Machine Learning and Gene Editing at the Helm of a Societal Evolution

DNA Strand with one end lit up, image by Rasi/Getty Images

Image by Rasi/Adobe Stock

The integration of gene editing and machine learning is in an early stage of maturity: Lack of balanced oversight could either stifle innovation or create inequities.

What is the issue?

The integration of artificial intelligence (AI) and biotechnology, while in its infancy, presents significant opportunities and risks, and proactive policy is needed to manage these emerging technologies. While 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) can foster substantial benefits as well as daunting risks that range from ethics to national security.

Both genome editing and AI technologies are being pursued at scale in various global markets. There is clearly an urgency to addressing policy issues surrounding these emerging technologies. Yet, this combined field has not been adequately studied from a policy perspective. The components need clear definitions and analyses with respect to their practical combined implications.

How did we help?

We investigated the policy implications of the application of AI/ML to gene editing in humans, in particular technology governance as a cross-cutting theme. This analysis developed a future scenario-focussed framework to protect human interest by considering the implications of these technologies being pursued at scale and globally.

Landscape assessment

We used software-assisted horizon scanning for assessing the state-of-the-art AI and genomic tools, creating a typology for their use, and assessing where they have been or integrated. This was supplemented by desk-based review of current governance arrangements of AI and genome editing and engagement with key practitioners and policy stakeholders.

Futures assessment

Our landscape assessment outlined how the relationship between AI and genomic editing is currently configured, and used a futures framework to explore how this relationship might change over time. We used a scenarios-based futures exercise, where the landscape assessment facilitated crafting of illustrative stories that outline how AI and genomic tools might remain the same, improve, or change in some other way based on the effects of the factors identified in the landscape assessment.

What did we find?

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 bears significant practical implications, however, much of the underlying technology still requires development.

Technology is advancing faster than policies and oversight mechanisms

Technology is advancing faster than its associated policies, particular at the intersection of ML and GE where there has been little to no policy development. The domino effect of national AI plans across the international stage highlights the reactionary nature of recent AI policy actions, shaped by geopolitics rather than technological progress.

In contrast, GE adopts a precautionary approach, involving constant iteration of technology and policy development. Furthermore, while key GE policy milestones are spread out over time and focussed on regulation, AI/ML landmark policies are concentrated in clusters with past policies focussed on innovation and current topical activity focussed on regulation.

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

Public engagement and perception of ML and GE are also 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 also be considered.

What can be done?

  1. Policymakers should analyse the trajectory of both policy and technology development concurrently in multiple countries, to foster better understanding and planning of international cooperation and/or competition.
  2. National policymakers should create frameworks and opportunities to support more public education and deliberative dialogue.
  3. Governments should develop centralised workforce development plans that target the interface of ML and GE and all levels of education.
  4. Governments and national policymakers should adopt both upstream (prior to the application of the technology) and downstream (pertaining to applications) regulation.
  5. Policymakers should focus on regulating the accessibility and distribution of underlying data.
  6. Governments should establish a knowledge bank about biosecurity measures, technology standards and frameworks.
  7. To accommodate the fast pace of technology advances and the uncertainty with international relationships, policy must be anticipatory, participatory, and nimble and follow a policy lifecycle, oscillating between policy approaches, to mirror technology maturity levels.
  8. State governments and scientific communities should incentivise international collaboration and coordination by publicising potential national/international stakeholders; leveraging existing international brokers, which have a history of independently setting policy where there previously has been none; and encouraging the technical community to communicate more frequently to non-technical audiences.
  9. Governments and international brokers should develop and use international standards to foster international agreements.