International Security & Defense

This article outlines current U.S. government processes to bolster biosecurity and biosafety measures and summarizes five key findings from biosecurity experts.

Joseph Nye argues that prudence results from the fear of creating unintended consequences in unpredictable systems and can develop into a norm of nonuse or limited use of certain weapons or a norm of limiting targets. Something like this happened with nuclear weapons when the superpowers came close to the brink of nuclear war in 1962, during the Cuban missile crisis. The Limited Test Ban Treaty followed a year later.

U.S. President Joe Biden has spoken frequently of restoring the United States’ credibility as a global leader. That task, which comes at a moment of global crisis, will require the United States to recommit to multilateral diplomacy, even while managing a dangerously deteriorating relationship with China. By acting on biosecurity—a neglected priority hiding in plain sight—Biden can make progress on all of these goals. Washington has an opportunity to lead in an era of heightened great-power competition, address the need for arms control measures that reduce the risk of biological weapons, and potentially even push China to cooperate to that end.  

The editors of International Security have selected the following articles as excellent starting points to help achieve a greater understanding of the crisis at hand.

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.