TY - JOUR
T1 - Comparative, collaborative, and integrative risk governance for emerging technologies
AU - Linkov, Igor
AU - Trump, Benjamin D.
AU - Anklam, Elke
AU - Berube, David
AU - Boisseasu, Patrick
AU - Cummings, Christopher
AU - Ferson, Scott
AU - Florin, Marie Valentine
AU - Goldstein, Bernard
AU - Hristozov, Danail
AU - Jensen, Keld Alstrup
AU - Katalagarianakis, Georgios
AU - Kuzma, Jennifer
AU - Lambert, James H.
AU - Malloy, Timothy
AU - Malsch, Ineke
AU - Marcomini, Antonio
AU - Merad, Myriam
AU - Palma-Oliveira, José
AU - Perkins, Edward
AU - Renn, Ortwin
AU - Seager, Thomas
AU - Stone, Vicki
AU - Vallero, Daniel
AU - Vermeire, Theo
PY - 2018/6
Y1 - 2018/6
N2 - Various emerging technologies challenge existing governance processes to identify, assess, and manage risk. Though the existing risk-based paradigm has been essential for assessment of many chemical, biological, radiological, and nuclear technologies, a complementary approach may be warranted for the early-stage assessment and management challenges of high uncertainty technologies ranging from nanotechnology to synthetic biology to artificial intelligence, among many others. This paper argues for a risk governance approach that integrates quantitative experimental information alongside qualitative expert insight to characterize and balance the risks, benefits, costs, and societal implications of emerging technologies. Various articles in scholarly literature have highlighted differing points of how to address technological uncertainty, and this article builds upon such knowledge to explain how an emerging technology risk governance process should be driven by a multi-stakeholder effort, incorporate various disparate sources of information, review various endpoints and outcomes, and comparatively assess emerging technology performance against existing conventional products in a given application area. At least in the early stages of development when quantitative data for risk assessment remain incomplete or limited, such an approach can be valuable for policymakers and decision makers to evaluate the impact that such technologies may have upon human and environmental health.
AB - Various emerging technologies challenge existing governance processes to identify, assess, and manage risk. Though the existing risk-based paradigm has been essential for assessment of many chemical, biological, radiological, and nuclear technologies, a complementary approach may be warranted for the early-stage assessment and management challenges of high uncertainty technologies ranging from nanotechnology to synthetic biology to artificial intelligence, among many others. This paper argues for a risk governance approach that integrates quantitative experimental information alongside qualitative expert insight to characterize and balance the risks, benefits, costs, and societal implications of emerging technologies. Various articles in scholarly literature have highlighted differing points of how to address technological uncertainty, and this article builds upon such knowledge to explain how an emerging technology risk governance process should be driven by a multi-stakeholder effort, incorporate various disparate sources of information, review various endpoints and outcomes, and comparatively assess emerging technology performance against existing conventional products in a given application area. At least in the early stages of development when quantitative data for risk assessment remain incomplete or limited, such an approach can be valuable for policymakers and decision makers to evaluate the impact that such technologies may have upon human and environmental health.
KW - Biotechnology
KW - Decision analysis
KW - Governance
KW - Nanotechnology
KW - Policy
KW - Regulations
KW - Risk assessment
KW - Synthetic biology
UR - http://www.scopus.com/inward/record.url?scp=85046489480&partnerID=8YFLogxK
U2 - 10.1007/s10669-018-9686-5
DO - 10.1007/s10669-018-9686-5
M3 - Article
AN - SCOPUS:85046489480
SN - 2194-5403
VL - 38
SP - 170
EP - 176
JO - Environment Systems and Decisions
JF - Environment Systems and Decisions
IS - 2
ER -