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Comparative, collaborative, and integrative risk governance for emerging technologies.

Igor Linkov1, Benjamin D Trump1, Elke Anklam2

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Emerging technologies require a new risk governance approach. Integrating expert insights with data helps manage risks of novel technologies like AI and synthetic biology.

Keywords:
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Area of Science:

  • Science and Technology Governance
  • Emerging Technology Risk Assessment
  • Societal Implications of Innovation

Background:

  • Existing risk-based paradigms are insufficient for high-uncertainty emerging technologies (e.g., nanotechnology, synthetic biology, artificial intelligence).
  • Novel technologies present unique challenges in identification, assessment, and management of potential risks.
  • A complementary approach is needed to address the early-stage assessment and management of these complex innovations.

Purpose of the Study:

  • To propose a comprehensive risk governance framework for emerging technologies.
  • To advocate for integrating quantitative data with qualitative expert judgment in risk assessment.
  • To guide policymakers in evaluating the societal and health impacts of nascent technologies.

Main Methods:

  • Developing a multi-stakeholder risk governance process.
  • Incorporating diverse information sources, including experimental data and expert insights.
  • Establishing comparative assessments against conventional technologies and products.
  • Reviewing various endpoints and outcomes to characterize risks and benefits.

Main Results:

  • The proposed approach facilitates the characterization and balancing of risks, benefits, costs, and societal implications.
  • It provides a structured method for evaluating emerging technologies when quantitative risk data is limited.
  • Enables informed decision-making for policymakers regarding human and environmental health impacts.

Conclusions:

  • A hybrid risk governance model, combining quantitative and qualitative methods, is essential for emerging technologies.
  • Multi-stakeholder involvement and diverse data integration are crucial for effective risk management.
  • This framework supports responsible innovation and policy development for technologies with high uncertainty.