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.
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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.
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.
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.
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.
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 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.
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.
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.
This was one of several research projects featured in RAND Europe's annual review.
