We’re Failing to Evaluate How Biologists Could Misuse AI
Argues that AI biorisk evaluations focus on novices while expert biologists with AI assistance could pose greater misuse risks, and that this gap needs addressing.
Research / Sources
Expert essays on AI safety, policy and governance. ai-frontiers.org
Argues that AI biorisk evaluations focus on novices while expert biologists with AI assistance could pose greater misuse risks, and that this gap needs addressing.
Proposes a $100 million NSF AI Resilience Pilot through America's Seed Fund to fund startups building defenses against AI-enabled cyber, bio and autonomous-agent threats.
Argues that AI researchers lack a safe way to report jailbreaks of frontier models and proposes adopting cybersecurity-style responsible disclosure norms, citing universal jailbreaks and malicious misuse.
Argues that a large AI capabilities lead could make nuclear counterforce strikes feasible, eroding mutual assured destruction and destabilising great-power nuclear deterrence.
An opinion piece arguing that fear of AI launching nuclear weapons, despite funded research and media attention, is overstated relative to human-driven nuclear risks and should not dominate AI risk policy.
Argues that consumer humanoid robots such as Figure 03 and Unitree R1 are entering homes faster than rules govern them, and that regulators should set safety, privacy and liability standards now.
Responds to critiques of Mutually Assured AI Malfunction, a deterrence framework for superintelligence races, arguing that ASI development fuels state tensions and assessing stability and redline issues.
An opinion piece arguing that frontier AI models kept private inside labs pose hidden dual-use risks, and calling for transparency and third-party oversight of these systems while protecting them from theft.
An essay arguing that releasing AGI, whether deliberately through open-sourcing or through leaks and theft, would be catastrophic and could displace humanity, and that AGI should not be built until it can be kept under control.
Examines whether a Manhattan Project-style AI program could secure model weights against foreign theft, drawing on RAND's proposed five security levels and the feasibility of the required infrastructure.
Argues that autonomous AI agents that act in digital environments are being deployed faster than governance can keep up, and examines the risks they pose, such as cyberattacks and fraud, and the challenges of governing them.