Short abstract:

The use of AI in synthetic biology challenges existing governance frameworks. This study systematically evaluates their assumptions and how adaptations can be made for future. We argue for a shift in research paradigms from ‘T+0’ to ‘T+1’, transitioning from reactive to anticipatory governance.

Long abstract:

The integration of AI in synthetic biology introduces significant uncertainty and complexity, posing challenges to existing governance frameworks. This study systematically examines and evaluates the assumptions of current governance frameworks for AI and synthetic biology, assessing their suitability and limitations in the context of their integration, and how adaptations can be made for future readiness. We argue for a shift in research paradigms and thinking from ‘T+0’ to ‘T+1’ when developing governance frameworks, transitioning from reactive to anticipatory governance, and from passive to proactive governance. By employing fact-based technology foresight, we construct hypothetical governance models to accommodate the integration trends of emerging technologies and their implications for societal governance structures. A concrete example in AI and synthetic biology is provided to illustrate this approach. This study underscores the necessity for governance structures to possess foresight, enabling effective responses in future scenarios to ensure governance remains effective and resilient. The aim of the study is to gain a deeper understanding of how the integration and advancement of emerging technologies can redefine governance paradigms, guiding decision-making in future governance practices.