Why It Matters
The gap between technological capability and governance readiness has never been wider. CRISPR gene editing can modify human embryos—but regulatory frameworks for heritable genetic modification remain incomplete in most countries. Large language models generate human-quality text at scale—but liability frameworks for AI-generated misinformation are still nascent. Synthetic biology enables the design of novel organisms—but biosafety governance was built for naturally occurring pathogens.
Responsible innovation (RI) and anticipatory governance (AG) represent the policy response to this acceleration. Rather than waiting for technologies to cause harm and then regulating reactively, these frameworks embed ethical reflection, stakeholder deliberation, and adaptive oversight into the innovation process itself. The goal is not to slow innovation but to steer it—amplifying benefits while identifying and mitigating risks before they materialize at scale.
The OECD's 2024 Framework for Anticipatory Governance of Emerging Technologies represents a landmark: it provides the first comprehensive, internationally endorsed blueprint for how governments can govern technologies that do not yet exist in their final form. This is governance under radical uncertainty, and the frameworks being developed reflect a sophisticated understanding of that challenge.
The Science
The OECD Anticipatory Governance Framework
The OECD (2024), with 21 citations, publishes a comprehensive framework for anticipatory governance of emerging technologies—the most authoritative policy document in this space to date. The framework addresses a fundamental paradox: emerging technologies are easiest to shape when they are least understood (early stage), but governance capacity is lowest precisely at that moment.
The framework proposes five pillars: (1) horizon scanning and foresight—systematic identification of emerging technologies and their potential impacts; (2) inclusive deliberation—engaging diverse stakeholders (not just industry and scientists) in defining acceptable development pathways; (3) adaptive regulation—regulatory approaches that can evolve as technologies mature and understanding improves; (4) international coordination—aligning governance approaches across borders to prevent regulatory arbitrage; and (5) monitoring and evaluation—continuous assessment of whether governance mechanisms are achieving their intended effects.
Critically, the framework acknowledges that anticipatory governance is not prediction—no one can predict exactly how emerging technologies will develop. Instead, it focuses on building institutional capacity to respond effectively to a range of plausible futures.
Anticipatory Governance for Food Systems
Mason-D'Croz et al. (2025), with 2 citations, apply anticipatory governance specifically to food system transformation—an area where technology interventions (precision agriculture, cellular meat, gene-edited crops) intersect with deeply held cultural values, livelihood dependencies, and power asymmetries.
Their argument is that food system interventions require more rigorous anticipatory governance than currently practiced. Most food system transformations are designed with optimistic assumptions about adoption, equity effects, and environmental outcomes—but historical precedent (the Green Revolution, biofuel mandates) shows that well-intentioned interventions often produce unexpected negative consequences for marginalized populations.
The study calls for mandatory foresight assessments before large-scale food system interventions, analogous to environmental impact assessments but focused on distributional consequences: who benefits, who bears costs, and what happens if assumptions prove wrong?
AI for Foresight and Policy Design
Panizzon et al. (2025) develop a prototype generative AI system designed to support anticipatory governance by integrating foresight and policy design. The system uses large language models to analyze emerging trends, generate scenario narratives, and propose policy options—essentially using AI as a foresight amplifier for policymakers.
The assessment method evaluates the AI system across multiple dimensions: accuracy of trend identification, plausibility of generated scenarios, actionability of policy recommendations, and stakeholder trust in AI-assisted foresight. Results are promising but bounded: AI excels at synthesizing large volumes of information and generating diverse scenarios, but its policy recommendations require substantial human curation to be politically and culturally appropriate.
This creates an interesting recursion: AI is both the object of anticipatory governance (we need frameworks to govern AI) and a tool for anticipatory governance (AI can enhance foresight capacity). Navigating this dual role requires careful attention to which governance questions AI can usefully inform and which require distinctly human judgment.
Responsible Innovation in Prenatal Therapies
Meslin et al. (2025), with 1 citation, develop a "Points to Consider" framework for responsible innovation in prenatal therapies—medical interventions at the fetal stage that address severe congenital conditions. This domain exemplifies the challenges RI frameworks must navigate: life-saving potential, profound ethical questions about fetal personhood and consent, high uncertainty about long-term outcomes, and strong public opinion.
Their framework operationalizes RI through structured questions that researchers and clinicians must address at each stage of development: Who defines "severe" congenital conditions? How is informed consent obtained when the patient cannot consent? What long-term follow-up obligations exist? How are equity concerns addressed when prenatal therapies are expensive?
Anticipatory Governance Design Principles
<| Principle | Description | Implementation Challenge |
|---|---|---|
| Foresight | Systematic scanning for emerging technologies and impacts | Distinguishing signal from noise |
| Inclusion | Diverse stakeholder engagement in governance design | Power asymmetries in deliberation |
| Adaptiveness | Regulations that evolve with technology maturity | Institutional inertia, legal certainty demands |
| Coordination | International alignment to prevent regulatory arbitrage | Sovereignty concerns, geopolitical competition |
| Reflexivity | Governance systems that question their own assumptions | Organizational resistance to self-criticism |
What To Watch
The OECD framework will increasingly be operationalized through national implementations—watch for how different countries adapt the five pillars to their institutional contexts. The EU AI Act is the most advanced example, but comparable frameworks are emerging in Singapore, Canada, and Brazil. The intersection of AI and anticipatory governance (using AI to govern AI, and governing AI that is used for governance) will generate both productive tools and conceptual paradoxes that require careful navigation. Expect responsible innovation frameworks to become mandatory for publicly funded research in high-risk domains—synthetic biology, neurological enhancement, and autonomous weapons—within the next five years.
Explore related work through ORAA ResearchBrain.