Marler, Mark Cabling, Suzanne Genc, Artur Honich, Mann Virdee, Sam Stockwell

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Research Questions

  1. What are the key advancements at the intersection of ML and GE?
  2. What is the connectedness between policy and technology and what we learn from trends over time?
  3. What kind of policy considerations are needed to govern converging technologies bearing in mind international drivers of collaboration and competition?

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. 

Key Findings

Machine learning is accelerating advances in biology

  • ML is accelerating advances in biology, primarily by enabling faster processes with efficiencies as well as providing predictive capabilities. The integration of GE and ML has substantial practical implications, but much of the underlying technology still requires development and it in an early stage of maturity.

Technology is advancing faster than policies and oversight mechanisms at the convergence of technology

  • Technology is advancing faster than associated policies, with little to no policy development at the intersection of ML and GE. There are significant differences in the progress of technology and policy development for AI and GE. The domino effect of national AI plans across the international stage highlights the reactionary nature of recent policy actions regarding AI, which are underpinned by geopolitics rather than technological progress. Alternatively, GE involves constant iteration of technology and policy development adopting the precautionary approach. Furthermore, while key GE milestones in policy spread out over time and focussed on regulation, AI/ML landmark policies are concentrated in a few clusters with past policies focussed on innovation and current topical activity focussed on regulation.

There is a cultural and public perception chasm in ML and GE sectors

  • ML and GE are set to revolutionise multiple sectors, but public engagement and perception are crucial to consider in future policymaking. The culture gap between the ML and GE communities must be bridged to enact policies that address both communities and their concerns. Education and engagement of both the public and policymakers are crucial to policymaking but must be undertaken with a focus on the applications rather than on debating the technical aspects.

Multiple policy levers can be leveraged to support more oversight of converging technologies

  • International brokers can help fill a vacuum of agile and responsive policymakers. Managing access to data could be central to effective policy development but related political and ethical issues must be addressed.

Recommendations

  • Policymakers should analyse the trajectory of both policy and technology development concurrently in multiple countries.
  • Policy must be anticipatory, participatory, and nimble and adopt a policy lifecycle, oscillating between policy approaches, to mirror technology maturity levels.
  • State governments and scientific communities should incentivise international collaboration and coordination.
  • National policymakers should create frameworks and opportunities to support more public education and deliberative dialogue.
  • Governments and national policymakers should adopt both upstream (prior to the application of the technology) and downstream (pertaining to applications) regulation.
  • Policymakers should focus on regulating the accessibility and distribution of underlying data.
  • Governments should establish a knowledge bank about biosecurity measures, technology standards, and frameworks.

Table of Contents

  • Chapter One

    Introduction

  • Chapter Two

    State of the art and history of gene editing

  • Chapter Three

    Trends in gene editing policies

  • Chapter Four

    State of the art and history of machine learning technology

  • Chapter Five

    Trends in machine learning policies

  • Chapter Six

    Convergence of technologies

  • Chapter Seven

    Future of technology and policy: maximising the gains and minimising the risks of technology convergence out to 2045

  • Chapter Eight

    Conclusion: risks, opportunities and policy considerations

  • Annex A

    Landscape methodology

  • Annex B

    Figures of timelines for GE and AI/ML

  • Annex C

    Futures methodology

Research conducted by

This research was conducted by RAND Europe.

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