Assessing the Risks Posed by the Convergence of Artificial Intelligence and Biotechnology
Author(s) -
John T. O’Brien,
Cassidy Nelson
Publication year - 2020
Publication title -
health security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.705
H-Index - 37
eISSN - 2326-5108
pISSN - 2326-5094
DOI - 10.1089/hs.2019.0122
Subject(s) - technological convergence , biosecurity , emerging technologies , risk analysis (engineering) , microbiology and biotechnology , merge (version control) , transformative learning , human health , risk assessment , computer science , engineering , management science , artificial intelligence , business , medicine , biology , computer security , environmental health , ecology , psychology , pedagogy , information retrieval , operating system
Rapid developments are currently taking place in the fields of artificial intelligence (AI) and biotechnology, and applications arising from the convergence of these 2 fields are likely to offer immense opportunities that could greatly benefit human health and biosecurity. The combination of AI and biotechnology could potentially lead to breakthroughs in precision medicine, improved biosurveillance, and discovery of novel medical countermeasures as well as facilitate a more effective public health emergency response. However, as is the case with many preceding transformative technologies, new opportunities often present new risks in parallel. Understanding the current and emerging risks at the intersection of AI and biotechnology is crucial for health security specialists and unlikely to be achieved by examining either field in isolation. Uncertainties multiply as technologies merge, showcasing the need to identify robust assessment frameworks that could adequately analyze the risk landscape emerging at the convergence of these 2 domains.This paper explores the criteria needed to assess risks associated with Artificial intelligence and biotechnology and evaluates 3 previously published risk assessment frameworks. After highlighting their strengths and limitations and applying to relevant Artificial intelligence and biotechnology examples, the authors suggest a hybrid framework with recommendations for future approaches to risk assessment for convergent technologies.
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